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Please summarize the input | FULL-DUPLEX COMMUNICATION METHODS AND APPARATUSTechniques are described for a full duplex communication method and apparatus for inter-vehicle communication (V2V). A communication apparatus includes one or more transmit antennas, one or more receive antennas, and a processor. For cases where a single transmit antenna and multiple receive antennas are used, a distance between the transmit and receive antennas is greater than a pre-determined value. Further, the transmit antenna is located on or in a central region of a top surface of the vehicle and the receive antennas are evenly distributed located on the vehicle. The processor configured to generate one or more messages to be transmitted via the transmit antenna, where the one or more messages includes vehicle condition information, operational information about a driver of the vehicle, or information associated with one or more sensors of the vehicle. | The communication apparatus has transmit antenna (104) and receive antenna (106a-106d) that are located on or in first side and second side of the vehicle (102). A processor generates one or more messages to be transmitted via transmit antenna. The messages include vehicle condition information, operational information about the driver of the vehicle or information associated with one or more sensors of the vehicle. An INDEPENDENT CLAIM is included for a wireless communication method. Communication apparatus for full-duplex vehicle-to-vehicle (V2V) or device-to-device (D2D) communication. Assists driver of the vehicle as the vehicle transmits information to or receives information from the vehicles surrounding the driver, and as well as assists vehicle to operate in autonomous driving mode. The drawing is the schematic view of the communication apparatus for full-duplex vehicle-to-vehicle (V2V) or device-to-device (D2D) communication. 102Vehicle104Transmit antenna106a-106dReceive antenna |
Please summarize the input | Method, system and vehicle for controlling over-vehicle on ice and snow road of automatic driving vehicleThe invention claims an automatic driving vehicle ice and snow road overtaking control method, system and vehicle, firstly detecting whether the surrounding vehicle has overtaking intention; detecting the front and back position vehicle on the borrowing lane is a non-automatic driving vehicle or an automatic driving vehicle, and a non-automatic driving vehicle or automatic driving vehicle located on the to-be-executed overtaking vehicle overtaking route position, and the safe distance of the front vehicle position and the rear vehicle position on the borrowing lane, then sending signal to the surrounding vehicle, after executing the first lane change of the overtaking vehicle, judging whether the detecting road is the ice film road, if not, performing the second lane changing to finish the overtaking after the overtaking vehicle speed change driving exceeds the original lane, sending the over-vehicle signal to the surrounding vehicle, the surrounding vehicle recovers the original driving state, the invention controls the automatic driving vehicle and the surrounding automatic driving vehicle, reduces the uncertainty of the non-automatic driving vehicle in the vehicle process, improves the super-vehicle safety performance of the ice and snow road.|1. An automatic driving vehicle control method for snowy and icy road of automobile, wherein it comprises the following steps: firstly comparing the to-be-executed overtaking vehicle and the front vehicle speed, if it is greater than the front vehicle speed, carrying out overtaking, and then detecting whether the surrounding vehicle has overtaking intention; if there is, then to be executed overtaking vehicle deceleration or original state driving; if not, then detecting the front and back position vehicle on the borrowing lane is a non-automatic driving vehicle or an automatic driving vehicle, and a non-automatic driving vehicle or an automatic driving vehicle located on the vehicle overtaking route to be executed on the position, and the safety distance of the front vehicle position and the rear vehicle position on the borrowing lane, It includes the following three cases: The first situation: if the vehicle located at the front vehicle position and the rear vehicle position of the overtaking vehicle borrowing lane is a non-automatic driving vehicle, then the to-be-executed overtaking vehicle sends a first signal for prompting the non-automatic driving vehicle having an overtaking intention, detecting the front vehicle and the rear vehicle speed and does not change or decelerate, executing the first lane changing for the overtaking vehicle; The second situation: if the non-automatic driving vehicle and the automatic driving vehicle are respectively located at the front vehicle position and the rear vehicle position of the overtaking vehicle to be executed, the priority selection sends the second signal to prompt the automatic driving vehicle to change the speed, the overtaking vehicle is to be executed for the first lane change; The third scenario: if the automatic driving vehicle is respectively located at the front vehicle position and the rear vehicle position of the overtaking vehicle to be executed, detecting whether the road surface of the front vehicle and the rear vehicle is the ice film road surface, preferably selecting the second signal to prompt as the automatic driving vehicle changing speed of the non-ice film road surface, performing the first lane changing for the overtaking vehicle, in the three cases, when the first signal or the second signal is selected, the overtaking vehicle sends the third signal to the automatic driving vehicle located at the front of the front vehicle position or the automatic driving vehicle after the rear vehicle position, after the vehicle receives the third signal of the overtaking vehicle to be executed, detecting the current position of the vehicle, if the rear vehicle at the rear vehicle position is slowly decelerated, and sending the variable speed driving warning to the front and rear vehicles, if the vehicle in the front of the front vehicle position is slowly accelerated, and sending the variable speed driving early warning to the surrounding vehicle ; after executing the first lane change of the overtaking vehicle, judging whether the detecting road is ice film road, if so, performing the original state driving of the overtaking vehicle, if not, detecting whether the surrounding vehicle state is changed, if there is no change, performing the second lane changing to finish the overtaking after the overtaking vehicle speed changing running exceeds the original lane front vehicle, sending the over-vehicle signal to the surrounding vehicle, the surrounding vehicle recovers the original driving state.
| 2. The overtaking control method for snowy and icy road of automatic driving automobile according to claim 1, wherein the judging process of the icy road surface is as follows: the camera of the automatic driving car is matched with the sensor to judge; the distance of the surrounding vehicle passes through the laser radar, the matching of the millimeter-wave radar and the camera can realize measurement; ground adhesion coefficient through the road passing state, the camera sensing and sensor measuring cooperation for judging.
| 3. The overtaking control method for snowy and icy road of automatic driving automobile according to claim 1, wherein the safe distance of the vehicle and the surrounding vehicle is calculated by the following formula: wherein t1 and t2 are the time of the brake, S is the braking process driving distance, v driving speed, g gravity acceleration, μ road adhesion coefficient, s0 after braking distance from the front vehicle. when the automatic driving vehicle is to perform the overtaking operation, the condition that the vehicle should satisfy the vehicle in the super-vehicle borrowing lane is as follows: the vehicle driving condition with the super-vehicle borrowing lane: △S1 ?S; v is not less than v1; and the super-vehicle driving condition of the front vehicle borrowing lane: DELTA S2 IS NOT LESS THAN S; v is less than or equal to v2; wherein ΔS1 is the horizontal distance with the borrowing lane rear vehicle; v1 is the speed of borrowing lane back vehicle; wherein delta S2 is the horizontal distance with the borrowing lane front vehicle; v2 is the speed of borrowing lane front vehicle.
| 4. The overtaking control method for snowy and icy road of automatic driving automobile according to claim 1, wherein said borrowing traffic lane can be the adjacent left side or the adjacent right side traffic lane.
| 5. The overtaking control method for snowy and icy road of automatic driving automobile according to claim 1, wherein said first signal provides lane changing information to the surrounding vehicle in the same way as the non-automatic driving vehicle lane changing process.
| 6. The overtaking control method for snowy and icy road of automatic driving automobile according to claim 1, wherein the second signal is the interactive vehicle information between the overtaking vehicle to be executed and the automatic driving vehicle at the upper front vehicle position or the rear vehicle position of the borrowing lane, the position and the speed of the vehicle. steering speed and steering time and so on.
| 7. The method for controlling overtaking of ice and snow road surface of automatic driving automobile according to claim 1, wherein the third signal is the interactive vehicle information between the to-be-executed overtaking vehicle and the automatic driving vehicle located in front of the front vehicle or the automatic driving vehicle after the rear vehicle position, comprising a position, to be executed overtaking vehicle speed, front vehicle position speed or back vehicle position speed and steering time and so on.
| 8. An automatic driving vehicle ice and snow road overtaking control system, comprising a vehicle controller, a V2V communication unit and a combined instrument; the vehicle controller is adapted to collect the position information of the vehicle, vehicle speed information and steering information; the V2V communication unit is adapted to transmit position information, vehicle speed information and steering information; the vehicle controller is adapted to according to the position information of each vehicle, vehicle speed information and steering information to generate super-vehicle intention signal; the combined instrument is suitable for displaying the corresponding overtaking information according to the intention of the overtaking.
| 9. An automatic driving vehicle, comprising the automatic driving vehicle ice and snow road overtaking control system. | The self-driving car overtaking control method involves comparing the speed of a vehicle to be overtaken with the vehicle in front, overtaking if it is greater than the speed of the vehicle in front, and detecting whether the surrounding vehicles have overtaking intentions. The overtaking vehicle is to be decelerated or driven in its original state. Determination is made to detect whether the front and rear position vehicles on the borrowed lane are non-autonomous driving vehicles or automatic driving vehicles. The first signal is sent to remind the non-autonomous vehicle that the vehicle has an overtaking intention if the vehicles in the front and rear positions of the borrowed lane of the vehicle to be overtaken are non-autonomous vehicles. The second signal is preferred to prompt the automatic driving vehicle to change speed if the non-autonomous and automatic driving vehicles are respectively located in the front and rear positions of the borrowed lane of the overtaking vehicle. INDEPENDENT CLAIMS are included for:(1) an overtaking control system for an automatic driving vehicle on icy and snowy roads;(2) an automatic driving vehicle. Self-driving car overtaking control method on icy and snowy roads. The method reduces the uncertainty of the non-automatic driving vehicle in the process of driving through the cooperative control of the automatic driving vehicle and the surrounding automatic driving vehicles, and improves the safety performance of overtaking on ice and snow roads. The drawing shows a flow chart of a self-driving car overtaking control method on icy and snowy roads. (Drawing includes non-English language text). |
Please summarize the input | A non-human bus wire control chassis and automatic driving system thereofThe invention claims a non-human bus wire control chassis and automatic driving system thereof, a unmanned bus wire control chassis, comprising a chassis main body. In the application, the image and the 3 D laser front fusion sensing, for pedestrian and lane line environment detection to ensure the correct understanding and corresponding decision of the vehicle body surrounding environment of the automatic driving vehicle. identifying the road red street lamp by V2X intelligent network connection technology based on the 5 G communication, the technology by installing the signal emitter on the traffic light to continuously transmit the state information of the traffic light, automatically driving the vehicle by receiving the signal sent by the signal emitter to judge the state of the traffic light, using the MPC track tracking can make the vehicle travel along the predetermined track, the algorithm has excellent performance, the track of the track exhibits stable and accurate tracking capability, at the same time, it has enough real-time performance.|1. A non-human bus wire control chassis, comprising a chassis main body, wherein the chassis main body front side is top part mounted with 32-line laser radar, providing a horizontal 360 degrees, vertical 40 degrees of view, four corners of the chassis main body are fixedly mounted with 16-line laser radar, for making up the view blind area caused by 32-line laser radar height, finishing the monitoring area covering 360 degrees, the back side of the chassis main body, the outer side of the two groups of 16-line laser radar is fixedly installed with two groups of blind area auxiliary millimeter wave radar, the front side of the chassis main body is fixedly installed with a preposed millimeter wave radar, three surfaces of the chassis main body outer wall adjacent are distributed with 12 groups of ultrasonic radar, and the ultrasonic radar on the same surface are distributed in equal distance, the front side of the chassis main body is fixedly mounted with an industrial camera 1 and an industrial camera 2, the industrial camera is used for identifying the lane line and the traffic identification, the bottom of the chassis main body is fixedly mounted with an automatic driving calculation platform ADU, comprising an automatic driving operation platform controller, the bottom of the chassis main body is fixedly installed with a 5 G host and a 5 G antenna, and four sides of the chassis main body are respectively fixedly installed with four groups of 5 G cameras, one group of outer side of two groups of the 5 G antennas is provided with a combined navigation host fixedly connected with the chassis main body; 16-line laser radar, 32-line laser radar, preposed millimeter wave radar blind area auxiliary millimeter wave radar and ultrasonic radar can provide millions of data points per second, so as to create a three-dimensional map of surrounding object and environment, combining the auxiliary of combined navigation host and industrial camera, constructing high precision map needed by bus operation.
| 2. The unmanned bus automatic driving system according to claim 1, wherein the unmanned bus control chassis according to claim 1, wherein it comprises a module software interface and hardware interface, further comprising: sensing algorithm module, which is detected by laser radar, camera detection, laser radar tracking, camera tracking and predicting five sub-modules. The camera detection mainly uses the laser radar obtain the high quality barrier information and detects the final result. the tracking result of each sensor will be fused by the filter algorithm, and the prediction module through the fusion tracking result, inputting and outputting the future track of each type of road participant; a locating module, the locating algorithm is mainly composed of a laser radar milemeter, combined navigation calculation and fusion three sub-modules. after outputting the relative positioning information of the fixed frequency, the fusion module combines the positioning information of different frequencies by filtering algorithm, finally outputting the global position of the fixed frequency, providing absolute positioning capability; a global path planning module, responding to the external routing request, giving an optimal route from the current position to the end point of the request; planning control module, the sensor receives the external information, and through the locating and sensing algorithm module, obtaining the state of the vehicle itself and the surrounding vehicle, and receiving the state of the external information and the vehicle, responsible for autonomous vehicle movement planning and trajectory tracking control; a decision planning module, receiving the real-time location, prediction information, planning according to the global path, combining obstacle avoidance, multiple factors, real time planning the vehicle future a collision-free track of a period of time, and sending to the track tracking controller to execute the vehicle; action prediction module, receiving the input of sensing and positioning module, responsible for giving the action of surrounding other participants 5s-7s the specific motion track, for decision planning module, track tracking control module, after the decision planning module gives the track of safety without collision, the track tracking control module is responsible for calculating the proper control command according to the current vehicle state and the planned track, so that the vehicle can move along the planned track.
| 3. The unmanned bus automatic driving system according to claim 2, wherein the output frequency of the positioning module is the fixed frequency and is processed in the ADU of the automatic driving calculation platform.
| 4. The unmanned bus automatic driving system according to claim 2, wherein the hardware interface comprises: a communication data interface: for interactive scheduling command, vehicle positioning, posture; sensor data interface: the combined inertial navigation system IMU and the automatic driving calculation platform ADU, using the USART interface of the IMU to transmit data; multi-line laser radar interface, millions of point cloud data per second, using UDP protocol for data transmission; the ultrasonic radar is a near-distance obstacle detection, the output result is a barrier distance, the data reading is performed by the CAN interface on the ultrasonic radar control box; a control data interface, an automatic driving operation platform ADU and vehicle control chassis interface, using the mode of CAN to transmit.
| 5. The unmanned bus automatic driving system according to claim 2, wherein said module software interface comprises: sensor abstract layer service interface, providing two types of service interface, one is the information service interface of the intelligent sensor, and the other one is other vehicle sensor interface.
| 6. The unmanned bus automatic driving system according to claim 2, wherein the laser radar mileage meter in the positioning module uses the GNSS data to finish the initialization, and the point cloud data generated by the laser radar is matched with the high precision map, and the absolute positioning information of the fixed frequency is output. combined navigation calculating module combined with GNSS data and IMU data, then outputting the relative positioning information of the fixed frequency.
| 7. The non-human bus wire control chassis and automatic driving system thereof according to claim 2, wherein the radar detection and camera detection in the sensing algorithm module can be decoupled and used for tracking. | The chassis has a chassis main body whose front side is fixedly mounted with first and second industrial cameras. A bottom of the chassis main body is fixedly mounted with an Automatic Drive Unit (ADU). A bottom of the chassis main body is fixedly installed with a fifth generation (5G ) host and a 5G antenna. Four sides of the chassis main body are fixedly installed with four groups of 5G cameras. Groups of the 5G antennas is provided with a combined navigation host fixedly connected with the chassis main body. An INDEPENDENT CLAIM is included for an automatic driving system. Non-human bus wire control chassis for an automatic driving system (claimed). The chassis enables Model predictive control (MPC) tracking to make the vehicle travel along the predetermined track, thus obtaining excellent performance, and stable and accurate tracking capability. The drawing shows a schematic view of a non-human bus wire control chassis. (Drawing includes non-English language text). |
Please summarize the input | lane recognition system, method and automatic driving automobileThe invention embodiment claims a lane recognition system, method and automatic driving vehicle, the lane recognition system, comprising: at least one camera, at least one radar, data processing unit and a lane recognition unit; the weight ratio of the road edge feature the lane recognition unit is connected with the data processing unit for determining the road edge feature obtained by processing the image information and obtained by processing the radar information, and according to the weight proportion, the road edge feature obtained by processing the image information; road edge feature obtained by processing the radar information and the lane mark feature to identify the lane position. the technical solution of the invention realizes the camera and radar device data on the lane recognition fusion, so as to better deal with complex road condition and environmental interference, avoid the occurrence of dangerous accident and improve the safety of driving.|1. A lane recognition system, wherein, comprising: at least one camera for obtaining vehicle driving image information of lane in the path, at least one radar, for obtaining vehicle running radar information of the area, and a processing unit for: processing the image information to obtain the lane line feature and the road edge feature, processing the radar information to acquire the road edge feature, determining the weight ratio of road edge feature by the road edge feature processing obtained by the image information and information obtained by processing the radar; according to said weight proportion, road edge feature obtained by processing the image information, by processing road edge feature obtained by the radar information and the lane mark feature to identify the lane position.
| 2. The system according to claim 1, wherein, further comprising: at least one illumination device, used for obtaining the vehicle driving the illumination intensity information of the area, wherein the processing unit is used for determining the weight proportion according to the illumination intensity information.
| 3. The system according to claim 2, wherein it further comprises a vehicle-to-vehicle communication device, which is used for obtaining the vehicle driving road traffic information and auxiliary lane information of the area, wherein: the processing unit is used for according to the road traffic information and the illumination information to determine the road edge feature by processing the weight rate of said image information, obtained by processing the weight ratio of road edge feature obtained by the radar information, the auxiliary lane information of the weight proportion; road edge feature and according to the weight proportion, obtained by processing the image information by processing the road edge feature obtained by the radar information, the lane line feature, the auxiliary lane information to identify lane position.
| 4. A lane recognition method, wherein the method comprises: according to the image information, obtaining the vehicle running lane in the lane route and lane road edge feature of the two side, wherein the image information collected by the camera by the installed on the vehicle, determining the weight ratio of the respectively obtained according to image information and radar information two sides of the lane of the road edge feature, wherein the radar information by mounting the radar acquisition of the vehicle according to the lane line feature. the weight proportion is, the road edge feature obtained by the image information and the road edge feature identifying lane position acquired by the radar information.
| 5. The method according to claim 4, wherein it further comprises the following steps: obtaining the vehicle driving the illumination intensity information of the area according to the illumination intensity information to determine the road edge feature by processing the image information to that obtained by processing the weight ratio of road edge feature obtained by the radar information.
| 6. The method according to claim 5, wherein it further comprises the following steps: obtaining the vehicle running road traffic information and auxiliary lane information of the region; The road traffic information and the illumination information to determine the road edge feature by processing weight ratio of the image information obtained by the weight ratio of the road edge feature obtained by processing the radar information, the auxiliary lane information of weight ratio, and the road edge feature according to the weight proportion, obtained by processing the image information, road edge feature obtained by processing the radar information, the lane line feature, the auxiliary lane information to identify lane position.
| 7. The method according to claim 4, wherein said according to the image information, obtaining the vehicle running lane and a lane in the lane path at two sides of road edge feature, is implemented as: for enhancing white balance processing to the image information. the said image information into area according to the RGB value of the pixel point, gray processing the image information of the divided area, extracting the road feature, the road characteristic input deep learning model trained in advance, output lane and road edge feature.
| 8. The method according to claim 4, wherein the radar information obtaining the lane road edge feature at two sides, is implemented as: performing filtering processing to the radar information, extracting the road edge feature.
| 9. The method according to claim 4, wherein said lane characteristic according to the weight proportion, the road edge feature obtained by the image information and the road edge feature identification vehicle acquired by the radar information of position, comprising: calculating the lane width according to the lane mark feature according to the weight proportion, the road edge feature obtained by the image information and the road edge feature acquired by the radar information of the calculated road width; according to the lane width and the width of the road lane number calculation, and based on the lane width and the lane number identifying the lane position.
| 10. An automatic driving automobile, comprising one of a lane recognition system according to any one of claims 1~3. | The system has a camera for obtaining vehicle driving image information of lane in a path. A radar obtains radar information of an area in which a vehicle is traveling. A processing unit processes image information to obtain lane line features and road edge features. The processing unit processes radar information to obtain road edge features. The processing unit determines a road edge feature obtained by processing image information and a weight ratio of a road edge feature obtained by processing radar information. The processing unit identifies a lane position by processing the road edge feature obtained by radar information and lane line feature. An INDEPENDENT CLAIM is also included for a lane recognition method. Lane recognition system. The system realizes data fusion of the camera and the radar device in lane recognition, so as to better deal with complex road conditions and environmental disturbances, avoids the occurrence of dangerous accidents, and improves the safety of driving. The drawing shows a block diagram of a lane recognition system. '(Drawing includes non-English language text)' |
Please summarize the input | A formation control system of automatic driving vehicleThe application model claims a formation control system of automatic driving vehicle, the formation control system of the automatic driving vehicle comprises a vehicle end and a road end, the vehicle end comprises a vehicle unit and a domain controller, the road end comprises a road side unit, a vehicle unit, for obtaining the sensor data of the vehicle end and the vehicle road cooperative data obtained by the V2X communication mode, and sending the sensor data of the vehicle end and the road end of the vehicle road cooperative data to the domain controller; domain controller, used for performing fusion processing for the received data, and performing formation control of automatic driving vehicle according to the fusion processing result; a road side unit, for obtaining the road cooperation data of the road end and sending to the vehicle end through the V2X communication mode. The application realizes the full link design of automatic driving vehicle formation control based on V2X communication, by combining the sensor data of the vehicle end with the road of the road end, providing more abundant, more reliable judging basis for the formation decision planning, improving the formation control precision.|1. A formation control system of automatic driving vehicle, wherein the formation control system of the automatic driving vehicle comprises a vehicle end and a road end, the vehicle end comprises a vehicle-mounted unit and a domain controller, the road end comprises a road side unit, the vehicle-mounted unit, for obtaining the sensor data of the vehicle end and the vehicle road cooperative data of the road end by the V2X communication mode, and sending the sensor data of the vehicle end and the vehicle road cooperative data of the road end to the domain controller; the domain controller is used for performing fusion processing to the sensor data of the vehicle end and the road end of the road end, and according to the fusion processing result for automatically driving the formation control of the vehicle; the road side unit is used for obtaining the road coordinate data of the road end and sending it to the vehicle end through the V2X communication mode.
| 2. The formation control system of automatic driving vehicle according to claim 1, wherein the domain controller is further used for: performing target prediction according to the fusion processing result; and performing formation control of the automatic driving vehicle according to the target prediction result.
| 3. The formation control system of automatic driving vehicle according to claim 1, wherein the domain controller is further used for: fusing the data of each sensor of the vehicle end, obtaining the sensor data after fusion; integrating the converged sensor data with the road-end vehicle-road cooperative data to obtain the final fusion processing result.
| 4. The formation control system of automatic driving vehicle according to claim 1, wherein the domain controller is further used for: determining whether the bicycle can be used as a pilot vehicle according to the fusion processing result; under the condition that the bicycle can be used as a pilot vehicle, based on self-vehicle for formation decision planning, generating formation decision planning task.
| 5. The formation control system of automatic driving vehicle according to claim 4, wherein the domain controller is further used for: executing the formation decision planning task, and obtaining the self-vehicle fleet according to the execution result of the formation decision planning task; controlling the self-vehicle fleet according to the preset fleet driving strategy for driving.
| 6. The formation control system of automatic driving vehicle according to claim 4, wherein the vehicle unit is further used for: sending the formation request to the surrounding vehicle corresponding to the bicycle through the V2X communication mode, so that the surrounding vehicle according to the formation request application added to the self-vehicle fleet; according to the response result of the surrounding vehicle to the formation request, updating the to-be-processed state list of the vehicle end, the to-be-processed state list comprises adding the vehicle queue list, member list and leaving the vehicle queue list.
| 7. The formation control system of automatic driving vehicle according to claim 6, wherein the vehicle unit is further used for: according to the response result of the surrounding vehicle to the formation request, determining the candidate surrounding vehicle; obtaining the vehicle information of the candidate surrounding vehicle by V2X communication mode, and sending the vehicle information of the candidate surrounding vehicle to the domain controller.
| 8. The formation control system of automatic driving vehicle according to claim 7, wherein the domain controller is further used for: according to the vehicle information of the candidate surrounding vehicle, determining whether the candidate surrounding vehicle satisfy into the requirement of the fleet, under the condition that the candidate surrounding vehicle satisfy added with the requirement of the fleet, the candidate surrounding vehicle is used as the following vehicle to join the self-vehicle fleet.
| 9. The formation control system of automatic driving vehicle according to claim 4, wherein the formation decision planning task comprises a vehicle fleet driving track, the domain controller is further used for: determining a current lane where the bicycle is located; according to the current lane of the bicycle and the driving track of the train, determining whether the bicycle needs to change the lane; under the condition that the bicycle needs to be changed, generating and executing the lane-changing track planning task, so that the bicycle is changed from the current lane to the target lane.
| 10. The formation control system of the automatic driving vehicle according to any one of claims 1 to 9, wherein the vehicle road cooperation data is data obtained by sensing the surrounding environment in the preset range of the road side device, the vehicle road cooperation data comprises other traffic participation object data, traffic signal lamp data and road event data in the one kind of or more. | The system has a vehicle end provided with a vehicle-mounted unit and a domain controller. A road end is provided with a road side unit. The vehicle-mounted unit is used for obtaining the sensor data of the vehicle end and the vehicle road cooperative data of the road end by the V2X communication mode and sending the sensor data of the vehicle end and the vehicle road cooperative data of the road end to the domain controller. The domain controller is used for performing the fusion processing to the sensor data of the vehicle end and the road end. The road side unit is used for obtaining the road coordinate data of the road end and sending to the vehicle end through the V2X communication mode. The domain controller is used for performing the target prediction according to the fusion processing result and performing formation control of the automatic driving vehicle according to the target prediction result. Formation control system for an automatic driving vehicle e.g. automatic driving bus and automatic driving lorry. The application realizes the full link design of automatic driving vehicle formation control based on V2X communication, by combining the sensor data of the vehicle end with the road of the road end, providing more abundant, more reliable judging basis for the formation decision planning, improving the formation control precision. The drawing shows a structure schematic diagram of a formation control system for an automatic driving vehicle e.g. automatic driving bus and automatic driving lorry. (Drawing includes non-English language text). |
Please summarize the input | SYSTEM AND METHOD FOR LONGITUDINAL REACTION-CONTROL OF ELECTRIC AUTONOMOUS VEHICLEA PID automatic control system and method for speed control of an electric vehicle, according to an embodiment, design a commercial vehicle-based autonomous driving system and a controller for development of a semi-autonomous driving acceleration/deceleration controller, develop a driving priority determination algorithm on the basis of V2X communication, develop technologies for correcting autonomous navigation and improving position precision, develop a technology for recognizing autonomous driving road environments by using a cognitive sensor, conceive an application method of a driving habits improvement algorithm by using learning, develop a driving habits improvement algorithm by using learning, and develop an AEB function for a commercial vehicle to which a semi-autonomous driving technology is applied.|1. A PID automatic control system for semi-autonomous driving acceleration/deceleration control, comprising: a communication module for communicating with a nearby vehicle and a leading vehicle when a platooning group is formed around the vehicle;
a detection module for detecting obstacles on the front and rear sides of the vehicle, detecting surrounding vehicle information including the vehicle speed and driving path of the front vehicle, and detecting road information including stop lines, traffic lights, signs, and road curbs;
The vehicle speed is controlled according to the result of V2X communication with communication objects around the vehicle and information about surrounding vehicles and road information, and the amount of change in vehicle speed due to the change in pedal tilt is calculated and reflected in the proportional gain value and error calculation, and acceleration control response characteristics and deceleration control a semi-autonomous driving acceleration/deceleration control module for learning response characteristics and applying the result of learning response characteristics for each vehicle to a gain value calculation;
a deceleration/acceleration sensor that detects the inclination of the pedal and the amount of change in the inclination of the vehicle brake and accelerator; and driver-specific driving data and vehicle control data are collected and driver-specific learning data is applied to the vehicle, and driving habits are identified based on the learning data stored for each driver. a driving habit improvement learning module that improves driving habits through and the semi-autonomous driving acceleration/deceleration control module; is a gain calculator for calculating the difference between the target speed and the running speed, the amount of change in the running speed, and proportional gain (Kp), integral gain (Ki), and differential gain (Kd), which are proportional gains for PID calculation;
an error amount calculator for calculating an error with a target speed after controlling the motor according to the calculated gain value;
a feedback unit for feeding back motor control by applying the calculated error to each gain value; And The detection module implements an autonomous driving navigation position correction algorithm using sensor information to correct the current position of the vehicle from a sensor including a LiDAR and a camera by comparing it with global coordinates, , Through camera coordinate system calibration using camera coordinate system calibration, 1:1 pixel coordinates of external parameters and internal parameters are matched, and the driving habit improvement learning module; the amount of deceleration in the silver curve is small, or the habit of rapidly accelerating when waiting for a signal If monitored, it feeds back to the semi-autonomous driving acceleration/deceleration control module to decelerate further than the amount of deceleration caused by the brake by the driver. PID automatic control system for semi-autonomous driving acceleration/deceleration control, characterized in that it decelerates.
| 2. A PID automatic control method for semi-autonomous driving acceleration/deceleration control, comprising the steps of: (A) an autonomous driving vehicle communicating with a nearby vehicle and a leading vehicle when a platooning group is formed around the vehicle;
(B) detecting obstacles on the front and rear sides of the vehicle in the autonomous vehicle and detecting surrounding vehicle information including the vehicle speed and driving path of the vehicle in front, and road information including stop lines, traffic lights, signs, and curbs; and (C) controlling the vehicle speed according to the result of V2X communication with the communication object around the vehicle in the autonomous vehicle, and information about the surrounding vehicle and the road; and (D) the driver's driving data and vehicle control data in the autonomous vehicle. It collects and applies learning data for each driver to the vehicle, identifies driving habits based on the learning data stored for each driver, and implements autonomous driving to improve driving habits through semi-autonomous driving when high-risk driving habits are identified. Including; and the step of (B); Detecting the inclination of the pedal and the amount of change in the inclination of the vehicle brake and accelerator in the deceleration and acceleration sensor; and calculating a change in vehicle speed due to a change in pedal inclination in the autonomous vehicle and reflecting it in calculating a proportional gain value and an error; comprising the step of (B); implements an autonomous driving navigation position correction algorithm using sensor information to compare and correct the current position of the vehicle from sensors including LiDAR and Camera with global coordinates, and calibrate the camera coordinate system using The pixel coordinates of the external parameter and the internal parameter are matched 1:1 through the camera coordinate system calibration, and the step of (C); is the difference between the target speed and the running speed, the amount of change in the running speed, and the proportional gain for PID calculation calculating a gain (Kp), an integral gain (Ki), and a differential gain (Kd);
calculating an error with a target speed after controlling the motor according to the calculated gain value;
feeding back the motor control by applying the calculated error to each gain value;
learning acceleration control response characteristics and deceleration control response characteristics; And Applying the response characteristic learning result for each vehicle to the gain value calculation; Including; Step of (D); When the amount of deceleration in the curve is small or the habit of sudden acceleration when waiting for a signal is monitored, the driver's brake PID automatic control for semi-autonomous driving acceleration/deceleration control, which feeds back to make the vehicle decelerate further than the amount of deceleration by method. | The system has a communication module (110) that communicates with a nearby vehicle and a leading vehicle when platooning group is formed around the vehicle. A detection module (130) detects obstacles on the front and rear sides of the vehicle. The vehicle speed is controlled according to the V2X communication result with the communication object around the vehicle. A semi-autonomous driving acceleration and deceleration control module (150) learns acceleration control response characteristics and deceleration control response characteristics. A driving habit improvement learning module (170) improves driving habits through semi-autonomous driving. A feedback unit feeds back motor control by applying the calculated error to each gain value. The detection module implements autonomous navigation position correction algorithm using sensor information. An INDEPENDENT CLAIM is included for a proportional integral derivative (PID) automatic control method for semi-autonomous driving acceleration and deceleration control. PID automatic control system for semi-autonomous driving acceleration and deceleration control of 1-ton electric commercial vehicle. The fuel consumption caused by air resistance is reduced, thus improving fuel economy. The semi-autonomous speed control is more accurately performed according to the individual driving characteristics of the vehicle. The driver habit improvement module predicts collision by recognizing obstacle in front of the vehicle being driven by interlocking with the automatic emergency braking system and automatically applies the brake when the driver does not intervene to prevent collision. The drawing shows a block diagram of PID automatic control system for semi-autonomous driving acceleration and deceleration control. (Drawing includes non-English language text) 110Communication module130Detection module150Semi-autonomous driving acceleration and deceleration control module170Driving habit improvement learning module |
Please summarize the input | Interconnected automatic driving decision method based on cooperative sensing and self-adaptive information fusionThe invention claims an interconnected automatic driving decision method based on cooperative sensing and self-adaptive information fusion, which mainly solves the problem that the existing automatic driving decision has low applicability under complex road structure and traffic light information condition. The method considers the multi-lane traffic environment under the world coordinate system, wherein there is the mixed traffic flow composed of the interconnected automatic driving vehicle and the human driving vehicle. Each CAV can obtain surrounding multimodal environmental features (such as lane information, HDV vehicle information, and traffic light information) through a vehicle-mounted sensor and an off-line high precision map. With the help of the vehicle-to-vehicle communication, the CAV can share its information and make a decision within a specified time step t. The aim of the method is to generate speed decision and steering angle decision for CAV. With such action decision, the automatic driving vehicle can safely and effectively drive according to the special route, at the same time, the comfort level of the passenger is greatly improved and the influence to the surrounding HDV is reduced.|1. An interconnected automatic driving decision method based on cooperative sensing and self-adaptive information fusion, wherein it comprises the following steps: S1, collecting the vehicle dynamics information fA of the CAV through the vehicle GNSS and IMU sensor, and detecting the vehicle dynamics information fH of the HDV through the vehicle radar of the CAV; wherein CAV is automatic interconnected driving vehicle, HDV is human driving vehicle; S2, accurately locating the position and direction of the CAV, and identifying the road and traffic light near the CAV, so as to obtain the real-time road information of the CAV preset driving route; S3, each CAV respectively transmits its own vehicle dynamics information fA and the sensed vehicle dynamics information fH of HDV to an MLP, and splices the obtained result codes to form a vehicle code hi; and aggregating the CAV vehicle code based on the CAV communication link matrix M using the graph attention layer to obtain the vehicle flow information of the CAV preset travelling route; wherein MLP represents a multilayer perceptron; S4, adopting the multi-intelligent body strengthening learning algorithm MAPAC training parameterized CAV action structure to obtain the optimal action strategy, and adopting the random Gaussian strategy to improve the searching ability of the algorithm, so as to realize the action decision of CAV; S5, setting the self-central reward function for improving the safety, efficiency and comfort of the CAV and the social influence reward function for reducing the negative influence to the surrounding HDV to optimize the action decision of the CAV.
| 2. The interconnected automatic driving decision method based on collaborative sensing and self-adaptive information fusion according to claim 1, wherein in the step S1, the vehicle dynamics information fA of CAV comprises vehicle speed, direction, length, width, a lane ID and an offset from the lane centre line; The vehicle dynamics information fH of the HDV includes the relative distance, speed, direction of the HDV relative to the CAV and the lane mark and lane centre line offset of the HDV.
| 3. The interconnected automatic driving decision method based on cooperative sensing and self-adaptive information fusion according to claim 2, wherein in the step S2, the CAV preset driving route is composed of multiple lanes of roads. the characteristic fL of the road is represented by the lane track point, wherein each track point comprises the lane horizontal height, namely the lane gradient change, the direction and the lane ID; the traffic light information uses the detection technology based on the camera to detect the real-time state and the distance from the vehicle; wherein red is [1, 0, 0], green is [0, 1, 0], yellow is [0, 0, 1]; if the characteristic fL of the road and the traffic light state fTL are coded by a road encoder, Er, i= sigma (phi (fL, L, fTL, L) | phi (fL, C, fTL, C) | phi (fL, R, fTL, R)), wherein Er, i represents road coding, phi represents MLP layer, sigma is ReLU activation function, fL, L, fTL, L represents the left side lane code and traffic light code of the lane where the vehicle is located, fL, C, fTL, C represents the lane code and traffic light code of the lane where the vehicle is located, fL, R, fTL, R represents the right lane code and the traffic light code of the lane where the vehicle is located, and the absolute value is the connection operation; for each intelligent agent, the attention point is only limited to the lane characteristic of the current lane, the red-green lamp and two adjacent lanes; finally connecting the traffic flow code Et, i and the road code Er, i to obtain the state code Es, i for the final road information code operated by the subsequent module.
| 4. The interconnected automatic driving decision method based on cooperative sensing and self-adaptive information fusion according to claim 3, wherein in the step S3, in the CAV vehicle coding based on CAV communication link matrix M, according to the attention mechanism, Each intelligent agent i in the vehicle communication network calculates the query vector qi, the key vector ki and the value vector vi are listed as follows: qi = Wqhi, ki = Wkhi, vi=Wvhi, in the formula, Wq represents query matrix, Wv represents value matrix, Wk represents key matrix, hi is vehicle code; Assuming that the intelligent agent i has Ni adjacent intelligent agents, the attention score a ij of the intelligent agent to the adjacent intelligent agent j can be calculated as: wherein sigma is the activation function ReLU; LeakyReLU represents a LeakyReLU activation function, exp represents an exponential operation operation, and l represents one of Ni adjacent intelligent bodies; Due to the change of the traffic environment, the intelligent body which lost the communication connection with it in the current time step is filtered, and the final traffic flow code Et is calculated in combination with the CAV link matrix, and i is listed as follows: wherein phi represents the MLP layer, Mi, j are the values of the link matrix, Mi, j = 0 represents that there is no connection between the agent i and the agent j in the current time step, and vice versa; Wherein, the intelligent agent is CAV.
| 5. The interconnected automatic driving decision method based on collaborative sensing and self-adaptive information fusion according to claim 4, wherein in the step S4, the multi-intelligent body strengthening learning algorithm MAPAC uses the actor commentator structure in strengthening learning, wherein the actor network is used for calculating the action, the commentator network is used for evaluating the action through the estimation value function; the random Gaussian network replaces the original depth Q network, the random Gaussian network outputs a Gaussian distribution, the intelligent agent samples from the distribution to form parameterized continuous action; Wherein, in the model training process using the multi-agent enhanced learning algorithm MAPAC, the actor network pi i of the agent i updates the network by minimizing the following objects: wherein the experience buffer D is used for storing the state and action of all interconnected intelligent bodies, and Q (beta) represents the network parameter of the commentator; lambda is the regularization coefficient of the search performance of the control algorithm, and respectively representing the state information and action information of the intelligent agent i in the time step t, an actor network representing the parameters of the agent i; the combined set of the state and action of the interconnected intelligent body is used as the input of the commentator network, the network then outputs the Q value of the action taken by the intelligent body i in the time step t; The critic network is updated by minimizing the following Berman error JQ: wherein gamma is rewarding discount factor, ri is instant rewarding of t time step; Two target commentator networks for stable training process, when executing, each intelligent body operates the copy of the respective actor network and commentator network, namely distributed execution; the intelligent agent i only needs to obtain the observed traffic environment information and performs information enhancement through the shared information from the interconnected intelligent agent, and then calculates the final parameterized action based on the fused information; finally, selecting the action with the maximum Q value as the actually executed action; All CAVs in the Internet follow the process described above to generate their respective action decisions.
| 6. The interconnected automatic driving decision method based on collaborative sensing and self-adaptive information fusion according to claim 5, wherein in the step S5, the self-central reward function and the social influence reward function form a mixed reward function. The expression is as follows: In the formula, rego represents a self-centered reward, and rsoc represents a social impact reward; it is used for quantifying the cooperation degree between the automatic driving vehicle union and the human driving vehicle.
| 7. The interconnected automatic driving decision method based on cooperative sensing and self-adaptive information fusion according to claim 6, wherein the expression of the social influence reward function is as follows: FORMULA. In the formula, rsoc, 1 is used to quantify the incidence of sudden parking of CAV or sudden cut-in of corresponding lane, and the expression is as follows: rsoc = rsoc, 1 + rsoc, 2, wherein rsoc, 1 is used to quantify the incidence of sudden parking of CAV or sudden cut-in of corresponding lane, and the expression is as follows: In the formula, represents the speed of the HDV in the time step t, thrvel is a threshold value of the speed change, for determining whether the CAV causes the quick brake action of the HDV, thracc* delta t is the speed change threshold value between two continuous time steps; The reward is only effective when the HDV deceleration is greater than thrvel; rsoc, 2 is used for quantizing CAV to adjust its speed or position so as to reserve incidence rate of variable track space behaviour for HDV, the expression is as follows: wherein is the adjacent HDV of the agent i in the time step t; when the vehicle of the adjacent lane is in front of the CAV or behind the CAV safe lane change,/> set as 1; In other cases, is set as 0.
| 8. The interconnected automatic driving decision method based on collaborative sensing and self-adaptive information fusion according to claim 7, wherein the expression of the self-central reward function is as follows: FORMULA. rego=rsaf + reff + rcom, in the formula, rsaf is security reward, reff is efficiency reward, rcom is passenger comfort reward; wherein the security reward is the reward sum of CAV unsafe behaviour and traffic rule compliance rate; the vehicle-following safety uses the predicted collision time TTC to ensure the safe vehicle-following distance of the CAV; The TTC calculation formula is as follows: In the formula, fA. level and fH-level represent speeds of CAV and HDV, respectively, dis (A, H) represents Euclidean distance between A and H; On-vehicle security reward rsaf, 1 is calculated as follows: Wherein, is a threshold value of TTC; Secondly, the lane-keeping security reward keeping CAV travels in the center of the lane, and the calculation method is as follows: wherein dis (wp, A) measures the distance between CAV and the central point of the lane, d is half of the width of the lane; the emergency safety is CAV collision, deviation road action or violation traffic signal lamp of penalty, other condition is 0; efficiency reward: the efficiency of the multi-lane task is the sum of the speed control efficiency and the lane changing efficiency; The speed control efficiency reff, 1 promotes the automatic driving vehicle to keep a safe driving speed, calculating as follows: wherein fA. level represents the speed of the automatic driving vehicle, velmax is the maximum driving speed set by the vehicle; lane change reward reff, 2 encourage vehicle overtaking and avoid obstacle; It is calculated as follows after the lane change is completed wherein dis (Htar, A) and dis (Hprev, A) represent the distance of the vehicle from an obstacle or a forward vehicle on the target lane and the previous lane, respectively; Comfortable rewards for passengers: using the vehicle acceleration change rate Jerk to measure; The Jerk calculation method is as follows: wherein acct is the acceleration of the vehicle under the time step t, delta t is each time step length; rcom is calculated by Jerk: wherein thracc is the allowed maximum Jerk value. | The method involves collecting vehicle dynamics information of a vehicle through a vehicle global navigation satellite system (GNSS) and an internet of things (IMU) sensor. A self-central reward function is set for improving safety, efficiency and comfort of a CAV and a social influence reward function for reducing negative influence to a surrounding HDV to optimize an action decision of the CAV. Cooperative sensing and self-adaptive information fusion based interconnected automatic driving decision method for automatic driving vehicles e.g. human driving vehicle (HDV) and inter-connected auto-driving vehicle (CAV). The automatic driving vehicle can safely and effectively drive according to the special route, at the same time, the comfort level of the passenger is greatly improved and the influence to the surrounding HDV is reduced. The drawing shows an overall frame diagram of a cooperative sensing and self-adaptive information fusion based interconnected automatic driving decision method. (Drawing includes non-English language text). |
Please summarize the input | Intelligent cruise robot reverse searching system and method based on cloud wireless transmissionThe invention relates to the technology field of reverse searching in parking lot, specifically to an intelligent cruise robot reverse searching system and method based on cloud wireless transmission. The system comprises four parts of an intelligent cruise robot terminal, a parking space identification terminal, a cloud terminal and a reverse searching inquiry terminal. The system is a full-automatic intelligent robot device with vehicle position identification, license plate automatic identification and mobile walking functions. The searching robot device integrates automatic control, video recognition analysis, autonomous driving, wireless communication and algorithm based on artificial intelligence, realizing fast and accurate recognition of license plate and parking space and issuing the information for searching vehicle position.|1. An intelligent cruise robot reverse searching system based on cloud wireless transmission, wherein the system comprises the following four parts: intelligent cruise robot end of the intelligent cruise robot end comprises one or more intelligent cruise robot; each intelligent cruise robot comprises a high-definition camera and a license plate number recognition software, automatically control the cruise module, universal high-performance wireless communication transmission module, server module and a battery drive module, the high definition camera and license plate number recognition software uses video analysis algorithm for license plate number and the carport associated identification of the vehicle, and the computer visual detection; the automatic control cruise module for controlling the intelligent cruise robot walking and obstacle recognition avoidance function, the module through ultrasonic wave radar plus video visual detection of two kinds of technology, it can detect the size and distance of the obstacle object of all ultrasonic radar in the walking. after calculating controlling the robot to perform the corresponding avoid and special marks (such as white lines) video visual detection may be based on the path of the robot intelligent cruise control according to the route automatic walking; the wireless communication module adopts 4 G internet of things technology responsible for identifying the licence plate number and the associated parking space information to the cloud; the generic high performance server module is used for license plate number recognition software, automatically control the cruise module, wireless communication transmission module provides hardware computing and memory function; the battery drive module for providing intelligent cruise robot driving walking power and to charge the battery under the condition of low battery, parking mark end must be a parking mark end formed by the position identification device for associating the license plate of parking and stopping vehicle, wherein identification device adopts two technical methods for parking space recognition, and the two technical means also determines the working mode and working state of the intelligent cruise robot: A smart sign technology using the intelligent vehicle is mounted below the stall, integrated with an automatic induction device, wireless communication transmission device and the parking state display device; automatic sensing device adopts low power consumption communication technology, when the intelligent car label after sensing the vehicle parking the parking state display device from a vehicle green light into red light of a vehicle and the wireless communication transmission device to send a wireless signal to the intelligent cruise robot; the intelligent cruise robot working mode is driven, robot parked at the appointed place, after the wireless signal or more intelligent receiving the intelligent car label sent by the car label of white lines, road is fast by no change of vehicle area to stall before collecting the vehicle license plate number, B adopts traditional ground printed number technology using traditional printing mode, printing numbers on each parking space, the intelligent cruise robot working mode is active the cruise. Under this working mode, intelligent cruise robot according to the set time interval, driving the all vehicle in the area which is responsible for identifying one identifying vehicle license plate at the same time, the identification number of the ground printed. the two kinds of number associated to the cloud for issuance, cloud the cloud comprising one or more management servers, one or more indoor LED guide screen and one or more outdoor LED guide screen, the server cluster working mode, can support large data storage operation, providing the original learning data for future data mining and artificial intelligence algorithms, the server providing a plurality of external interfaces, which can remotely control one or more indoor LED guide screen, one or more outdoor LED guide screen. communicated through the local area network or the Internet server and the guide screen for multiple parking the parking information, receiving parking and vehicle information transmitted by multiple intelligent cruise robot, providing for indoor and outdoor LED guide screen to issue to realize centralized management and resource sharing of the vehicle information; reverse the searching inquiry user through reverse searching inquiry APP software or Minicell public number and cloud communication end, can query the vehicle position after inputting the inquiry condition.
| 2. A searching method based on the reverse searching system according to claim 1, wherein, when the vehicle identification end is a traditional ground printing vehicle number, the method adopts the intelligent cruise robot via the driving mode to the parking space recognition, specifically comprising the following steps: S1. the parking space of the parking lot A and lot B printed with the traditional ground parking space number associated set walking route on the lane marked white cruise robot, S2. Intelligent cruise robot via preset time (such as every 5 minutes or 10 minutes), driving walking cruise on a predetermined area and route, simultaneous analysis of license plate and parking space number through video analysis and recognition in the walking process, S3. the analysis back to the license and parking space number, the license plate and parking space number information uploaded to the cloud through the wireless communication module of the robot, S4. cloud end after statistic analysis, the transmission guide screen, the vehicle is guided into position to guide indoor LED screen and outdoor LED parking information and vehicle location through the network, S5. APP or Minicell public number owner inquiry end through backward searching when the need to find vehicle to vehicle position inquiry so as to fast and conveniently to the navigation guidance.
| 3. The searching method based on the reverse searching system according to claim 1, wherein when the vehicle identification terminal is intelligent sign, the method uses intelligent cruise robot parking space recognition by passive cruise mode, specifically comprising the following steps: S1. the parking space of the parking lot A and lot B is equipped with an intelligent vehicle, the intelligent vehicle is integrated with automatic induction device, the wireless communication transmission device and the parking state display device, then information associated with the parking space S2. when there is the vehicle parking position, in the intelligent vehicle automatic induction device utilizing photoelectric or electromagnetic induction technology, after sensing the vehicle parks, the intelligent vehicle position state display device of label index light from the non-green light into red light of a vehicle, the intelligent vehicle one or more label by the wireless communication transmission device and respective intelligent cruise robot communication, intelligent cruise robot receives the information, it will rapidly reach with change of the parking area, S3. cloud end after statistic analysis, the transmission guide screen, the vehicle is guided into position to guide indoor LED screen and outdoor LED parking information and vehicle location through the network, S4. APP or Minicell public number owner inquiry end through backward searching when the need to find vehicle to vehicle position inquiry so as to fast and conveniently to the navigation guidance. | The system has an intelligent cruise robot for comprising a high-definition camera, a number plate, an universal high-performance wireless communication transmission module, a server module and a battery drive module. An automatic control cruise module controls intelligent cruise robot walking and obstacle recognition avoidance function. A server is connected with external interfaces to control an indoor LED guide screen. A cloud communication end determines a vehicle position by using reverse searching inquiry application (APP) software or public number after inputting inquiry condition. An INDEPENDENT CLAIM is also included for a cloud wireless transmission based intelligent cruise robot reverse searching method. Cloud wireless transmission based intelligent cruise robot reverse searching system. The system can automatically control video recognition analysis, autonomous driving, wireless communication and algorithm based on artificial intelligence and realize information in the number plate and a parking space after searching the vehicle position. The drawing shows a block diagram of a cloud wireless transmission based intelligent cruise robot reverse searching system. '(Drawing includes non-English language text)' |
Please summarize the input | Method for determining reliability of received dataThe invention relates to a computer-implemented method for determining the reliability level of the data received by the autonomous vehicle from the target vehicle, the target vehicle is different from the autonomous vehicle. The invention also relates to a corresponding control system and a computer program product.|1. A computer implemented method for determining the reliability level of the data received by the autonomous vehicle from the target vehicle, wherein the target vehicle is different from the autonomous vehicle and is arranged near the autonomous vehicle, the autonomous vehicle comprises a control unit, wherein the method comprises the following steps: and using wireless communication at the control unit, receiving a first set of operation data associated with the target vehicle, and determining the reliability level by the control unit based on the first set of operation data and a predetermined model of the expected behaviour of the first set of indicating operation data.
| 2. The method according to claim 1, wherein the predetermined model is further dependent on the target vehicle.
| 3. The method according to any one of claim 1 and 2, wherein the predetermined model further depends on the expected change of the first set of operation data over time.
| 4. The method according to claim 1, further comprising the following steps: using a first sensor included in the autonomous vehicle to determine a second set of operational data associated with the target vehicle by the control unit, and determining, by the control unit, a difference between the first set of operational data and the second set, wherein the control unit determines the second set of operational data associated with the target vehicle, The determination of the level of reliability is also based on the determined difference.
| 5. The method according to claim 4, wherein the predetermined model further indicates an expected behaviour of the second set of operational data.
| 6. The method according to claim 4, wherein the predetermined model further indicates an expected behaviour of the difference between the first set of operational data and the second set.
| 7. The method according to any one of the preceding claim, further comprising the following steps: -only when the reliability level is higher than the first predetermined threshold value, the operation data from the target vehicle is defined as reliable.
| 8. The method according to any one of the preceding claim, further comprising the following steps: -if the reliability level is below a second predetermined threshold value, defining the operation data from the target vehicle as unreliable.
| 9. The method according to claim 1, wherein the first set of operating data relates to at least one of a speed of the target vehicle, an acceleration, a deceleration, and the like.
| 10. The method according to claim 2, further comprising the following steps: -determining an identifier of the target vehicle using a second sensor included in the autonomous vehicle.
| 11. The method according to claim 4, wherein the predetermined model represents a statistical behaviour of the set of operational data.
| 12. The method according to any one of the preceding claim, further comprising the following steps: -establishing a network connection with a server disposed outside the autonomous vehicle, and requesting the predetermined model from a remote server.
| 13. The method according to claim 12, further comprising the following steps: if the reliability level is higher than the third predetermined threshold value, providing the updated model.
| 14. The method according to claim 4, wherein the first sensor is at least one of a radar, a laser radar sensor, or a camera.
| 15. The method according to any one of the preceding claim, wherein the operation data is a vehicle-to-vehicle (V2V) data.
| 16. The invention claims an control system vehicle, comprising a control unit, which is suitable for determining the reliability level of the data received by the autonomous vehicle from the target vehicle, the control system control system vehicle is different from the autonomous vehicle and is arranged near the autonomous vehicle, wherein the control unit is adapted to: receiving a first set of operational data associated with the target vehicle using wireless communication, and determining the reliability level based on the first set of operational data and a predetermined model indicative of an expected behaviour of the first set of operational data.
| 17. The system according to claim 16, wherein the predetermined model is further dependent on the target vehicle.
| 18. The system according to any one of claim 16 and 17, wherein the predetermined model further depends on the expected change of time of the first set of operation data.
| 19. The system according to claim 16, wherein the control unit is further adapted to: -determining a second set of operational data associated with the target vehicle using a first sensor included in the autonomous vehicle, and determining a difference between the first set of operational data and the second set, wherein the determination of the reliability level is further based on the determined difference.
| 20. The system according to claim 19, wherein the predetermined model further indicates an expected behaviour of the second set of operational data.
| 21. The system according to claim 19, wherein the predetermined model further indicates an expected behaviour of the difference between the first set of operational data and the second set.
| 22. The system according to any one of 16 to 22 claim to 22, wherein the control unit is further adapted to: -only when the reliability level is higher than the first predetermined threshold value, the operation data from the target vehicle is defined as reliable.
| 23. The system according to any one of claim 16 to 23, wherein the control unit is further adapted to: -if the reliability level is below a second predetermined threshold value, defining the operation data from the target vehicle as unreliable.
| 24. A vehicle comprising the control system according to any one of claim 16 to 23.
| 25. The vehicle according to claim 24, wherein the vehicle is a truck, a vehicle, a bus or a working machine.
| 26. A computer program product comprising a non-transitory computer readable medium, the non-transitory computer readable medium is stored with a computer program component for operating the control system included in the autonomous vehicle; said control system is suitable for determining the reliability level of the data received by the autonomous vehicle from the target vehicle, the target vehicle is different from the autonomous vehicle and is arranged near the autonomous vehicle, the control system comprises a control unit, wherein the computer program product comprises: a code for receiving, at the control unit, a first set of operational data associated with the target vehicle using wireless communication, and a code for determining the reliability level by the control unit based on the first set of operating data and a predetermined model of expected behaviour of the first set of operating data. | The method involves receiving a first set of operational data relating to the target vehicle, at the control unit and using wireless communication. The reliability level based on the first set of operational data and a predetermined model indicative of an expected behavior of the first set of operational data is determined by the control unit. The predetermined model is further dependent on the target vehicle. The predetermined model is dependent on an expected variation over time of the first set of operational data. The method comprises determining, by the control unit, a second set of operational data related to the target vehicle using a first sensor comprised with the ego vehicle. INDEPENDENT CLAIMS are included for the following:a control system comprised with an ego vehicle;a computer program product; anda vehicle comprising a control system. Method for use in determining a reliability level of data received by an ego vehicle from a target vehicle being different from the ego vehicle. Greatly improves the determination of the reliability level for the received data. The drawing shows a schematic view of a conceptual control system. 200Control system202Control unit204Radar206Lidar sensor arrangement208Camera |
Please summarize the input | Method and system of determining trajectory for an autonomous vehicleA method of determining a navigation trajectory for an autonomous ground vehicle (AGV) is disclosed. The method may include receiving first Region of Interest (ROI) data associated with an upcoming trajectory path, and receiving predicted attributes associated with a future navigation trajectory for the upcoming trajectory path. The predicted attributes are derived based on map for the upcoming trajectory path. The method may further include modifying the predicted attributes based on environmental attributes extracted from first ROI data to generate modified attributes, and dynamically receiving a second ROI data associated with the upcoming trajectory path upon reaching the upcoming trajectory path. The method may further include predicting dynamic attributes associated with an imminent navigation trajectory for the upcoming trajectory path based on the second ROI data, and refining the modified attributes based on the one or more dynamic attributes to generate a final navigation trajectory.What is claimed is:
| 1. A method of determining a navigation trajectory for an autonomous ground vehicle (AGV), the method comprising:
receiving, by a navigation device, first Region of Interest (ROI) data associated with an upcoming trajectory path,
wherein the first ROI data is captured using a set of vision sensors installed on one or more road side infrastructures over vehicle-to-infrastructure (V2I) communication network, and
wherein the first ROI data is an environmental data indicative of an obstacle present in a predetermined ROI along the upcoming trajectory path from an anticipated future location of the AGV;
deriving, by the navigation device, one or more environmental attributes associated with the predetermined ROI based on the first ROI data;
receiving, by the navigation device, one or more predicted attributes associated with a static map of the predetermined ROI, from a cloud,
wherein the one or more predicted attributes are predicted by performing at least one of: a semantic segmentation, an object detection, and a lane detection on map data associated with the predetermined ROI, using a first artificial intelligence (AI) prediction model deployed on a cloud-based computing device;
modifying, by the navigation device, the one or more predicted attributes associated with the static map of the predetermined ROI, based on the one or more environmental attributes associated with the predetermined ROI, to generate one or more modified attributes associated with the static map of the predetermined ROI along the future navigation trajectory, wherein the one or more modified attributes are generated by:
extracting environmental information from the one or more environmental attributes, wherein the environmental information indicates obstacles present in the predetermined ROI;
receiving predictions from a cloud server;
merging the environmental information and the predictions received from the cloud server; and
generating the one or more modified attributes based on the merging of the environmental information and the predictions received from the cloud server;
receiving, by the navigation device, a second ROI data associated with the predetermined ROI along the upcoming trajectory path upon reaching the anticipated future location,
wherein the second ROI is captured by a current field-of-view (FOV) of the camera sensor mounted on the AGV;
predicting, by the navigation device, one or more dynamic attributes associated with the predetermined ROI along the upcoming trajectory path based on the second ROI data using a second AI prediction model deployed on the navigation device;
determining, by the navigation device, an error based on a comparison between the one or more modified attributes with the one or more dynamic attributes;
generating, by the navigating device, one or more refined attributes by correcting the one or more modified attributes based on the error;
updating, by the navigation device, the future navigation trajectory based on the one or more refined attributes to generate a final navigation trajectory to refine the one or more dynamic attributes, wherein the one or more modified attributes are refined based on the one or more dynamic attributes to generate the final navigation trajectory; and
controlling, by the navigation device, the AGV to follow the final navigation trajectory.
| 2. The method of claim 1, wherein, the one or more dynamic attributes associated with the predetermined ROI are predicted, by the second AI prediction model by performing at least one of: the semantic segmentation, the object detection, and the lane detection.
| 3. The method of claim 1, wherein the first ROI data is further captured using a set of vision sensors installed on or other AGVs, and wherein the first ROI data is provided to the navigation device or vehicle-to-vehicle (V2V) communication network.
| 4. The method of claim 1, wherein the one or more environmental attributes comprise at least one of a type of an obstacle present in the upcoming trajectory path, and a location of the obstacle.
| 5. A navigation device for determining a navigation trajectory for an autonomous ground vehicle (AGV), the navigation device comprising:
a processor; and
a memory communicatively coupled to the processor, wherein the memory stores processor instructions, which, on execution, causes the processor to:
receive first Region of Interest (ROI) data associated with an upcoming trajectory path,
wherein the first ROI data is captured using a set of vision sensors installed on one or more road side infrastructures over vehicle-to-infrastructure (V2I) communication network,
wherein the first ROI data is an environmental data indicative of an obstacle present in a predetermined ROI along the upcoming trajectory path from an anticipated future location of the AGV;
derive one or more environmental attributes associated with the predetermined ROI based on the first ROI date;
receive one or more predicted attributes associated with a static map Of the predetermined ROI, from a cloud;
wherein the one or more predicted attributes are predicted by performing at least one of: a semantic segmentation, an object detection, and a lane detection based—on map data associated with the predetermined ROI using a first artificial intelligence (AI) prediction model deployed on a cloud-based computing device;
modify the one or more predicted attributes associated with the static map of the predetermined ROI, based on the one or more environmental attributes associated with the predetermined ROI, to generate one or more modified attributes associated with the static map of the predetermined ROI along the future navigation trajectory, wherein the one or more modified attributes are generated by:
extracting environmental information from the one or more environmental attributes, wherein the environmental information indicates obstacles present in the predetermined ROI,
receiving predictions from a cloud server,
merging the environmental information and the predictions received from the cloud server, and
generating the one or more modified attributes based on the merging of the environmental information and the predictions received from the cloud server,
receive a second ROI data associated with the predetermined ROI along the upcoming trajectory path upon reaching the anticipated future location,
wherein the second ROI is captured by a current field-of-view (FOV) of the camera sensor mounted on the AGV;
predict one or more dynamic attributes associated with the predetermined ROI along the upcoming trajectory path based on the second ROI data using a second AI prediction model deployed on the navigation device;
determine an error based on a comparison between the one or more modified attributes with the one or more dynamic attributes;
determine an error based on a comparison between the one or more modified attributes with the one or more dynamic attributes;
generate one or more refined attributes by correcting the one or more modified attributes based on the error;
update the future navigation trajectory based on the one or more refined attributes to generate a final navigation trajectory to refine the one or more dynamic attributes, wherein the one or more modified attributes are refined based on the one or more dynamic attributes to generate the final navigation trajectory; and
control the AGV to follow the final navigation trajectory.
| 6. The navigation device of claim 5, wherein the one or more dynamic attributes associated with the predetermined ROI are predicted, based-on by the second AI prediction model by performing at least one of: the semantic segmentation, the object detection, and the lane detection.
| 7. The navigation device of claim 5, wherein the first ROI data is captured using a set of vision sensors installed on or other AGVs, and wherein the first ROI data is provided to the navigation device over vehicle-to-vehicle (V2V) communication network.
| 8. The navigation device of claim 5, wherein the one or more environmental attributes comprise at least one of a type of an obstacle present in the upcoming trajectory path, and a location of the obstacle.
| 9. A non-transitory computer-readable storage medium having stored thereon, a set of computer-executable instructions causing a computer comprising one or more processors to perform steps comprising:
receiving first Region of Interest (ROI) data associated with an upcoming trajectory path,
wherein the first ROI data is captured using a set of vision sensors installed on one or more road side infrastructures,
wherein the first ROI data is an environmental data indicative of an obstacle present in a predetermined ROI along the upcoming trajectory path from an anticipated future location of an autonomous ground vehicle (AGV);
deriving one or more environmental attributes associated with the predetermined ROI based on the first ROI data;
receiving one or more predicted attributes associated with a static map of the predetermined ROI, from a cloud,
wherein the one or more predicted attributes are predicted by performing at least one of: a semantic segmentation, an object detection, and a lane detection on map data associated with the predetermined ROI using a first artificial intelligence (AI) prediction model deployed on a cloud-based computing device;
modifying the one or more predicted attributes associated with the static map of the predetermined ROI, based on the one or more environmental attributes associated with the predetermined ROI to generate one or more modified attributes associated with the static map of the predetermined ROI along the future navigation trajectory, wherein the one or more modified attributes are generated by:
extracting environmental information from the one or more environmental attributes, wherein the environmental information indicates obstacles present in the predetermined ROI,
receiving predictions from a cloud server,
merging the environmental information and the predictions received from the cloud server, and
generating the one or more modified attributes based on the merging of the environmental information and the predictions received from the cloud server,
receiving a second ROI data associated with the predetermined ROI along the upcoming trajectory path upon reaching the anticipated future location,
wherein the second ROI is captured by a current field-of-view (FOV) of the camera sensor mounted on the AGV;
predicting one or more dynamic attributes associated with the predetermined ROI along the upcoming trajectory path based on the second ROI data using a second AI prediction model deployed on the navigation device;
determining an error based on a comparison between the one or more modified attributes with the one or more dynamic attributes;
generating one or more refined attributes by correcting the one or more modified attributes based on the error;
updating the future navigation trajectory based on the one or more refined attributes to generate a final navigation trajectory to refine the one or more dynamic attributes, wherein the one or more modified attributes are refined based on the one or more dynamic attributes to generate the final navigation trajectory; and
controlling the AGV to follow the final navigation trajectory.
| 10. The non-transitory computer-readable storage medium of 9, wherein one or more dynamic attributes associated with the ROI are predicted by the second AI prediction model by performing based-on at least one of a semantic segmentation, an object detection, and a lane detection.
| 11. The non-transitory computer-readable storage medium of claim 9, wherein the first ROI data is captured using a set of vision sensors installed on other AGVs, and wherein the first ROI data is provided to the navigation device over vehicle-to-vehicle (V2V) communication network.
| 12. The non-transitory computer-readable storage medium of claim 9, wherein the one or more environmental attributes comprise at least one of a type of an obstacle present in the upcoming trajectory path, and a location of the obstacle. | The method involves receiving region of interest (ROI) data associated with an upcoming trajectory path by a navigation device, where the ROI data is captured while approaching the trajectory path from an anticipated future location of an autonomous ground vehicle (AGV). A set of predicted attributes associated with a future navigation trajectory for the trajectory is received by the navigation device. The attributes are modified based on a set of environmental attributes to generate modified attributes. The modified attributes are refined based on the dynamic attributes by the device to generate a final navigation trajectory by using an artificial intelligence (AI) prediction model. INDEPEDENT CLAIMS are included for the followinga navigation device for determining navigation trajectory for AGV; anda non-transitory computer-readable storage medium storing program for determining navigation trajectory for AGV. Method for determining navigation trajectory for AGV i.e. autonomous ground vehicle (AGV), in indoor and outdoor settings to facilitate efficient transportation. The AGV is capable of sensing the dynamic changing environment, and of accurately navigating without any human intervention. The method provides for automatic classification for the objects detected in an environment and for enhancing mapping for the AGVs. The drawing shows a schematic view of the system for determining a navigation trajectory.602Computing system 604Processor 608Input device 610Output device 626RAM |
Please summarize the input | MODIFYING BEHAVIOR OF AUTONOMOUS VEHICLE BASED ON PREDICTED BEHAVIOR OF OTHER VEHICLESA vehicle configured to operate in an autonomous mode could determine a current state of the vehicle and the current state of the environment of the vehicle. The environment of the vehicle includes at least one other vehicle. A predicted behavior of the at least one other vehicle could be determined based on the current state of the vehicle and the current state of the environment of the vehicle. A confidence level could also be determined based on the predicted behavior, the current state of the vehicle, and the current state of the environment of the vehicle. In some embodiments, the confidence level may be related to the likelihood of the at least one other vehicle to perform the predicted behavior. The vehicle in the autonomous mode could be controlled based on the predicted behavior, the confidence level, and the current state of the vehicle and its environment.|1. A method, comprising:
* determining, using a computer system (112), a current state of a vehicle (308), wherein the vehicle is configured to operate in an autonomous mode;
* determining, using the computer system, a current state of an environment of the vehicle (308), wherein the environment of the vehicle (308) comprises at least one other vehicle (312, 314);
* determining, using the computer system, a predicted behavior of the at least one other vehicle (312, 314) based on at least the current state of the vehicle (308) and the current state of the environment of the vehicle (308);
* determining, using the computer system, a confidence level, wherein the confidence level comprises a likelihood of the at least one other vehicle (312, 314) to perform the predicted behavior, and wherein the confidence level is based on at least the predicted behavior, the current state of the vehicle (308), and the current state of the environment of the vehicle (308); and
* controlling, using the computer system, the vehicle (308) in the autonomous mode based on the predicted behavior, the confidence level, the current state of the vehicle (308), and the current state of the environment of the vehicle (308).
| 2. The method of claim 1, wherein determining the current state of the vehicle comprises determining at least one of a current speed of the vehicle, a current heading of the vehicle, a current position of the vehicle, and a current lane of the vehicle.
| 3. The method of claim 1, wherein determining the current state of the environment of the vehicle comprises determining at least one of a respective position of the at least one other vehicle, a respective speed of the at least one other vehicle, and a position of an obstacle.
| 4. The method of claim 1, wherein controlling the vehicle comprises at least one of controlling the vehicle to accelerate, controlling the vehicle to decelerate, controlling the vehicle to change heading, controlling the vehicle to change lanes, controlling the vehicle to shift within the current lane and controlling the vehicle to provide a warning notification.
| 5. The method of claim 1, wherein the predicted behavior is determined by obtaining a match or near match between the current states of the vehicle and the environment of the vehicle and predetermined scenarios.
| 6. The method of claim 4, wherein the warning notification comprises at least one of a horn signal, a light signal, and a vehicle-to-vehicle communication message transmission and optionally wherein the vehicle-to-vehicle communication message transmission is transmitted using a dedicated short range communications (DSRC) device.
| 7. A vehicle (308), comprising:
* at least one sensor (310), wherein the at least one sensor is configured to acquire:
* i) vehicle state information; and
* ii) environment state information;
* wherein the vehicle state information comprises information about a current state of the vehicle (308), wherein the environment state information comprises information about a current state of an environment of the vehicle (308), wherein the environment of the vehicle comprises at least one other vehicle (312, 314); and
* a computer system configured to:
* i) determine the current state of the vehicle (308) based on the vehicle state information;
* ii) determine the current state of the environment of the vehicle (308) based on the environment state information;
* iii) determine a predicted behavior of the at least one other vehicle (312, 314) based on at least the current state of the vehicle (308) and the current state of the environment of the vehicle (308);
* iv) determine a confidence level, wherein the confidence level comprises a likelihood of the at least one other vehicle (312, 314) to perform the predicted behavior, and wherein the confidence level is based on at least the predicted behavior, the current state of the vehicle (308), and the current state of the environment of the vehicle (308); and
* v) control the vehicle (308) in the autonomous mode based on the predicted behavior, the confidence level, the current state of the vehicle (308), and the current state of the environment of the vehicle (308).
| 8. The vehicle of claim 7, wherein the at least one sensor comprises at least one of a camera, a radar system, a lidar system, a global positioning system, and an inertial measurement unit.
| 9. The vehicle of claim 7, wherein the computer system is further configured to determine the current state of the vehicle based on at least one of a current speed of the vehicle, a current heading of the vehicle, a current position of the vehicle, and a current lane of the vehicle.
| 10. The vehicle of claim 7, wherein the computer system is further configured to determine the current state of the environment of the vehicle based on at least one of a respective position of the at least one other vehicle, a respective speed of the at least one other vehicle, a position of an obstacle, and a map of the roadway.
| 11. The vehicle of claim 7, wherein the computer system is further configured to cause at least one of accelerating the vehicle, decelerating the vehicle, changing a heading of the vehicle, changing a lane of the vehicle, shifting a position of the vehicle within a current lane, and providing a warning notification.
| 12. The vehicle of claim 7, wherein the warning notification comprises at least one of a horn signal, a light signal, and a vehicle-to-vehicle communication message transmission.
| 13. The vehicle of claim 7, wherein the vehicle-to-vehicle communication message transmission is transmitted using a dedicated short range communications (DSRC) device.
| 14. A non-transitory computer readable medium having stored therein instructions executable by a computer system to cause the computer system to perform functions comprising:
* determining a current state of a vehicle (308), wherein the vehicle is configured to operate in an autonomous mode;
* determining a current state of an environment of the vehicle (308), wherein the environment of the vehicle comprises at least one other vehicle (312, 314);
* determining a predicted behavior of the at least one other vehicle (312, 314) based on at least the current state of the vehicle (308) and the current state of the environment of the vehicle (308);
* determining a confidence level, wherein the confidence level comprises a likelihood of the at least one other vehicle (312, 314) to perform the predicted behavior, and wherein the confidence level is based on at least the predicted behavior, the current state of the vehicle (308) and the current state of the environment of the vehicle (308); and
* controlling the vehicle (308) in the autonomous mode based on the predicted behavior, the confidence level, the current state of the vehicle (308), and the current state of the environment of the vehicle (308).
| 15. A computer program to be executed by the computer system of the vehicle claimed in any one of claims 7 to 13 to perform a method as claimed in any one of claims 1 to 6. | The behavior modification method involves determining confidence level which comprises the likelihood of at least one other vehicle (314,316) to perform a predicted behavior including acceleration, deceleration, change heading, change lanes, and leaving roadway. The confidence level is determined is based on predicted behavior of other vehicle, current vehicle state, and current vehicle environment state. Own vehicle (308) is controlled in autonomous mode based on the predicted behavior, confidence level, current vehicle state, and current vehicle environment state. INDEPENDENT CLAIMS are included for the following:a vehicle; anda non-transitory computer readable medium for storing instructions executable by computer system. Behavior modification method for vehicle (claimed) e.g. truck based on predicted behavior of other vehicle. Interaction between vehicles is allowed through the peripherals. Safety is improved through the computer system that causes the vehicle to slow down slightly by reducing the throttle. The drawing shows the top view of the autonomous vehicle operating scenario.302Left-most lane304Center lane306Right-most lane308Own vehicle314,316Other vehicle320Scenario |
Please summarize the input | Portable universal autonomous driving systemThis invention includes an autonomous driving system for automobiles, comprising: one or more common electronic communication ports of autonomous driving (communication ports) that are built-in on the automobiles; and one or more universal autonomous driving portable controllers (portable controllers) that are to be plugged-in to the said communication ports that are built-in on the automobiles. The interfaces of the communication ports and the portable controllers are both standardized such that the portable controllers can be plugged-in universally to all of the automobiles that are equipped with the built-in communication ports. The communication ports comprise electronic communication of all relevant electronic control units (ECUs) and feedback information of the automobiles, dedicated for the said portable controllers to communicate with and to control the automobiles. In addition to the portable controllers, the communication ports comprise a buffer that is designed to execute a short duration of controls to make emergency stops, in case of loss of connection with the portable controllers due to accidents or other failure conditions. The portable controllers comprise a central control unit (CCU), and a plurality of sensors and processors, and a plurality of data storages, and a plurality of data links, and a Global Positioning System (GPS). The portable controllers have standardized interfaces that match with that of the communication ports. The invention disclosed herein enables all automobiles to be ready for autonomous driving with minimal cost, provided that the said communication ports are adapted to the automobiles. The said portable controllers integrate all the hardware and software relevant to autonomous driving as standalone devices which can share the components, simplify the systems, reduce parasitic material and components, and most importantly, will be safer when multiple sensors and processors that are based on different physics are grouped together to detect objects and environment conditions. A method of compound sensor clustering (CSC) is introduced herein. The CSC method makes the sensors and processors to self-organize and to address real-world driving conditions. The portable controllers can be mass-produced as standard consumer electronics at lower cost. The portable controllers can also be more easily updated with the latest technologies since that they are standalone devices, which would be otherwise hard to achieve when the hardware and software are built-in permanently as part of the automobiles. The invention disclosed herein is more efficient, since that the portable controllers can be plugged-in to the automobiles when there are needs for autonomous driving, comparing with current methods of integrating autonomous driving control hardware and software that are built-in to automobiles permanently, which may not be used for autonomous driving frequently. The system also decouples the liability from automotive manufactures in case of accidents. The portable controllers can be insured by insurance companies independently, much like insuring human drivers.I claim:
| 1. An autonomous driving system for an automobile, comprising:
a) one or more common electronic communication ports ( 100) for autonomous driving, wherein the communication ports are built-in on the automobile;
b) one or more universal autonomous driving portable controllers, wherein said portable controllers are plugged in to the exterior of the automobile via the communication ports to detect a driving environment and to control the automobile for autonomous driving; wherein the communication ports and portable controllers share common interfaces;
c) said one or more communication ports having a primary high speed control area network wherein said primary high speed control area network providing communication between said one or more portable controllers via said one or more communication ports, and at least one electronic control unit, further wherein said at least one electronic control unit configured to control at least one of steering, braking, and acceleration;
d) said one or more communication ports having a secondary control area network, said secondary control area network configured to provide electronic communication, via said one or more communication ports, between said one or more portable controllers and secondary electronic control units, said secondary electronic control units configured to control at least one of turn signals, brake lights, emergency lights, head lamps and tail lamps, fog lamps, windshield wipers, defrosters, defogs, window regulators, and door locks;
e) said one or more communication ports having a tertiary control area network configured to electronically communicate at least one feedback parameter to said one or more portable controllers, via said one or more communication ports, said at least one feedback parameter comprised one or more of velocity, acceleration, ABS activation, airbag deployment, and traction control activation;
f) said one or more communication ports having a quaternary control area network configured to electronically communicate at least one status parameter to said one or more portable controllers via said one or more communication ports, said at least one status parameter comprised of one or more of fuel level, battery charge, tire pressure, engine oil level, coolant temperature, and windshield washer level;
g) said one or more communication ports having a buffer memory controller that provides emergency control instruction for emergency stops of the automobiles in the event of loss of electronic connection with the portable controller due to accidents or other failure conditions;
h) said one or more communication ports having electronic connections to the portable controllers and adapted to take at least one of the methods of: wired pin connections, wireless connections, or combinations of wired pin and wireless connections;
i) said one or more portable controllers adapted for mounting locations and anchorages for the portable controllers, which match with the configurations of the portable controllers;
j) a driver interface, said driver interface positioned to enable the driver to provide driving instructions to said one or more portable controllers;
k) said one or more portable controllers having a plurality of sensors, said plurality of sensors comprising:
i. one or more digital color cameras that detect optical information;
ii. one or more LIDARs that detect geometrical information;
iii. task specific sensors, including one or more ultrasonic sensors to detect near distance objects;
iv. one or more RADARs to detect median and far distance objects;
v. one or more thermal imaging cameras or passive infrared sensors to detect objects that have heat emissions;
vi. one or more three dimensional accelerometers to detect acceleration and vibration in vertical, lateral, and fore/aft directions;
vii. one or more gyroscopes to detect inclination angles;
viii. one or more physical-chemical sensors which adapted to detect specific air contents;
ix. one or more sound sensors to detect human languages or warning sirens;
x. one or more water sensors for detecting rain and rain intensity;
xi. one or more temperature sensors adapted for detecting temperature at the vicinity of the automobiles;
l) said one or more portable controllers having a plurality of processors comprising:
i. one or more processors for the digital color cameras;
ii. one or more processors for the LIDARs;
iii. one or more processors for the ultrasonic sensors;
iv. one or more processors for the RADARs;
v. one or more processors for the thermal imaging cameras or passive infrared sensors;
vi. one or more processors for the one or more three dimensional accelerometers;
vii. and one or more processors for the gyroscopes;
viii. one or more processors for the physical-chemical sensors;
ix. one or more processors for the sound sensors;
x. one or more processors for the water sensors;
xi. one or more processors for the temperature sensors;
m) said one or more portable controllers programmed to generate driving instructions based on information from said plurality of processors; said processors of the plurality of processors programmed to generate queries addressing specific driving conditions, said specific driving conditions being determined by pre-defined criteria, wherein said queries include queries between the processors of said plurality of processors, said queries programmed in the processors;
n) said one or more portable controllers having a Central Control Unit to direct the operations of the processors;
o) said one or more portable controllers having a plurality of communication links to send and/or receive data, said communication links including vehicle-to-vehicle and vehicle-to-infrastructure links;
p) said one or more portable controllers having a global positioning system to identify the locations of the automobiles to which the portable controllers are plugged-in;
q) said one or more universal autonomous driving portable controllers are compatible with said communication ports.
| 2. The autonomous driving system of claim 1 wherein,
a. the processors of said plurality of processors are integrated into said one or more portable controllers,
b. the sensors of said plurality of sensors are each integrated with at least one of the processors.
| 3. The autonomous driving system of claim 2 wherein the sensors of said plurality of sensors are built on one or more common substrates and/or integrated circuit boards.
| 4. The autonomous driving system of claim 2 further comprising wherein querying sensors are dynamically organized as clusters to function as groups such that sensors and processors communicate with each other to validate sensed information pertaining to specific driving conditions.
| 5. The autonomous driving system of claim 2 further comprising wherein queries function to detect mismatches between information between sensors and alert the Central Control Unit when mismatches are found.
| 6. The autonomous driving system of claim 5 wherein a mismatch between LIDARs and RADARs generates an alert to the central Control Unit, thereby enabling the Central Control Unit to respond to potential hazards.
| 7. The autonomous driving system of claim 5 wherein information derived from queries from the temperature sensors and water sensors is used to jointly determine a potential freezing rain condition.
| 8. The autonomous driving system of claim 5 wherein the queries for detection of said potential freezing rain condition include detection of rain, and/or ice, and/or snow using captured images and pattern recognition.
| 9. The autonomous driving system of claim 5 wherein detection of smoke by said physical chemical is used to query the thermal imaging cameras of passive infrared sensors to determine if there is a hazardous fire condition.
| 10. The autonomous driving system of claim 5 wherein road curvatures are detected by the cameras and/or LIDARs when lateral acceleration is detected by combined information from said gyroscopes and accelerometers to inform the central control unit of lateral stability status.
| 11. The autonomous driving system of claim 8 wherein ABS activation feedback triggers querying the water and temperature sensors.
| 12. The autonomous driving system of claim 2 wherein the cameras are queried to identify icy road surfaces, thereby generating a categorized information of low coefficient of friction road surface to the Central Control Unit.
| 13. The autonomous driving system of claim 5 wherein information derived from queries from the cameras and thermal sensors is used to jointly verify an existence of pedestrians.
| 14. The autonomous driving system of claim 5 wherein,
a. said thermal sensors are queried to detect a human heat signature, and if the human heat signature is detected, then,
b. the thermal sensor's processor queries object detection sensors for the presence of a human, said object sensors comprising the cameras, LIDARs, RADAR and/or the ultrasonic sensors.
| 15. The autonomous driving system of claim 5 wherein information derived from the RADARs and/or the ultrasonic sensors detection of a potential road sign generates a query to the cameras, thereby reducing likelihood of missing or misidentifying road signs.
| 16. The autonomous driving system of claim 15 wherein the queries from a RADAR are generated for detection of a road sign not identified or misidentified by camera captured images and pattern recognition.
| 17. The autonomous driving system of claim 3 further comprising wherein querying sensors are dynamically organized as clusters to function as groups such that sensors and processors communicate with each other to validate sensed information pertaining to specific driving conditions.
| 18. The autonomous driving system of claim 17, further comprising wherein queries function to detect mismatches between information between sensors and alert the Central Control Unit when mismatches are found.
| 19. The autonomous driving system of claim 14, wherein the sensors of said plurality of sensors are built on one or more common substrates and/or integrated circuit boards.
| 20. The autonomous driving system of claim 3, wherein one or more of the queries from at least one of said one or more RADARs are generated for detection of road signs not identified or misidentified by camera captured images and pattern recognition. | The computerized control system has common electronic communication ports (100) that are built-in on each of automobiles, and one or more universal autonomous driving portable controllers (200) that can be attached to the automobiles via the communication ports to accomplish the computerized control or autonomous driving. INDEPENDENT CLAIMS are included for the following:a design for buffer memory controller (BMC);a design of location of interface of communication ports;a design of the communication ports;a design of universal autonomous driving portable controllers;a sensor;a compound sensor clustering method;a back-up safety mechanism of interacting with the buffer memory controller; anda manufacturing rights of the electronic communication port of autonomous driving. Computerized control system or autonomous driving for automobiles. Ensures that computerized control or autonomous driving much more efficient, since that the portable controllers can be plugged-in to any of the automobiles that are equipped with the communication ports when there are needs for autonomous driving. The drawing shows the design of universal autonomous driving portable controller and its relation to common electronic communication port of autonomous driving. 100Common electronic communication ports160Mounting fixtures on automobiles200Autonomous driving portable controllers221Data storages230Central control unit241Data links260Wired or wireless user interface |
Please summarize the input | PREDICTING REALISTIC TIME OF ARRIVAL FOR QUEUE PRIORITY ADJUSTMENTA queue prioritization system and method for predicting a realistic time of arrival for performing a queue priority adjustment is provided. The method includes the steps of determining an estimated initial arrival time of a first user and a second user to a destination, the estimated initial arrival time being used to establish a queue priority, tracking a current location and predicting a route to be taken to arrive at the destination from the current location, detecting a schedule-altering event of the first user by analyzing: (i) the predicted route of the first user, or (ii) a current state of a vehicle, and reprioritizing the queue priority database, in response to calculating an updated queue priority of the first user that is lower than the queue priority of the second user, based on the detection of the schedule-altering event of the first user.CLAIMS
| 1. A method for predicting a realistic time of arrival for performing a queue priority adjustment, the method comprising:
determining, by a processor of a computing system, an estimated initial arrival time of a first user and a second user to a destination, the estimated initial arrival time being used to establish a queue priority of the first user and a queue priority of the second user, in a queue priority database, the queue priority of the first user being higher than the queue priority of the second user;
tracking, by the processor, a current location of the first user and a current location of the second user, during transit to the destination;
predicting, by the processor, a route to be taken to arrive at the destination from the current location of the first user and the current location of the second user, respectively; detecting, by the processor, a schedule-altering event of the first user by analyzing: (i) the predicted route of the first user, or (ii) a current state of a vehicle; and
reprioritizing, by the processor, the queue priority database, in response to calculating an updated queue priority of the first user that is lower than the queue priority of the second user, based on the detection of the schedule-altering event of the first user.
| 2. The method of claim 1, wherein determining the estimated initial arrival time of the first user and the second user includes:
receiving, by the processor, a customer pick-up order and current GPS location information of the first user and the second user, the GPS location information obtained from a mobile device of the first user and the second user;
reviewing, by the processor, historical user data, including a historical path taken by the first user and the second user to the destination;
evaluating, by the processor, a complexity of the customer pick-up order to determine an earliest store pick-up time; and
comparing, by the processor, the earliest store pick-up time, the current GPS location of the first user and the second user, and the historical path taken by the first user and the second user to the destination, to determine the estimated initial arrival time, in response to: (i) prompting the first user and the second user to depart for the destination, or (ii) receiving confirmation from the first user and the second user that the first user and the second user have departed for the destination.
| 3. The method of claim 2, wherein prompting the first user and the second user to depart for the destination includes providing, by the processor, a suggested departure time based on at least one of: the earliest store pick-up time, current traffic conditions, current location of the first user and the second user, and historical traffic patterns of the first user and the second user.
| 4. The method of claim 1, wherein determining the estimated initial arrival time includes: receiving, by the processor, scheduled delivery information and current GPS location information of the first user and the second user;
reviewing, by the processor, historical delivery pattern data, based on previous
deliveries to the destination;
evaluating, by the processor, the scheduled delivery information, the current GPS location information of the first user and the second user, and the historical delivery pattern data, to determine an earliest delivery arrival time.
| 5. The method of claim 1, wherein the detecting of the schedule -altering event includes receiving data from a plurality of data sources, the plurality of data sources including a current GPS location of the user received from a mobile device of the user, a real-time traffic data received from the mobile device of the user, a real-time traffic data received from a third party application server, a weather data received from the mobile device of the user, a weather data retrieved from a third party application server, a historical traffic pattern information of the user, a sensor data received from one or more sensors associated with the user, a vehicle and traffic information received from a vehicle-to- vehicle communication network, and a combination thereof.
| 6. The method of claim 1, wherein the schedule-altering event is at least one of: a delay, a traffic jam, a traffic accident, a vehicle failure, a weather occurrence, an intervening stop by the first user, a wrong turn of the user, an alternative route taken by the user, a predicted traffic delay of the first user, and a predicted weather delay of the first user.
| 7. The method of claim 1, wherein predicting the route of the first user includes analyzing the current location of the first user, current traffic data, construction data, historical routes to the destination taken by the first user, and map data.
| 8. The method of claim 1, wherein reprioritizing the queue priority database causes: (i) an in-store pickup order associated with the second user to be available for pickup when the second user arrives at the destination, and before an in-store pickup order associated with the first user is available for pickup, or (ii) a delivery vehicle operated by the first user to be assigned to an available unloading location at the destination, when the first user arrives at the destination.
| 9. The method of claim 1, wherein the first user and the second user is a customer, a delivery truck driver, an autonomous vehicle, or an unmanned drone.
| 10. A computer system, comprising:
a processor;
a memory device coupled to the processor; and
a computer-readable storage device coupled to the processor, wherein the storage device contains program code executable by the processor via the memory device to implement a method for predicting a realistic time of arrival for performing a queue priority adjustment, the method comprising: determining, by a processor of a computing system, an estimated initial arrival time of a first user and a second user to a destination, the estimated initial arrival time being used to establish a queue priority of the first user and a queue priority of the second user, in a queue priority database, the queue priority of the first user being higher than the queue priority of the second user;
tracking, by the processor, a current location of the first user and a current location of the second user, during transit to the destination;
predicting, by the processor, a route to be taken to arrive at the destination from the current location of the first user and the current location of the second user, respectively;
detecting, by the processor, a schedule-altering event of the first user by
analyzing: (i) the predicted route of the first user, or (ii) a current state of a vehicle; and
reprioritizing, by the processor, the queue priority database, in response to
calculating an updated queue priority of the first user that is lower than the queue priority of the second user, based on the detection of the schedule- altering event of the first user.
| 11. The computer system of claim 10, wherein determining the estimated initial arrival time of the first user and the second user includes:
receiving, by the processor, a customer pick-up order and current GPS location information of the first user and the second user, the GPS location information obtained from a mobile device of the first user and the second user;
reviewing, by the processor, historical user data, including a historical path taken by the first user and the second user to the destination;
evaluating, by the processor, a complexity of the customer pick-up order to determine an earliest store pick-up time; and
comparing, by the processor, the earliest store pick-up time, the current GPS location of the first user and the second user, and the historical path taken by the first user and the second user to the destination, to determine the estimated initial arrival time, in response to: (i) prompting the first user and the second user to depart for the destination, or (ii) receiving confirmation from the first user and the second user that the first user and the second user have departed for the destination.
| 12. The computer system of claim 1 1, wherein prompting the first user and the second user to depart for the destination includes providing, by the processor, a suggested departure time based on at least one of: the earliest store pick-up time, current traffic conditions, current location of the first user and the second user, and historical traffic patterns of the first user and the second user.
| 13. The computer system of claim 10, wherein determining the estimated initial arrival time includes: receiving, by the processor, a scheduled delivery information and a current GPS location information of the first user and the second user;
reviewing, by the processor, a historical delivery pattern data, based on previous deliveries to the destination;
evaluating, by the processor, the scheduled delivery information, the current GPS location information of the first user and the second user, and the historical delivery pattern data, to determine an earliest delivery arrival time.
| 14. The computer system of claim 10, wherein the detecting of the schedule-altering event includes receiving data from a plurality of data sources, the plurality of data sources including a current GPS location of the user received from a mobile device of the user, a real-time traffic data received from the mobile device of the user, a real-time traffic data received from a third party application server, a weather data received from the mobile device of the user, a weather data retrieved from a third party application server, a historical traffic pattern information of the user, a sensor data received from one or more sensors associated with the user, a vehicle and traffic information received from a vehicle-to- vehicle communication network, and a combination thereof.
| 15. The computer system of claim 10, wherein the schedule -altering event is at least one of: a delay, a traffic jam, a traffic accident, a vehicle failure, a weather occurrence, an intervening stop by the first user, a wrong turn of the user, an alternative route taken by the user, a predicted traffic delay of the first user, and a predicted weather delay of the first user.
| 16. The computer system of claim 10, wherein predicting the route of the first user includes analyzing the current location of the first user, current traffic data, construction data, historical routes to the destination taken by the first user, and map data.
| 17. The computer system of claim 10, wherein reprioritizing the queue priority database causes: (i) an in-store pickup order associated with the second user to be available for pickup when the second user arrives at the destination, and before an in-store pickup order associated with the first user is available for pickup, or (ii) a delivery vehicle operated by the first user to be assigned to an available unloading location at the destination, when the first user arrives at the destination.
| 18. The computer system of claim 10, wherein the first user and the second user is a customer, a delivery truck driver, an autonomous vehicle, or an unmanned drone.
| 19. A computer program product, comprising a computer-readable hardware storage device storing a computer-readable program code, the computer-readable program code comprising an algorithm that when executed by a computer processor of a computing system implements a method for predicting a realistic time of arrival for performing a queue priority adjustment, the method comprising:
determining, by a processor of a computing system, an estimated initial arrival time of a first user and a second user to a destination, the estimated initial arrival time being used to establish a queue priority of the first user and a queue priority of the second user, in a queue priority database, the queue priority of the first user being higher than the queue priority of the second user;
tracking, by the processor, a current location of the first user and a current location of the second user, during transit to the destination;
predicting, by the processor, a route to be taken to arrive at the destination from the current location of the first user and the current location of the second user, respectively; detecting, by the processor, a schedule-altering event of the first user by analyzing: (i) the predicted route of the first user, or (ii) a current state of a vehicle; and
reprioritizing, by the processor, the queue priority database, in response to calculating an updated queue priority of the first user that is lower than the queue priority of the second user, based on the detection of the schedule-altering event of the first user.
| 20. The computer program product of claim 19, wherein determining the estimated initial arrival time of the first user and the second user includes:
receiving, by the processor, a customer pick-up order and a current GPS location information of the first user and the second user, the GPS location information obtained from a mobile device of the first user and the second user;
reviewing, by the processor, historical user data, including a historical path taken by the first user and the second user to the destination;
evaluating, by the processor, a complexity of the customer pick-up order to determine an earliest store pick-up time; and
comparing, by the processor, the earliest store pick-up time, the current GPS location of the first user and the second user, and the historical path taken by the first user and the second user to the destination, to determine the estimated initial arrival time, in response to: (i) prompting the first user and the second user to depart for the destination, or (ii) receiving confirmation from the first user and the second user that the first user and the second user have departed for the destination.
| 21. The computer program product of claim 20, wherein prompting the first user and the second user to depart for the destination includes providing, by the processor, a suggested departure time based on at least one of: the earliest store pick-up time, current traffic conditions, current location of the first user and the second user, and historical traffic patterns of the first user and the second user.
| 22. The computer program product of claim 19, wherein determining the estimated initial arrival time includes:
receiving, by the processor, a scheduled delivery information and current GPS
location information of the first user and the second user;
reviewing, by the processor, a historical delivery pattern data, based on previous deliveries to the destination; and evaluating, by the processor, the scheduled delivery information, the current GPS location information of the first user and the second user, and the historical delivery pattern data, to determine an earliest delivery arrival time.
| 23. The computer program product of claim 19, wherein the detecting of the schedule -altering event includes receiving data from a plurality of data sources, the plurality of data sources including a current GPS location of the user received from a mobile device of the user, a real-time traffic data received from the mobile device of the user, a real-time traffic data received from a third party application server, a weather data received from the mobile device of the user, a weather data retrieved from a third party application server, a historical traffic pattern information of the user, a sensor data received from one or more sensors associated with the user, a vehicle and traffic information received from a vehicle-to-vehicle communication network, and a combination thereof.
| 24. The computer program product of claim 19, wherein the schedule -altering event is at least one of: a delay, a traffic jam, a traffic accident, a vehicle failure, a weather occurrence, an intervening stop by the first user, a wrong turn of the user, an alternative route taken by the user, a predicted traffic delay of the first user, and a predicted weather delay of the first user.
| 25. The computer program product of claim 19, wherein predicting the route of the first user includes analyzing the current location of the first user, current traffic data, construction data, historical routes to the destination taken by the first user, and map data.
| 26. The computer program product of claim 19, wherein reprioritizing the queue priority database causes: (i) an in-store pickup order associated with the second user to be available for pickup when the second user arrives at the destination, and before an in-store pickup order associated with the first user is available for pickup, or (ii) a delivery vehicle operated by the first user to be assigned to an available unloading location at the destination, when the first user arrives at the destination.
| 27. The computer program product of claim 19, wherein the first user and the second user is a customer, a delivery truck driver, an autonomous vehicle, or an unmanned drone . | The method involves tracking a current location of a first user and a current location of a second user during transit to a destination by a processor (141). A route to be taken to arrive at the destination from the current location of the first user and the second user is predicted by the processor. A schedule-altering event of the first user is detected by the processor by analyzing the predicted route of the first user, or a current state of a vehicle. A queue priority database (114) is reprioritized by the processor in response to calculating an updated queue priority of the first user that is lower than queue priority of the second user based on the detection of the schedule-altering event of the first user. INDEPENDENT CLAIMS are also included for the following:a computer systema computer program product comprising a set of instructions for predicting a realistic time of arrival for performing a queue priority adjustment for a user at a retail location by a computer system. Method for predicting a realistic time of arrival for performing a queue priority adjustment for a user e.g. customer, delivery lorry driver, autonomous vehicle or unmanned drone, at a retail location by a computer system (all claimed). The method enables allowing goods to be retrieved in response to the predicted time of arrival based on user input information on a purchase order or to be automatically generated based on complexity of the purchase order. The method enables allowing the customers to pick up purchased items at the retail location selected by the customer by maintaining the queue priority such that the purchased items are ready for the customer when the customer arrives at the retail location. The drawing shows a schematic block diagram of a queue prioritization system. 113Customer database114Queue priority database120Computer system141Processor142Memory |
Please summarize the input | Method for controlling an autonomously operated vehicle and error control moduleThe invention relates to a method for controlling an autonomously operated vehicle (1) in the event of an error (Fm; m = 1,2, ... N0), with at least the following steps:
- Detection of a fault (Fm) in the vehicle (1);
- Evaluating the detected error (Fm) and, as a function thereof, outputting an error assessment result (EF), the error assessment result (EF) indicating a significance of the detected error (Fm);
- Determination of a local environment (Uk; k = 1,2, ..., N3) in which the vehicle (1) is located;
- Selecting and activating an emergency operating mode as a function of the determined local environment (Uk) as well as the output evaluation result (EF) and / or the significance of the detected error (Fm); and
- Autonomous control of the vehicle (1) as a function of the selected and activated emergency operating mode using at least one movement system (100; 101, 102, 103) and / or environment detection system (300; 301, 302, 303, 304, 305) in the vehicle (1).|1. Method for controlling an autonomously operated vehicle (1) in the event of an error (Fm; m = 1,2, ... N0), with at least the following steps:
- Detection of a fault (Fm) in the vehicle (1) (ST1);
- Assessment of the detected error (Fm) (ST2) and, as a function thereof, output of an error evaluation result (EF) (ST3), the error evaluation result (EF) having a significance (Bi; i = 1,2 ... N1) of the detected fault (Fm);
- Determination of a local environment (Uk; k = 1,2, ..., N3) in which the vehicle (1) is located (ST4);
- Selecting and activating an emergency operating mode (NBnk; n = 1,2, ..., N2) depending on the determined local environment (Uk) and the output evaluation result (EF) and / or the significance (Bi) of the detected error (Fm) (ST5); and
- Autonomous control of the vehicle (1) as a function of the selected and activated emergency operating mode (NBnk) using at least one movement system (100; 101, 102, 103) and / or environment detection system (300; 301, 302, 303, 304, 305 ) in the vehicle (1) (ST6).
| 2. Procedure according to Claim 1, characterizedthat the error (Fm) from in the vehicle (1) via a vehicle-internal data transmission system (30), for example a CAN bus (31), or from directly transmitted status signals (So, o = 1, 2, ..., N4) is derived.
| 3. Procedure according to Claim 2, characterizedthat the status signals (So) from the at least one movement system (100; 101, 102, 103) of the vehicle (1) and / or environment detection system (300; 301, 302, 303, 304, 305) of the vehicle (1) and / or from further sources (3) of the vehicle (1), for example a V2X module (3), the status signals (So) indicating whether the respective movement system (100; 101, 102, 103) and / or the environment detection system (300; 301, 302, 303, 304, 305) has an error (Fm).
| 4. Method according to one of the preceding claims, characterizedthat as an error (Fm) at least
- an elementary, fatal error (F1) or
- a moderate error (F2) or
- a non-safety-critical error (F3) can be detected.
| 5. Method according to one of the preceding claims, characterizedthat the error evaluation result (EF) output is at least that there is an error (Fm) with high importance (B1) or medium importance (B2) or low importance (B3).
| 6. Procedure according to Claim 4 or 5, characterizedthat if present
- A fault (Fm) with high significance (B1) and / or an elementary, serious fault (F1) depending on the local environment (Uk) a first emergency operating mode (NB1k) is activated to use the vehicle (1) of the at least one movement system (100; 101, 102, 103) and / or environment detection system (300; 301, 302, 303, 304, 305) in the vehicle (1) to be brought to a standstill (H) autonomously, or
- an error (Fm) with medium significance (B2) and / or a moderately serious error (F2) depending on the local environment, a second emergency operating mode (NB2k) is activated in order to move the vehicle (1) using the at least one movement system ( 100; 101, 102, 103) and / or environment detection system (300; 301, 302, 303, 304, 305) in the vehicle (1) to move autonomously to a stopping area (HB), or
- an error (Fm) of little importance (B3) and / or a non-safety-critical error (F3) depending on the local environment (Uk) a third emergency operating mode (NB3k) is activated in order to stop the autonomous driving of the vehicle (1) To continue using the at least one movement system (100; 101, 102, 103) and / or environment detection system (300; 301, 302, 303, 304, 305) in the vehicle (1).
| 7. Procedure according to Claim 6, characterizedthat in the first emergency operating mode (NB1k) at least one brake system (101) and / or a drive system (103) for reducing the engine power (ML) is controlled autonomously in order to bring the vehicle (1) to a standstill (H), and as a function of the local environment (Uk), a steering system (103) is still controlled autonomously in order to enable an avoidance to a secured area (BS), for example an emergency lane (BS1), before the standstill (H) is reached.
| 8. Procedure according to Claim 6 or 7, characterizedthat in the second emergency operating mode (NB2k) a drive system (102) and / or a braking system (101) and / or a steering system (103) of the vehicle (1) are controlled autonomously in such a way that the vehicle (1) moves with reduced Speed ??(vred) and / or with reduced engine power (ML) moves autonomously along a defined driving trajectory (T) to the stopping area (HB).
| 9. Procedure according to Claim 8, characterizedthat the neck area (HB) and / or the reduced speed (vred) and / or the motor power (ML) is selected as a function of the local environment (Uk).
| 10. Method according to one of the Claims 6 to 9, characterizedthat the first emergency operating mode (NB1k) is fixed and unchangeable and / or the second emergency operating mode (NB2k) can be expanded and / or the third and / or further emergency operating modes (NBnk, for n> = 3) changed and / or can be expanded.
| 11. Method according to one of the preceding claims, characterizedthat a motorway (U1), a country road (U2), an urban environment (U3), a depot (U4), a construction site (U5) or a port area (U6) are determined as the local environment (Uk).
| 12. Method according to one of the preceding claims, characterizedthat the local environment (Uk) is an environment with a public traffic area (?) or an environment with an enclosed area (G).
| 13. Method according to one of the preceding claims, characterizedthat the local environment (Uk) is determined as a function of position information (PI) and / or environment information (UI), the position information (PI) being based on automatically provided position data (DPa) and / or manually entered position data (DPm) and the environment information (UI) is based on provided environment data (DU) that are output, for example, by environment detection systems (300; 301, 302, 303, 304, 305) in the vehicle (1) become.
| 14. Procedure according to Claim 13, characterizedthat the automatically provided position data (DPa) are output by a position detection device (70) and contain a global position (Pg) of the vehicle (1) and / or are output by a telematics system (400), the telematics system ( 400) accesses external information (IX) which is transmitted via a local data interface (8).
| 15. Procedure according to Claim 13 or 14, characterizedthat the local environment (Uk) is extracted from the automatically provided position data (DPa) via map data (KD), in particular a navigation system (7).
| 16. Method according to one of the preceding claims, characterizedthat the autonomously and / or driverlessly controlled vehicle (1) is controlled according to an autonomy level (AS) equal to three or higher.
| 17. Method according to one of the preceding claims, characterizedthat the error (Fm) and / or the error evaluation result (EF) after the detection (ST1) and the evaluation (ST2, ST3) of the error (Fm) via a communication module (50) and / or a V2X module ( 3) is issued, for example to a vehicle operator (K1), a dispatcher (K2), to yard staff (K3) of a depot (U4) and / or to other people (K4) and / or to another vehicle (2) and / or to infrastructure facilities (200).
| 18. Error control module (60) for autonomous control of a vehicle (1) in the event of an error (Fm), in particular according to a method according to one of the preceding claims, the error control module (60) being designed
- An emergency operating mode (NBnk; n = 1,2 .. N2) as a function of a local environment (Uk) determined from position information (PI) and / or environment information (UI), in which the vehicle (1 ) and to select and activate an evaluation result (EF) output by an error evaluation module (40) and / or a significance (Bi) of an error (Fm) detected by an error detection module (20), and
- The vehicle (1) autonomously depending on the selected and activated emergency operating mode (NBnk) using at least one movement system (100; 101, 102, 103) and / or environment detection system (300; 301, 302, 303, 304, 305) to control in the vehicle (1).
| 19. Vehicle (1) with a movement coordination module (10) for coordinating and controlling movement systems (100; 101, 102, 103) and / or surrounding systems (300; 301, 302, 303, 304, 305) in the vehicle (1) for autonomous control of the vehicle (1), an error detection module (20) for detecting an error (Fm) in the vehicle (1), an error evaluation module (40) for evaluating the detected error (Fm) and to output an evaluation result (EF) and with an error control module (60) Claim 18 for autonomous control of the vehicle (1), in particular via the movement coordination module (10), in the event of an error (Fm). | The method involves detecting a fault (Fm) in vehicle (1). The assessment of the detected fault is performed. An error evaluation result (EF) is outputted, where the error evaluation result has a significance of detected fault. A local environment in which the vehicle is located is determined. An emergency operating mode is selected and activated as a function of a determined local environment and the evaluation result and/or the significance of the detected fault. The autonomous control of the vehicle is performed as a function of the selected and activated emergency operating mode using one movement system (100-103) and/or environment detection system (300-304) in the vehicle. INDEPENDENT CLAIMS are included for the following:an error control module for autonomous control of a vehicle in the presence of an error; anda vehicle with a movement coordination module for coordinating and controlling movement systems and/or surrounding systems in the vehicle. Method for controlling autonomously operated vehicle e.g. truck in event of fault, using error control module (claimed). The autonomous driving operation is ensured safely and efficiently in the event of a fault. The vehicle is autonomously controlled as a function of the local environment when the error occurs, where the error that can have an effect on the own vehicle and/or on an environment around the own vehicle taxes to a certain extent. The autonomous vehicles are operated in a single local environment, and in different local environments. The efficiency and the possibility of reacting to error can turn out to be different depending on the local environment, so that the different emergency operating modes are selected or activated depending on the environment. The warning is effectively issued to external persons or vehicles in addition to the autonomous control, when the vehicle is driving without any occupants. The drawing shows a schematic view of the autonomously operated vehicle in local environment with public traffic area. 1Vehicle100-103Movement system300-304Environment detection systemEFError evaluation resultFmFault |
Please summarize the input | METHOD FOR TRANSFORMING BETWEEN A LONG VEHICLE COMBINATION AND A PLATOON ON THE MOVEThe invention relates to a method for transforming between a long vehicle combination (10) and a platoon (12) on the move. The present invention also relates to vehicles (14a-b; 14b-c) for such a method.|1. A method for transforming between a long vehicle combination (10) and a platoon (12) on the move, wherein the long vehicle combination (10) comprises a plurality of vehicles (14a-c) mechanically coupled together one after the other, which method comprises the steps of:
* detecting (S2) that the long vehicle combination (10) is approaching a first road section (58) ahead, by means of a navigation system (22) or by means of wireless vehicle-to-infrastructure (V2I) communication, which first road section (58) stipulates decoupling the vehicles (14a-c) of the long vehicle combination (10) to form the platoon (12);
* automatically decoupling (S4) the vehicles (14a-c) from each other while the vehicles (14a-c) are in motion to form the platoon (12) before reaching the first road section (58);
* the platoon (12) driving (S5) through the first road section (58);
* detecting (S6) a second road section (62), by means of a navigation system (22) or by means of wireless vehicle-to-infrastructure (V2I) communication, which stipulates coupling together the vehicles (14a-c) of the platoon (12) to form the long vehicle combination (10);
* a vehicle (14a-b) in the platoon (12) immediately ahead of a following vehicle (14b-c) of said the platoon (12) sending (S7) information (64) to the following vehicle (14b-c) via wireless vehicle-to-vehicle communication, which information (64) indicates the position and speed of a rear automatic coupling device (18) of the vehicle (14a-b) immediately ahead;
* based at least on the position and speed indicated in the sent information (64), autonomously driving (S8) the following vehicle (14b-c) so that the rear automatic coupling device (18) of the vehicle (14a-b) immediately ahead of the following vehicle (14b-c) gets within an operational range (66) of a front coupling element (32) of the following vehicle (14b-c);
* while in motion and when the rear automatic coupling device (18) is within the operational range (66), the following vehicle (14b-c) automatically adjusting (S9) a front coupling device (30) including said front coupling element (32) so that the position of the front coupling element (32) matches the position of the rear automatic coupling device (18) as indicated in the sent information (64); and
* automatically coupling (S10) together the following vehicle (14b-c) and the vehicle (14a-b) immediately ahead while the vehicles (14a-c) are in motion to form at least a part of the long vehicle combination (10),
wherein each vehicle (14a-b) immediately ahead is adapted to estimate the position of its rear automatic coupling device (18) based on
* the heading of the vehicle (14a-b) immediately ahead,
* the position of a part of the vehicle (14a-b) immediately ahead as determined by a navigation system (22) of the vehicle immediately ahead,
* a vehicle model representing the vehicle (14a-b) immediately ahead,
* the height of the rear automatic coupling device (18), and
* in case the vehicle (14a-b) immediately ahead is an articulated vehicle, at least one articulation angle of the vehicle immediately ahead as detected by at least one articulation angle detection means (28) on the vehicle immediately ahead.
| 2. A method according to claim 1, wherein each following vehicle (14b-c) comprises actuator means (48a-b) adapted to adjust the front coupling device (30).
| 3. A method according to claim 2, wherein the actuator means (48a-b) is adapted to laterally adjust the front coupling device (30).
| 4. A method according to claim 2 or 3, wherein the actuator means (48a-b) is adapted to vertically adjust the front coupling device (30).
| 5. A method according to any preceding claim, wherein each following vehicle (14b-c) comprises means (54) adapted to adjust the length of the front coupling device (30).
| 6. A method according to claim 5, further comprising the step of: shortening (S1) the length of the front coupling device (30) while driving as the long vehicle combination.
| 7. A method according to any preceding claim, wherein each following vehicle (14b-c) is adapted to estimate the position of its front coupling element (32) based on
* the heading of the following vehicle (14b-c),
* the position of a part of the following vehicle (14b-c) as determined by a navigation system (36) of the following vehicle (14b-c),
* a vehicle model representing the following vehicle (14b-c),
* a first angle representing a lateral adjustment of the front coupling device (30),
* a second angle representing any vertical adjustment of the front coupling device (30),
* the length of the front coupling device (30), and
* a height related to the front coupling device (30).
| 8. A method according to any preceding claim, wherein each vehicle immediately ahead (14a-b) comprises at least two independent means (21, 22) for determining its speed.
| 9. A method according to any preceding claim, further comprising the step of: a leading vehicle of the platoon sending an acceleration or deceleration request (63) to the following vehicles (14b-c) of the platoon (12) via wireless vehicle-to-vehicle communication.
| 10. A method according to any preceding claim, wherein the information (64) sent from the vehicle (14a-b) immediately ahead to the following vehicle (14b-c) includes the heading of the rear automatic coupling device (18) of the vehicle (14a-b) immediately ahead.
| 11. A method according to any preceding claim, wherein the first road section (58) is at least one of a bridge, a roundabout, and a turn.
| 12. A method according to any preceding claim, further comprising the step of planning (S3) an inter-vehicle distance (60) between subsequent vehicles based on the first road section (58) ahead, wherein the platoon (12) is driven through the first road section (58) with the planned inter-vehicle distance(s) (60).
| 13. A method according to any preceding claim, wherein at least one of the automatic decoupling and the automatic coupling is performed while driving at a safety speed.
| 14. A method according to any preceding claim, wherein the automatic coupling is performed while driving on a straight road.
| 15. A method according to any preceding claim, wherein the automatic coupling starts with the vehicle (14b) immediately behind the leading vehicle (14a) of the platoon (12) coupling to the leading vehicle (14a) of the platoon (12).
| 16. A method according to any preceding claim, wherein the automatic decoupling starts with the last vehicle (14c) of the long vehicle combination (10) decoupling from the vehicle immediately ahead (14b).
| 17. A method according to any preceding claim, wherein each vehicle (14b-c) after the leading vehicle (14a) of the long vehicle combination (10) or platoon (12) is an autonomous vehicle.
| 18. A method according to any preceding claim, wherein at least one vehicle (14b-c) after the leading vehicle (14a) of the long vehicle combination (10) or platoon (12) is an autonomous dolly (16) and semi-trailer combination.
| 19. A vehicle (14a-b) comprising:
* a rear automatic coupling device (18); means (21) for speed determination;
* a control unit (20) adapted to estimate the position of the rear automatic coupling device (18) while the vehicle (14a-b) is in motion based on the heading of the vehicle (14a-b), the position of a part of the vehicle (14a-b) as determined by a navigation system (22) of the vehicle, a vehicle model representing the vehicle (14a-b), the height of the rear automatic coupling device (18) as determined by a height level sensor (24), and in case the vehicle (14a-b) is an articulated vehicle, at least one articulation angle of the vehicle as detected by at least one articulation angle detection means (28) on the vehicle; and
* communication means (26) adapted to wirelessly send information (64) indicating the estimated position and the speed of the rear automatic coupling device (18) to a following vehicle (14b-c).
| 20. A vehicle (14b-c) comprising:
* a front coupling device (30) including a front coupling element (32);
* a control unit (34) adapted to estimate the position of the front coupling element (32) while the vehicle (14b-c) is in motion;
* a navigation system (36) and a height level sensor (38);
* communication means (40) adapted to wirelessly receive information (64) from a vehicle (14a-b) immediately ahead, which information (64) indicates the position and speed of a rear automatic coupling device (18) of the vehicle (14a-b) immediately ahead;
* autonomous driving means (42) adapted to drive the vehicle (14b-c) based at least on the position and speed in the received information (64) so that the rear automatic coupling device (18) of the vehicle (14a-b) immediately ahead gets within an operational range (66) of the front coupling element (32); and
* means (48a-b, 54) adapted to automatically adjust the front coupling device (30), while in motion and when the rear automatic coupling device (18) is within the operational range (66), so that the position of the front coupling element (32) matches the position of the rear automatic coupling device (18) as indicated in the received information (64). | The method involves detecting (S6) second road section which stipulates coupling together the vehicles of the platoon to form the long vehicle combination. The information is sent (S7) to the following vehicle through wireless vehicle-to-vehicle communication. The information indicates the position and speed of rear automatic coupling device of the vehicle immediately ahead. The following vehicle is automatically driven (S8) so that the rear automatic coupling device of the vehicle immediately ahead of the following vehicle gets within an operational range of front coupling element of the following vehicle. The front coupling device is automatically adjusted (S9) so that the position of the front coupling element matches the position of the rear automatic coupling device as indicated in the sent information. The following vehicle and the vehicle immediately ahead are automatically coupled (S10) together while the vehicles are in motion to form portion of the long vehicle combination. An INDEPENDENT CLAIM is included for a vehicle. Method for transforming between long vehicle combination and platoon on move, for heavy duty vehicle e.g. truck. The following vehicle can receive the correct speed allowing to safely drive so that the rear automatic coupling device of the vehicle immediately ahead gets within the operational range, even if one of the systems fails. The acceleration or deceleration request sent through wireless vehicle-to-vehicle communication can allow following vehicle to safely drive within the operational range, even if the operational range results in relatively short headway between the following vehicle and the vehicle immediately ahead and even if the speed is relatively high. The long vehicle combination can be automatically re-formed in motion after the roundabout or turn, to improve fuel efficiency. The front coupling device is automatically adjusted so that the position of the front coupling element matches the position of the rear automatic coupling device as indicated in the sent information. The drawing shows a flowchart illustrating the method for transforming between long vehicle combination and platoon on move. S6Step for detecting second road section which stipulates coupling together the vehicles of the platoon to form the long vehicle combinationS7Step for sending information to the following vehicle through wireless vehicle-to-vehicle communicationS8Step for automatically driving following vehicle so that the rear automatic coupling device of the vehicle immediately ahead of the following vehicle gets within an operational range of front coupling element of the following vehicleS9Step for automatically adjusting front coupling device so that the position of the front coupling element matches the position of the rear automatic coupling device as indicated in the sent informationS10Step for automatically coupling together the following vehicle and the vehicle immediately ahead while the vehicles are in motion to form portion of the long vehicle combination |
Please summarize the input | A METHOD FOR PROVIDING A POSITIVE DECISION SIGNAL FOR A VEHICLEA method for providing a positive decision signal for a vehicle which is about to perform a traffic scenario action. The method includes receiving information about at least one surrounding road user, which information is indicative of distance to the surrounding road user with respect to the vehicle and at least one of speed and acceleration of the surrounding road user; calculating a value based on the received information; providing the positive decision signal to perform the traffic scenario action when the calculated value is fulfilling a predetermined condition. The value is calculated based on an assumption that the surrounding road user will react on the traffic scenario action by changing its acceleration.|1. A method for providing a positive decision signal for a vehicle which is about to perform a traffic scenario action, such as entering a crossing, entering a highway and/or changing lanes, the method comprising:
receiving information about at least one surrounding road user, which information is indicative of distance to the surrounding road user with respect to the vehicle and at least one of speed and acceleration of the surrounding road user;
calculating a value based on the received information;
providing the positive decision signal to perform the traffic scenario action when the calculated value is fulfilling a predetermined condition, wherein the value is calculated based on an assumption that the surrounding road user will react on the traffic scenario action by changing its acceleration, characterized in that, the surrounding road user is a predefined virtual surrounding road user and the predetermined condition is defined by a threshold value which is indicative of an acceleration limit for the surrounding road user.
| 2. The method according to claim 1, wherein the value is further calculated based on the assumption that the surrounding road user will react on the traffic scenario action by changing its acceleration after a reaction time.
| 3. The method according to claim 1, wherein the value is further calculated based on the assumption that the surrounding road user will react on the traffic scenario action by changing its acceleration to an acceleration profile having a constant acceleration.
| 4. The method according to claim 1, wherein the value is further calculated based on the assumption that the surrounding road user will react on the traffic scenario action by changing its acceleration to an acceleration profile having a variable acceleration.
| 5. The method according to claim 1, further comprising providing a negative decision signal not to perform the traffic scenario action when the calculated value is not fulfilling the predetermined condition.
| 6. The method according to claim 1, wherein the threshold value is variable depending on at least one factor, such as any one of speed of the surrounding road user, type of surrounding road user, ambient weather conditions with respect to the vehicle and a state of the surrounding road user, such as a state where a turning indicator is active.
| 7. The method according to claim 1, wherein the information about the at least one surrounding road user is received by any one of a perception sensor of the vehicle, a V2X communication interface and a remote perception sensor which is in communicative contact with the vehicle.
| 8. The method according to claim 1, wherein the method is used as a safety control method for an autonomous vehicle, wherein the autonomous vehicle is primarily performing traffic scenario actions by use of a primary autonomous vehicle control method, and wherein a traffic scenario action permitted to be performed by the primary autonomous vehicle control method is allowed to be performed if also the positive decision signal is provided.
| 9. The method according to claim 1, wherein the calculated value is further based on auxiliary information relating to the traffic scenario action, such as any one of shape and/or dimension(s) of a crossing, a road lane and a neighboring road lane.
| 10. A method for automatically performing a traffic scenario action of a vehicle, comprising:
providing a positive decision signal to perform the traffic scenario action, which positive decision signal has been provided according to the method of claim 1; and
automatically performing the traffic scenario action.
| 11. A method for automatically avoiding performing a traffic scenario action of a vehicle, comprising:
providing a negative decision signal not to perform the traffic scenario action, which negative decision signal has been provided according to the method of claim 5; and
automatically avoiding performing the traffic scenario action.
| 12. A control unit for a vehicle which is configured to perform the steps of claim 1.
| 13. A vehicle comprising the control unit according to claim 12.
| 14. The vehicle according to claim 13, wherein the vehicle is a fully autonomous or semiautonomous vehicle.
| 15. The vehicle according to claim 13, wherein the vehicle is a road vehicle, such as a public road vehicle, for example a truck, a bus and a construction equipment vehicle adapted to be driven on a road.
| 16. The vehicle according to claim 13, wherein the vehicle is a heavy-duty vehicle which has a minimum weight of at least 5000 kg, such as 30.000 kg.
| 17. A computer program comprising program code means for performing the steps of claim 1, when said program is run on a computer.
| 18. A computer readable medium carrying a computer program comprising program code means for performing the steps of claim 1, when said program product is run on a computer. | The method involves receiving information about the surrounding road user (2), which information is indicative of distance to the surrounding road user with respect to the vehicle and the speed and acceleration of the surrounding road user. A value is calculated based on the received information. The positive decision signal is provided to perform the traffic scenario action, when the calculated value is fulfilled a predetermined condition. The value is calculated based on an assumption that the surrounding road user is reacted on the traffic scenario action by changing the acceleration of the vehicle. INDEPENDENT CLAIMS are included for the following:a vehicle;a computer program for providing positive decision signal for vehicle; anda computer readable medium carrying computer program for providing positive decision signal for vehicle. Method for providing positive decision signal for vehicle (claimed). The surrounding road user increases and reduces the speed when the vehicle initiates lane change to the nearby lane to avoid the risk of collision. The improved and cost-efficient redundancy for the autonomous vehicle is implied. The drawing shows a schematic view of the traffic scenario. 1Heavy-duty truck2Surrounding road user |
Please summarize the input | method for the gap between vehicle control teamThe invention claims a vehicle gap (24a-24c) between control vehicle in method (10), the train (10) comprises a preceding vehicle (12) and one or a plurality of following vehicle (14a-14c). the method comprises the following steps: indicating parameter (27) acquires the potential collision threat (26) recognized by leader vehicle autonomous emergency braking system (16), wherein leader of the autonomous vehicle emergency braking system comprises a plurality of predefined control period (28a-28c), and wherein the parameter for indicating the current control stage at least partially determining the autonomous emergency braking system, and obtained the indication parameter is transmitted to the one or more of the following vehicle.|1. A vehicle gap (24a-24c) between a control vehicle in method (10), the vehicle (10) comprises a preceding vehicle (12) and one or a plurality of following vehicle (14a-14c), wherein, the feature of the method lies in the following steps: obtaining by the leader vehicle autonomous emergency braking system (16) identifies the potential collision threat indication parameter (27) (26), wherein the leading vehicle of the autonomous emergency braking system comprises a plurality of predefined control stage (28a-28c). and wherein the current control stage, the indication parameter at least partially determining the autonomous emergency braking system, and the obtained by the indication parameter is transmitted to the one or more of the following vehicle.
| 2. The method according to claim 1, further comprising the following step: in the one or more of the following vehicle receiving the indication parameter, and automatically adjusting the clearance between vehicle parameters based on the indication received.
| 3. The method according to claim 1 or 2, wherein the indicating parameter is collision time.
| 4. The method according to claim 2 or 3, wherein the step of adjusting the gap between the vehicle comprises automatically based on the indication parameter of the received: the one or more of the following vehicle following vehicle (14c) according to the position of the following vehicle in the motorcade from the avoidance time minus a predetermined time so as to generate the collision time (TTC14C), and the following vehicle based on the collision time is reduced to adjust the following vehicle and the preceding vehicle (14b) and gap (24c).
| 5. The method according to claim 2, wherein the step of automatically adjusting the clearance between the vehicle starts at the fleet of the last vehicle (14c) based on the received indicating parameter so as to increase the last vehicle (14c) and the gap between the preceding vehicle (14b) (24c).
| 6. The method according to claim 2, wherein the step of automatically adjusting the clearance between said vehicle is started before the complete braking phase of the leading vehicle in the autonomous emergency braking system (28c) based on the received indicating parameter.
| 7. The method according to claim 2, further comprising: the driver of the leading vehicle with respect to the vehicle of the last vehicle (14c) how to adjust the last vehicle (14c) and the preceding vehicle (14b) between the gap (24c).
| 8. The method according to claim 1, wherein, using a vehicle-to-vehicle communication device (18) to perform the indication parameter.
| 9. The method according to claim 2, wherein, using a vehicle-to-vehicle communication device (32a-32c) to perform reception of the indication parameter.
| 10. The method according to any one of the preceding claims, further comprising: a friction-based estimation value for determining the retarding capacity of the leader vehicle.
| 11. The method according to claim 2 and 10, wherein the step of automatically adjusting the clearance between the vehicle based on the received indication parameter comprises: also considers the reduction ability.
| 12. A vehicle gap (24a-24c) between a control vehicle in method (10), the vehicle (10) comprises a preceding vehicle (12) and one or more following vehicle (14a-14c), wherein the feature of the method lies in the following steps: indicating parameter (27) in the one or more of the following vehicle receiving the potential collision threat (26) identified by autonomous of the leader vehicle emergency brake system (16), wherein the leader vehicle of the autonomous emergency braking system comprises a plurality of predefined control period (28a-28c), and wherein the indication parameter the current control stage at least partially determining the autonomous emergency braking system, and automatically adjusting the clearance between vehicle parameters based on the indication received.
| 13. A computer program comprising program code, when said program is run on a computer, said program code to perform the method according to any one of claims 1-12 the step.
| 14. A computer readable medium, the computer readable medium carrying a computer program comprising program code, when said program product is run on a computer, said program code to perform the method according to any one of claims 1-12 the step.
| 15. A control unit (22, 34a-34c), said control unit is used for controlling the clearance between the vehicle in the fleet, the control unit is configured to perform the method according to any one of claims 1-12 the steps of the method.
| 16. A vehicle (12; 14a-14c), the vehicle (12; 14a-14c) is configured to perform the method according to any one of claim 1-12 the steps of the method. | The method involves obtaining an indicator (27) of potential collision threat (26) identified by an autonomous emergency braking system (16) of a lead vehicle, where the braking system of the lead vehicle comprises pre-defined control phases, and the indicator determines current control phase of the braking system. The obtained indicator is sent to following vehicles. The indicator is received in the following vehicles. Inter-vehicle gaps (24a-24c) are automatically adjusted based on the received indicator. INDEPENDENT CLAIMS are also included for the following:a computer program comprising a set of instructions for controlling inter-vehicle gaps in a platoona computer readable medium comprising a set of instructions for controlling inter-vehicle gaps in a platoona control unit for controlling inter-vehicle gaps in a platoona vehicle. Method for controlling inter-vehicle gaps between vehicles (claimed), e.g. lorries, buses and passenger cars, in a platoon. The method allows the lead vehicle to remain predictable for the following vehicles even if a slippery or low friction road reduces deceleration capacity and calls for earlier braking, and building buffer distance to mitigate effects of different braking capacity of the vehicles in the platoon. The method enables exploring possibilities to drive road vehicles in the platoons or road trains with small time gaps so as to save fuel and decrease driver workload and road footprint in an effective manner. The drawing shows a schematic view of a platoon. 10Platoon16Autonomous emergency braking system24a-24cInter-vehicle gaps26Potential collision threat27Indicator |
Please summarize the input | A METHOD FOR FORMING A VEHICLE COMBINATIONThe present disclosure relates to a method for selecting and identifying a powered dolly vehicle among a group of powered dolly vehicles in a geographical area for forming a vehicle combination with a primary vehicle and one or more trailers, the method being implemented by one or more processors of a wireless control system, each one of the powered dolly vehicles having an associated distinguishable identification information and an operational characteristic, the method comprising: receiving (S10) a request from the primary vehicle to select a powered dolly vehicle among the group of powered dolly vehicles based at least in part on a mission-characteristic for the vehicle combination; evaluating (S20) the operational characteristic of each one of the powered dolly vehicles based on said mission-characteristic; selecting (S30) a powered dolly vehicle among the group of powered dolly vehicles based at least in part on said evaluation; locating (S40) the selected powered dolly vehicle in the geographical area based at least in part on the identification information; and communicating (S50) the location of the selected powered dolly vehicle to the primary vehicle or operating the powered dolly vehicle to primary vehicle.|1. A method for selecting and identifying a powered dolly vehicle among a group of powered dolly vehicles in a geographical area for forming a vehicle combination with a primary vehicle and one or more trailers, the method being implemented by one or more processors of a wireless control system, each one of the powered dolly vehicles having an associated distinguishable identification information and an operational characteristic, the method comprising:
* - receiving (S10) a request from the primary vehicle to select a powered dolly vehicle among the group of powered dolly vehicles based at least in part on a mission-characteristic for the vehicle combination;
* - evaluating (S20) the operational characteristic of each one of the powered dolly vehicles based on said mission-characteristic;
* - selecting (S30) a powered dolly vehicle among the group of powered dolly vehicles based at least in part on said evaluation;
* - locating (S40) the selected powered dolly vehicle in the geographical area based at least in part on the identification information; and
* - communicating (S50) the location of the selected powered dolly vehicle to the primary vehicle or operating the powered dolly vehicle to the primary vehicle.
| 2. Method according to claim 1, further comprising selecting one or more trailers among a group of trailers based at least in part on the mission-characteristic for the vehicle combination and the selected powered dolly vehicle; communicating the location of the selected one or more trailers to the powered dolly vehicle, or to the primary vehicle; and operating the powered dolly vehicle to couple with the one or more trailers.
| 3. Method according to any one of the preceding claims, wherein the mission-characteristic of the vehicle combination comprises any one of an assignment instruction for the vehicle combination, a cargo space-requirement for the vehicle combination, a pick-up location of the cargo, a pick-up time for the cargo, a delivery time for the cargo, a delivery location of the cargo, and data indicating type of cargo.
| 4. Method according to any one of the preceding claims, wherein the mission-characteristic of the vehicle combination comprises data indicating type of primary vehicle.
| 5. Method according to any one of the preceding claims, further comprising receiving data relating to environmental conditions.
| 6. Method according to any one of the preceding claims, wherein the associated operational characteristic comprises data indicating any one of a brake capacity of the powered dolly vehicle, energy storage system capacity of the powered dolly vehicle, and state of charge of the energy storage system of the powered dolly vehicle.
| 7. Method according to any one of the preceding claims, wherein the associated operational characteristic comprises data indicating type of powered dolly vehicle.
| 8. Method according any one of the preceding claims, wherein the associated distinguishable identification information comprises an identification component configurable to be updated by the one or more processors of a wireless control system.
| 9. Method according to any one of the preceding claims, wherein the evaluating comprises determining if at least one operational characteristic of at least one of the powered dolly vehicles fulfils the mission-characteristic, or is at least sufficient for fulfilling the mission-characteristic.
| 10. Method according any one of the preceding claims, wherein the request is received at a remote-control source from the primary vehicle, the remote-control source comprising a transceiver for receiving the request from the autonomous vehicle.
| 11. Method according to claim 10, wherein the remote-control source comprises a memory configurable to contain and store the associated distinguishable identification information and operational characteristic of each one of the powered dolly vehicles.
| 12. Method according any one of the claims 10 to 11, comprising receiving the request from the primary vehicle at the remote-control source when the primary vehicle arrives at the geographical area.
| 13. Method according any one of the preceding claims, wherein the primary vehicle comprises a memory configurable to contain any one of the associated distinguishable identification information and operational characteristic of each one of the powered dolly vehicles.
| 14. Method according to any one of the preceding claims, comprising obtaining the operational characteristic directly from powered dolly vehicles.
| 15. Method according to any one of the preceding claims, comprising controlling any one of the primary vehicle and the selected powered dolly vehicle to couple to each other so as to form the vehicle combination.
| 16. A computer program comprising instructions, which when executed by one or more processors of a wireless control system, cause the one or more processors to perform operations comprising: receiving a request from the primary vehicle to select a powered dolly vehicle among the group of powered dolly vehicles based at least in part on a mission-characteristic for the vehicle combination; evaluating the operational characteristic of each one of the powered dolly vehicles based on said mission-characteristic; selecting a powered dolly vehicle among the group of powered dolly vehicles based at least in part on said evaluation; locating the selected powered dolly vehicle in the geographical area based at least in part on the identification information; and communicating the location of the selected powered dolly vehicle to the primary vehicle or operating the powered dolly vehicle to primary vehicle.
| 17. A non-transitory computer-readable medium comprising instructions, which when executed by one or more processors of a control system, cause the one or more processors to perform operations comprising: receiving a request from the primary vehicle to select a powered dolly vehicle among the group of powered dolly vehicles based at least in part on a mission-characteristic for the vehicle combination; evaluating the operational characteristic of each one of the powered dolly vehicles based on said mission-characteristic; selecting a powered dolly vehicle among the group of powered dolly vehicles based at least in part on said evaluation; locating the selected powered dolly vehicle in the geographical area based at least in part on the identification information; and communicating the location of the selected powered dolly vehicle to the primary vehicle or operating the powered dolly vehicle to primary vehicle.
| 18. A wireless control system for identifying and selecting a powered dolly vehicle among a group of powered dolly vehicles in a geographical area for forming a vehicle combination with a primary vehicle and one or more trailers, each one of the powered dolly vehicles having an associated distinguishable identification information and an operational characteristic, the system comprising a memory that stores a set of instructions and one or more processors which use the instructions from the set of instructions to:
* - receive a request from the primary vehicle to select a powered dolly vehicle among the group of powered dolly vehicles based at least in part on a mission-characteristic for the vehicle combination;
* - evaluate the operational characteristic of each one of the powered dolly vehicles based on said mission-characteristic;
* - select a powered dolly vehicle among the group of powered dolly vehicles based at least in part on said evaluation;
* - locate the selected powered dolly vehicle in the geographical area based at least in part on the identification information; and
* - communicate the location of the selected powered dolly vehicle to the primary vehicle or operating the powered dolly vehicle to primary vehicle.
| 19. Wireless control system according to claim 19, further comprising a communication interface operably coupled to the one or more processors for receiving instructions and for transmitting the location of the selected powered dolly vehicle to the primary vehicle.
| 20. A vehicle for forming a vehicle combination with a powered dolly vehicle and one or more trailers, comprising a memory that stores a mission-characteristic for the vehicle combination and one or more processors which use the mission-characteristic to:
* - select a powered dolly vehicle among a group of powered dolly vehicles based at least in part on the mission-characteristic for the vehicle combination, each one of the powered dolly vehicles having an associated distinguishable identification information and an operational characteristic;
* - evaluate the operational characteristic of each one of the powered dolly vehicles based on said mission-characteristic;
* - select a powered dolly vehicle among the group of powered dolly vehicles based at least in part on said evaluation;
* - locate the selected powered dolly vehicle in the geographical area based at least in part on the identification information; and
* - communicate the location of the selected powered dolly vehicle to the primary vehicle or operating the powered dolly vehicle to vehicle. | The method involves receiving (S10) a request from the primary vehicle to select a powered dolly vehicle among the group of powered dolly vehicles based on a mission-characteristic for the vehicle combination. The operational characteristic of each one of the powered dolly vehicles evaluated (S20) based on mission-characteristic. A powered dolly vehicle selected (S30) among the group of powered dolly vehicles based on evaluation. The selected powered dolly vehicle located (S40) in the geographical area based on the identification information. The location of the selected powered dolly vehicle is communicated (S50) to the primary vehicle or operating the powered dolly vehicle to the primary vehicle. INDEPENDENT CLAIMS are included for the following:a computer program for selecting and identifying powered dolly vehicle;a non-transitory computer-readable medium storing program for selecting and identifying powered dolly vehicle;a wireless control system for identifying and selecting a powered dolly vehicle among a group of powered dolly vehicles in a geographical area for forming a vehicle combination with a primary vehicle and one or more trailers; anda vehicle for forming a vehicle combination with a powered dolly vehicle and one or more trailers. Method for selecting and identifying powered dolly vehicle such as electric-powered dolly, steerable dolly vehicles. The method enables providing more efficient transportation vehicle systems that are fully, or partially, autonomous, thus increasing operational capacity of heavy-duty vehicles by vehicle combinations with multiple vehicle units in form of trailer units. The drawing shows a flowchart illustrating method for selecting and identifying powered dolly vehicle. S10Step for receiving a request from the primary vehicle to select a powered dolly vehicleS20Step for evaluating the operational characteristic of each one of the powered dolly vehiclesS30Step for selecting a powered dolly vehicleS40Step for locating the selected powered dolly vehicle in the geographical areaS50Step for communicating the location of the selected powered dolly vehicle to the primary vehicle or operating the powered dolly vehicle to the primary vehicle |
Please summarize the input | providing supporting device of vehicle, vehicle and method for vehicle driversproviding under the possible condition of overtaking the manual or semi-autonomous driving during a vehicle driver assisting device (1), the vehicle (2) and method (100). device (1) comprises a detector (4), a communication unit (5), which is arranged to be based on historical route information and car navigation system information of at least one of the front vehicle (3) receives the vehicle route information, a processing unit (6); which is set to the vehicle (3) will travel along one or more routes to one or more routes in the vehicle (2) in front of the possibility of processing the received information into a format that is suitable for display, and display the processed information. provide support to the vehicle by the driver.|1. providing support of the vehicle (2) in the device (1) is a vehicle driver during manual or semi-autonomous driving under the possible overtaking condition, wherein said device (1) comprising: a detector (4); the detector (4) is set to detecting the vehicle (2) and vehicle (3) between the closing velocity and distance of the vehicle (2) in the vehicle (3) is at least one of a communication unit (5), the communication unit (5) is set to front vehicle (3) receiving based on historical route information and car navigation system information of the vehicle (3) at least one of the possible route information; synchronized processing unit (6), said processing unit (6) is arranged from the detector (4) receiving indication to the vehicle (2) is at least one of information of the vehicle (3) between the closing velocity and distance of the vehicle (2) in the vehicle (3). if the vehicle (2) detected by the approach speed (3) is greater than the vehicle (2) approaches the speed threshold value or if the detected and the distance between the vehicle (3) is less than the distance threshold, triggering the communication unit (5) to receive information about possible route of the vehicle (3); for the vehicle (2) in front of one or more the plurality of routes, determining vehicle (3) along the one or more possibility of route travel. the determination is based on the received vehicle data and received historical route information in the car navigation system information of at least one, and is set to the received information into a format that is suitable for display, one or more display units (7), the display unit (7) is arranged from the processing unit (6) receives the determined vehicle route information and displays the processed information. providing a support to the vehicle by the driver.
| 2. The said device (1) according to claim 1, wherein determining the possible routes of two or more vehicle (3).
| 3. The said device (1) according to claim 1 or 2, wherein said communication unit (5) is set with one or more possible routes to one or more vehicle (3) sending a request to receive with said one or more vehicle (3) of related information.
| 4. The said device (1) according to claim 1 or 2, wherein said communication unit (5) is configured for communication with other vehicle through vehicle-to-vehicle communication (V2V).
| 5. The said device (1) according to claim 1 or 2, wherein said communication unit (5) is configured to then through vehicle to infrastructure for communication with other vehicle-to-vehicle communication (V2I2V).
| 6. The said device (1) according to claim 1 or 2, wherein said one or more display unit (7) is arranged to only less than the distance (3) between the vehicle (2) detected by the detector (4) of the display process of the information distance threshold value.
| 7. The said device (1) according to claim 1, wherein said device (1) comprises a positioning system (8), the positioning system (8) is connected to a map database (9) and configured to continuously determine the position of the vehicle (2).
| 8. The said device (1) according to claim 1, wherein the likelihood that the one or more display unit (7) is configured to display information via a chart, and the first graphic element (10a) represents (3) along the first path (11a), and the second graphic element (10b) represents (3) along a second path (11b) is possible.
| 9. The method according to claim 7 8, wherein said device (1) connected to the vehicle positioning system (8) of the map database (9) is configured to receive the vehicle route, the processing unit (6) is arranged to the vehicle route is compared with the determined route of one or more vehicle (3), and the one or more display unit (7) is arranged to display indicating two or more route overlapped distance information.
| 10. The method according to claim 7 8, wherein said device (1) is connected to a map database (9) of the vehicle locating system (8) is configured to receive the vehicle route. the processing unit (6) is arranged to the vehicle route is compared with the determined route of one or more vehicle (3), and the one or more display unit (7) is arranged to display indicating one or more vehicle (3) to be driving the possibility of optional distance along the route of the vehicle along the vehicle route information.
| 11. A vehicle (2), wherein the vehicle (2) comprises said device according to any one of said claims (3).
| 12. method (100) provides support to the vehicle driver during manual or semi-automatic driving under the possible condition of overtaking, wherein the method (100) comprises: at least one of a period detector (101) between the vehicle and the front vehicle closing velocity and the distance between the vehicle and the front vehicle, connecting the indication of vehicle and front vehicle closing velocity and the distance between the vehicle and the front of at least one of information received from the detector (102) to the processing unit; Pour detected if the closing velocity of the vehicle and the front vehicle is larger than the distance between the vehicle and the front vehicle approach speed threshold or if the detection is less than the distance threshold. is triggered by the processing unit (103) communication unit to front vehicle received based on historical route information and car navigation system information in front of at least one of the route information determined by the processing unit (104) along one or more routes to one or more routes the possibility of running in front of the vehicle, said determining historical route information based on at least one of the received data and the received car navigation system information in information processing (105) the received into a format that is suitable for display, by one or more display unit (106) for processing the information. | The arrangement (1) has a detector (4) to detect closing velocity and/or distance between a host vehicle (2) and a preceding vehicle (3). A processing unit (6) triggers a communication unit (5) to receive information on a probable route of preceding vehicle when a detected closing velocity is above threshold velocity or distance is below threshold distance. The probability that preceding vehicle will drive along routes is determined for route ahead of host vehicle. The received information is processed for display (7a). An INDEPENDENT CLAIM is included for a method for providing vehicle driver support for a driver of a host vehicle during manual or semi-autonomous driving in a potential overtake scenario. Arrangement in host vehicle (claimed) for providing vehicle driver support during manual or semi-autonomous driving. The vehicle driver is supported such that unnecessary overtaking is avoided, since the probability that the preceding vehicle will drive along the one or more route ahead of the host vehicle is determined and arranged to be displayed. The vehicles are enabled to share information between them in an easy, reliable and cost efficient manner. The drawing shows a schematic view of the vehicle and the arrangement in the vehicle for providing vehicle driver support during manual or semi-autonomous driving in the potential overtake scenario. 1Arrangement2Host vehicle3Preceding vehicle4Detector5Communication unit6Processing unit7aDisplay |
Please summarize the input | Apparatus and method for prediction of time available for autonomous driving, in a vehicle having autonomous driving capProvided are a method and an apparatus (1) for prediction of time available for autonomous driving, in a vehicle (2) having autonomous driving capabilities and comprising remote sensors (3) arranged to acquire vehicle surrounding information (4) and vehicle dynamics sensors (5) arranged to determine vehicle dynamics parameters (6), as well as a vehicle (2) comprising such an apparatus (1). At least one of a positioning arrangement (7) that provides map data with associated information; a route planning arrangement (8) that enables route planning; and a real time information acquiring arrangement, that acquires at least one of real time traffic information (9a) and real time weather information (9b). The time available is calculated based on a planned route and at least one of vehicle surrounding information (4), vehicle dynamics parameters (6), map data with associated information, real time traffic information (9a) and real time weather information (9b), for the planned route. The calculated time is output to a human machine interface (11) arranged in a vehicle (2).|1. An apparatus (1) for prediction of time available for autonomous driving, in a vehicle (2) having autonomous driving capabilities, the vehicle (2) comprising:
* remote sensors (3) arranged to acquire vehicle surrounding information (4);
* vehicle dynamics sensors (5) arranged to determine vehicle dynamics parameters (6);
* the apparatus further comprising:
* at least one of: a positioning arrangement (7) arranged to provide map data with associated speed limit and road infrastructure information; a route planning arrangement (8); and an arrangement for acquiring real time information (9), including at least one of real time traffic information (9a) and real time weather information (9b), and further
* a processor (10) arranged to calculate a time available for autonomous driving based on a planned route and at least one of vehicle surrounding information (4), vehicle dynamics parameters (6), map data with associated speed limit and infrastructure information, real time traffic information (9a) and real time weather information (9b),
* associated with the planned route; and
* a human machine interface (11) arranged to output to a vehicle (2) passenger compartment (12) the calculated time available for autonomous driving along the planned route characterized in that the processor (10) further is arranged to calculate a hand over time, required for hand over from autonomous driving to manual driving, and to include this calculated hand over time in the calculation of time available for autonomous driving.
| 2. An apparatus (1) according to claim 1, characterized in that the processor (10) is arranged to calculate the time available for autonomous driving based on at least road infrastructure information, real time traffic information (9a) and real time weather information (9b).
| 3. An apparatus (1) according to any one of claims 1 to 2, characterized in that the processor (10) further is arranged to calculate the time available for autonomous driving based on certified road sections allowed to drive autonomously on.
| 4. An apparatus (1) according to any one of claims 1 to 3, characterized in that the arrangement for acquiring real time information (9), when present, comprises an interface for communication via one or more portable communication devices of vehicle occupants for acquiring the real time information.
| 5. An apparatus (1) according to any one of claims 1 to 3, characterized in that the arrangement for acquiring real time information (9), when present, comprises an interface for performing at least one of vehicle-to-vehicle and vehicle-to-infrastructure communication for acquiring the real time information.
| 6. A method for prediction of time available for autonomous driving, in a vehicle (2) having autonomous driving capabilities, the vehicle (2) comprising:
* remote sensors (3) arranged to acquire vehicle surrounding information (4);
* vehicle dynamics sensors (5) arranged to determine vehicle dynamics parameters (6);
* the method comprising at least one of the steps of:
* providing map data with associated speed limit and road infrastructure information using a positioning arrangement (7);
* performing route planning using a route planning arrangement (8); and
* acquiring real time information, including at least one of real time traffic information (9a) and real time weather information (9b), and the steps of:
* calculating, using a processor (10), a time available for autonomous driving based on a planned route and at least one of vehicle surrounding information (4), vehicle dynamics parameters (6), map data with associated speed limit and infrastructure information,
* real time traffic information (9a) and real time weather information (9b), associated with the planned route, and calculating a hand over time, required for hand over from autonomous driving to manual driving, and including this calculated hand over time in the calculation of time available for autonomous driving; and
* outputting, to a human machine interface (11) arranged in a vehicle (2) passenger compartment (12), the calculated time available for autonomous driving along the planned route.
| 7. An automotive vehicle (2) having autonomous driving capabilities characterized in that it comprises an apparatus (1) for prediction of time available for autonomous driving according to any one of claims 1 to 5. | The apparatus (1) has a processor (10) arranged to calculate time available for autonomous driving based on a planned route and one of vehicle surrounding information (4), vehicle dynamics parameters (6), map data with associated speed limit and infrastructure information, real time traffic information (9a) and real time weather information (9b) associated with the planned route. A human machine interface (11) is arranged to output calculated time available for autonomous driving along the planned route to a vehicle and passenger compartment (12). Apparatus for prediction of time available for autonomous driving in an automotive vehicle (claimed). The apparatus calculates hand over time in calculation of time available for autonomous driving, thus ensuring that time available for autonomous driving is not less than time required for hand over from autonomous driving to manual driving so as to ensure that a vehicle driver does not suffer stressful and potentially dangerous transition to manual driving. The apparatus ensures that provision of an interface for communication through portable communication devices of vehicle occupants for acquiring real time information enables realization of less complex and cost effective apparatus or alternatively provision of a redundant back-up channel for acquiring real time information. The drawing shows a schematic view of an apparatus for prediction of time available for autonomous driving in a vehicle with autonomous driving capabilities. 1Apparatus for prediction of time available for autonomous driving in automotive vehicle4Vehicle surrounding information6Vehicle dynamics parameters9aReal time traffic information9bReal time weather information10Processor11Human machine interface12Vehicle and passenger compartment |
Please summarize the input | DEVICE AND METHOD FOR SAFETY STOPPAGE OF AN AUTONOMOUS ROAD VEHICLEDevice and method for safety stoppage of an autonomous road vehicle (2) having a localization system (3) and sensors (4) for monitoring the vehicle (2) surroundings and motion, and a signal processing system (5) for processing sensor signals enabling an autonomous drive mode thereof. Processing means (7) continuously: predict where a drivable space (8) exists; calculate and store a safe trajectory (10) to a stop within the drivable space (8); determine a current traffic situation; determine any disturbances in sensor data, vehicle systems or components enabling the autonomous drive mode. If an incapacitating disturbance is determined, a request for a driver to take over control is signaled and determined if a driver has assumed control within a pre-determined time. If not, the vehicle (2) is controlled to follow the most recent safe trajectory (10) to a stop in a safe stoppage maneuver during which, or after stopping, one or more risk mitigation actions adapted to the determined current traffic situation are performed.|1. A safety stoppage device (1) of an autonomous road vehicle (2) having a localization system (3) and sensors (4) for monitoring the autonomous road vehicle (2) surroundings and motion, and a signal processing system (5) for processing sensor signals enabling an autonomous drive mode of the autonomous road vehicle (2) by an autonomous drive control unit (6) thereof,
characterized in that it comprises processing means (7) arranged to continuously:
* predict where a drivable space (8) exists, based on data from the sensors (4);
* calculate and store to memory means (9) of the autonomous drive control unit (6) a safe trajectory (10) to a stop within the drivable space (8);
* determine from at least the localization system (3) and the sensors (4) a current traffic situation;
* determine any disturbances in sensor data, vehicle systems or components enabling the autonomous drive mode of the autonomous road vehicle (2); and
* if a disturbance is determined, such that the autonomous drive mode is incapacitated, signal to a driver environment of the autonomous road vehicle (2) a request for a driver to take over control of the autonomous road vehicle (2) and, determine if control of the autonomous road vehicle (2) has been assumed by a driver thereof within a pre-determined time, and ,upon a negative determination to control the autonomous vehicle (2) by the autonomous drive control unit (6) to follow the most recently calculated safe trajectory (10) to a stop within the drivable space (8) in a safe stoppage maneuver, wherein, during performance of such a safe stoppage maneuver or after the autonomous road vehicle (2) has stopped, the safety stoppage device (1) further is arranged to perform one or more risk mitigation actions adapted to the determined current traffic situation.
| 2. The safety stoppage device (1) according to claim 1, characterized in that the processing means (7) further are arranged to continuously estimate a risk associated with performing the safe stoppage maneuver in the determined current traffic situation and to adapt the one or more risk mitigation actions to the estimated risk.
| 3. The safety stoppage device (1) according to claims 2, characterized in that the processing means (7) further are arranged to adapt at least one of timing and intensity of the one or more risk mitigation actions to the estimated risk.
| 4. The safety stoppage device (1) according to any one of claims 1 to 3, characterized in that the processing means (7) further are arranged to signal the request to take over control of the autonomous road vehicle (2) to a driver environment of the autonomous road vehicle (2) using means (11, 12, 13) for visual, audible or haptic communication, or any combination thereof.
| 5. The safety stoppage device (1) according to any one of claims 1 to 4, characterized in that the one or more risk mitigation actions comprises at least one of: increasing the magnitude of the request for a driver to take over control of the autonomous road vehicle (2); activating hazard lights (14) of the autonomous road vehicle (2); activating a horn (15) of the autonomous road vehicle (2); warning or informing other traffic participants trough vehicle-to-vehicle communication (16); notifying a traffic control center (17) that a safe stoppage maneuver is in progress or completed; warning trailing vehicles (18) by blinking tail or brake lights (19) of the autonomous road vehicle (2).
| 6. The safety stoppage device (1) according to any one of claims 1 to 5, characterized in that the safety stoppage device (1) further is arranged to activate the one or more risk mitigation actions a predetermined time period after the autonomous road vehicle (2) has come to a stop.
| 7. The safety stoppage device (1) according to any one of claims 1 to 5, characterized in that the safety stoppage device (1) further is arranged to activate the one or more risk mitigation actions during performance of the safe stoppage maneuver.
| 8. The safety stoppage device (1) according to any one of claims 1 to 5, characterized in that the safety stoppage device (1) further is arranged to activate the one or more risk mitigation actions after the autonomous vehicle (2) has stopped.
| 9. The safety stoppage device (1) according to any one of claims 1 to 8, characterized in that it further comprises driver monitoring means (20) for determining a physical state of a driver of the autonomous road vehicle (2) and that the safety stoppage device (1) further is arranged to adapt the one or more risk mitigation actions to the monitored physical state of a driver of the autonomous road vehicle (2).
| 10. The safety stoppage device (1) according to claim 9, characterized in that the safety stoppage device (1) further is arranged to adapt the one or more risk mitigation actions to be performed earlier when the monitored physical state of a driver of the autonomous road vehicle (2) indicates an incapacitated driver.
| 11. The safety stoppage device (1) according to any one of claims 9 to 10, characterized in that it further is arranged to monitor and store to the memory means (9) data related to safe stoppage maneuver incidents where a monitored physical state of a driver of the autonomous road vehicle (2) indicates these safe stoppage maneuver incidents to be caused by a reckless driver and to deactivate the autonomous drive mode of the autonomous road vehicle (2) after a predetermined number (n) of such incidents.
| 12. The safety stoppage device (1) according to any one of claims 1 to 11, characterized in that it further comprises communication means (21) for communicating with a traffic control center (17), such that the traffic control center (17) is allowed to monitor the position of the autonomous road vehicle (2) and trigger the safety stoppage device (1) to perform the one or more risk mitigation actions when the monitored the position of the autonomous road vehicle (2) indicates that it is stationary in a potentially unsafe position.
| 13. A method for safety stoppage of an autonomous road vehicle (2) having a localization system (3) and sensors (4) for monitoring the autonomous road vehicle (2) surroundings and motion, and a signal processing system (5) for processing sensor signals enabling an autonomous drive mode of the autonomous road vehicle (2) by an autonomous drive control unit (6) thereof,
characterized in that it comprises using processing means (7) for continuously:
* predicting where a drivable space (8) exists, based on data from the sensors (4);
* calculating and storing to memory means (9) of the autonomous drive control unit (6) a safe trajectory (10) to a stop within the drivable space (8);
* determining from at least the localization system (3) and the sensors (4) a current traffic situation;
* determining any disturbances in sensor data, vehicle systems or components enabling the autonomous drive mode of the autonomous road vehicle (2); and
* if a disturbance is determined, such that the autonomous drive mode is incapacitated, signaling to a driver environment of the autonomous road vehicle (2) a request for a driver to take over control of the autonomous road vehicle (2) and, determining if control of the autonomous road vehicle (2) has been assumed by a driver thereof within a pre-determined time, and ,upon a negative determination controlling the autonomous vehicle (2) by the autonomous drive control unit (6) to follow the most recently calculated safe trajectory (10) to a stop within the drivable space (8) in a safe stoppage maneuver,
* and, during performance of such a safe stoppage maneuver or after the autonomous road vehicle (2) has stopped, performing one or more risk mitigation actions adapted to the determined current traffic situation.
| 14. An autonomous road vehicle (2) having a localization system (3) and sensors (4) for monitoring the autonomous road vehicle (2) surroundings and motion, and a signal processing system (5) for processing sensor signals enabling an autonomous drive mode of the autonomous road vehicle (2) by an autonomous drive control unit (6) of the autonomous road vehicle (2), characterized in that it comprises a safety stoppage device (1) according to any one of claims 1 to 12. | The device (1) has a processing unit (7) for determining whether control of an autonomous road vehicle (2) has been assumed by a driver within pre-determined time and following calculated safe trajectory to a stop within drivable space in a safe stoppage maneuver based on negative determination to control the autonomous road vehicle by an autonomous drive control unit (6), where the device performs risk mitigation actions adapted to determined current traffic situation during performance of the safe stoppage maneuver or after stopping the autonomous road vehicle. An INDEPENDENT CLAIM is also included for a method for facilitating safety stoppage of an autonomous road vehicle. Safety stoppage device for an autonomous road vehicle. The device continuously estimates risk associated with performing the safe stoppage maneuver in the determined current traffic situation, and adapts the risk mitigation actions to the estimated risk for providing reduced risk of accident when the autonomous vehicle is to be stopped, and the driver is not capable of taking control over the vehicle. The drawing shows a schematic view of an autonomous road vehicle comprising a safety stoppage device. 1Safety stoppage device2Autonomous road vehicle4Sensors6Autonomous drive control unit7Processing unit |
Please summarize the input | Apparatus and method for continuously establishing a boundary for autonomous driving availability and an automotive vehicle comprising such an apparatusProvided are a method and an apparatus (1) for continuously establishing a boundary for autonomous driving availability, in a vehicle (2) having autonomous driving capabilities and comprising remote sensors (3) for acquiring vehicle surrounding information (4) and vehicle dynamics sensors (5) for determining vehicle dynamics parameters (6), as well as a vehicle (2) comprising such an apparatus (1). At least one of a positioning arrangement (7) that provides map data with associated information; a route planning arrangement (8) that enables route planning; a vehicle driver monitoring arrangement (9) that provides driver monitoring information (10); and a real time information acquiring arrangement, that acquires at least one of traffic information (11 a) and weather information (11 b). The boundary is calculated based on a planned route and at least one of vehicle surrounding information (4), vehicle dynamics parameters (6), driver monitoring information (10), map data, traffic information (11a) and weather information (11b), for the planned route. Changes in the calculated boundary are output to a human machine interface (13) arranged in the vehicle (2).|1. An apparatus (1) for continuously establishing a boundary for autonomous driving availability in a vehicle (2) having autonomous driving capabilities, the vehicle (2) comprising:
* sensors (3) arranged to acquire vehicle surrounding information (4);
* vehicle dynamics sensors (5) arranged to determine vehicle dynamics parameters (6);
* further comprising:
* at least one of: a positioning arrangement (7) arranged to provide map data with associated speed limit and road infrastructure information; a route planning arrangement (8); a vehicle driver monitoring arrangement (9) arranged to provide vehicle driver monitoring information (10); and an arrangement for acquiring real time information (11), including at least one of real time traffic information (11a) and real time weather information (11b), and
* a processor (12) arranged to continuously calculate a boundary for autonomous driving availability based on a planned route and at least one of vehicle surrounding information (4), vehicle dynamics parameters (6), vehicle driver monitoring information (10), map data with associated speed limit and infrastructure information, real time traffic information (11a) and real time weather information (11b), associated with the planned route, and
* characterized in that it further comprises:
* a human machine interface (13) arranged to output to a vehicle (2) passenger compartment (14) information on any changes in the calculated boundary for autonomous driving availability along the planned route,
* wherein the human machine interface (13) is arranged to present the information graphically to a display indicating a distance to the calculated boundary for autonomous driving availability.
| 2. An apparatus (1) according to claim 1, characterized in that it further comprises an interface for communicating the information on any changes in the calculated boundary for autonomous driving availability along the planned route to an autonomous drive control unit of the vehicle.
| 3. An apparatus (1) according to any one of claims 1 to 2, characterized in that the human machine interface (13) further is arranged to output to the vehicle (2) passenger compartment (14) information relating to changes in automation level available with the current calculated boundary for autonomous driving availability.
| 4. An apparatus (1) according to any one of claims 1 to 3, characterized in that the arrangement for acquiring real time information (11), when present, comprises an interface for communication via one or more portable communication devices of vehicle (2) occupants for acquiring the real time information.
| 5. An apparatus (1) according to any one of claims 1 to 4, characterized in that the arrangement for acquiring real time information (11), when present, comprises an interface for performing at least one of vehicle-to-vehicle and vehicle-to-infrastructure communication for acquiring the real time information.
| 6. An apparatus (1) according to any one of claims 1 to 5, characterized in that it further comprises an interface for communicating the information on any changes in the calculated boundary for autonomous driving availability along the planned route externally of the vehicle (2) using at least one or more portable communication devices of vehicle (2) occupants, vehicle-to-vehicle communication and vehicle-to-infrastructure communication.
| 7. A method for continuously establishing a boundary for autonomous driving availability in a vehicle (2) having autonomous driving capabilities, the vehicle (2) comprising:
* sensors (3) arranged to acquire vehicle surrounding information (4);
* vehicle dynamics sensors (5) arranged to determine vehicle dynamics parameters (6);
* the method comprising at least one of the steps of:
* providing map data with associated speed limit and road infrastructure information using a positioning arrangement (7);
* performing route planning using a route planning arrangement (8);
* monitoring a vehicle driver and providing vehicle driver monitoring information (10) using a vehicle driver monitoring arrangement (9); and
* acquiring real time information, including at least one of real time traffic information (11a) and real time weather information (11b), and the step of:
continuously calculating, using a processor (12), a boundary for autonomous driving availability based on based on a planned route and at least one of vehicle surrounding information (4), vehicle dynamics parameters (6), vehicle driver monitoring information (10), map data with associated speed limit and infrastructure information, real time traffic information (11a) and real time weather information (11b), associated with the planned route,
* characterized in that the method further comprises:
outputting, to a human machine interface (13) arranged in a vehicle (2) passenger compartment (14), information on any changes in the calculated boundary for autonomous driving availability along the planned route,
* wherein the human machine interface (13) is arranged to present the information graphically to a display indicating a distance to the calculated boundary for autonomous driving availability.
| 8. An automotive vehicle (2) having autonomous driving capabilities characterized in that it comprises an apparatus (1) for continuously establishing a boundary for autonomous driving availability according to any one of claims 1 to 6. | The apparatus (1) has a processor (12) continuously calculating a boundary for autonomous driving availability based on a planned route and one of vehicle surrounding information (4), vehicle dynamics parameters (6), vehicle driver monitoring information (10), map data with associated speed limit and infrastructure information, real time traffic information (11a), and real time weather information (11b) associated with the planned route. A human-machine interface (13) outputs information on any changes in the calculated boundary along the planned route to the passenger compartment (14). An INDEPENDENT CLAIM is also included for a method for continuously establishing a boundary for autonomous driving availability in a vehicle having autonomous driving capabilities. Apparatus for continuously establishing boundary for autonomous driving availability in vehicle having autonomous driving capabilities. The provision of a continuously calculated boundary for autonomous driving availability and a human machine interface arranged to output to a vehicle passenger compartment information on any changes in the calculated boundary for autonomous driving availability along the planned route promotes the driver's trust in the autonomous driving capabilities of the vehicle as well as increases the driver's readiness to assume manual control of the vehicle if so required. The provision of a request for hand over from autonomous driving to manual driving provides sufficient time for safe hand over from autonomous driving to manual driving, thus ensuring that the vehicle driver does not suffer a stressful and potentially dangerous transition to manual driving. The provision of communicating the information on any changes in the calculated boundary for autonomous driving availability along the planned route to an autonomous drive control unit of the vehicle enables vehicle systems performs adaptations in dependence upon the available degree of automation indicated by the calculated boundary for autonomous driving availability. The provision of a human machine interface arranged to output to the vehicle passenger compartment information relating to changes in automation level available with the current calculated boundary for autonomous driving availability enables a driver of the vehicle to become aware of why adaptations in in the autonomous drive, e.g. towards a higher or lower degree of automation, is made and also enables the driver to continuously monitor the autonomous drive while retaining a feeling of control. The provision of outputting the information to the vehicle passenger compartment through one of a graphical, an audio or a tactile output arrangement provides options for ensuring that the information reaches the vehicle driver, irrespective of his/her current focus. The provision of an interface for communication via one or more portable communication devices of vehicle occupants for acquiring the real time information enables either the realization of a less complex and more cost effective apparatus or alternatively the provision of a redundant back-up channel for acquiring the real time information. The provision of an interface for performing one of vehicle-to-vehicle and vehicle-to-infrastructure communication for acquiring the real time information enables the realization of an effective apparatus for acquiring real time information which is highly relevant for the current surroundings. The drawing shows the schematic diagram of an apparatus for continuously establishing a boundary for autonomous driving availability, in a vehicle having autonomous driving capabilities. 1Apparatus4Vehicle surrounding information6Vehicle dynamics parameters10Vehicle driver monitoring information11aReal time traffic information11bReal time weather information12Processor13Human-machine interface14Passenger compartment |
Please summarize the input | CONCEPT OF COORDINATING AN EMERGENCY BRAKING OF A PLATOON OF COMMUNICATIVELY COUPLED VEHICLESThe present invention provides a concept of coordinating emergency braking of a platoon 100 of communicatively connected vehicles 110 . In response to the emergency situation 120 , individual braking control settings are centrally determined for one or more vehicles 110 of the platoon 100 by the management entity 110 - 3 managing the platoon 100 ( 230 ). ). The individual braking control settings are communicated from the management entity 110 - 3 to the one or more vehicles 110 of the platoon 100 . One or more vehicles 110 of the platoon 100 brake 250 according to each individual brake control setting received from the management entity 110 - 3 .|1. A method (200) for adjusting emergency braking of a platoon (100) of communicatively connected vehicles (110), wherein an emergency (120) is detected by the vehicle (110-1) of the platoon a step 210 of;
broadcasting (220) an emergency message from the vehicle (110-1) to other vehicles (110-2, 110-3) of the platoon in response to the detection of the emergency situation (120);
in response to receiving the emergency message from the vehicle (110-1), forming a braking pressure of the other vehicles (110-2, 110-3) of the platoon;
Individual braking for one or more vehicles 110 of the platoon 100 by the management entity 110 - 3 managing the platoon 100 after generating the braking pressure in response to the emergency situation 120 . determining (230) control settings;
transmitting (240) the individual braking control settings from the management entity (110-3) to one or more vehicles (110) of the platoon (100);
A method of adjusting emergency braking, comprising the step of braking (250) one or more vehicles (110) of the platoon (100) according to respective individual braking control settings received from the management entity (110-3) (200).
| 2. The method (200) of claim 1, wherein the individual braking control settings indicate respective braking forces to be applied.
| 3. The emergency braking according to claim 1 or 2, further comprising the step of notifying the management body (110-3) about at least one of individual characteristics and current conditions of each vehicle of the platoon (100). How to adjust (200).
| 4. A method (200) according to claim 3, wherein determining the individual braking control settings of the vehicle (110) is based on at least one of an individual characteristic and a current condition of each vehicle.
| 4. The method (200) according to claim 3, wherein at least one of the individual characteristics and the current state comprises at least one of weight, braking force, inter-vehicle distance, speed, and tire condition.
| 6. Method (200) according to claim 1 or 2, wherein the plurality of vehicles (110) of the platoon (100) are each an autonomous vehicle or an at least partially autonomous vehicle. .
| 3. A method (200) according to claim 1 or 2, wherein the plurality of vehicles (110) in the platoon communicate via a vehicle-to-vehicle communication system.
| 8. The emergency braking according to claim 1 or 2, wherein the management body (110-3) is a vehicle of the platoon (100) acting as a master vehicle with respect to other vehicles of the platoon acting as a slave vehicle. How to adjust (200).
| 9. In a platoon 100 of a plurality of communicatively connected vehicles 110, a first vehicle configured to detect an emergency situation and broadcast an emergency message to other vehicles in the platoon in response to the detected emergency situation ( 110-1), wherein in response to receiving the emergency message from the first vehicle (110-1), a braking pressure of another vehicle in the platoon is established;
a second vehicle (110-3) configured to determine a respective brake control parameter for each vehicle of the platoon in response to the emergency message and send the respective individual brake control parameter to each vehicle of the platoon;
a third vehicle (110-2) configured to adjust its braking setting according to its respective individual braking control parameter, wherein prior to reception of the individual braking control parameter by the second vehicle (110-3), the a plurality of communications, wherein in response to receiving the emergency message from the first vehicle 110 - 1 , a braking pressure is established in the second vehicle 110 - 3 and the third vehicle 110 - 2 . Platoon 100 of vehicles 110 connected to each other. | The method (200) involves determining (230) individual braking control settings for one or more vehicles of the platoon by a managing entity managing the platoon in response to an emergency situation. The individual braking control settings from the managing entity are communicated (240) to one or more vehicles of the platoon. One or more vehicles of the platoon are braked (250) in accordance with the respective individual braking control settings received from the managing entity. INDEPENDENT CLAIMS are included for the following:a system of several communicatively coupled vehicle; anda vehicle. Method for coordinating emergency braking of platoon of communicatively coupled vehicle (claimed). The managing entity can be kept up to date with respect to current vehicle parameters, leading to more accurate individual braking control settings. The accurate prediction and coordination of the individual emergency braking maneuvers can be allowed. The drawing shows a flowchart illustrating the process for coordinating emergency braking of platoon of communicatively coupled vehicle. 200Method for coordinating emergency braking of platoon of communicatively coupled vehicle210Step for detecting emergency situation by vehicle of platoon230Step for determining individual braking control settings for one or more vehicles of platoon240Step for communicating individual braking control settings from managing entity to one or more vehicles of platoon250Step for braking one or more vehicles of platoon |
Please summarize the input | Method for providing travel route presettingThe invention relates to a method for providing travel route presetting (100) for a travel route system of a vehicle (300), the method comprises the following steps: providing a plurality of detected tracks (101) of other vehicles (320) in the route section (150) to be travelled, determining track preset (102) from the detected tracks (101), determining a deviation area (110) according to the detected tracks (101), wherein the deviation area (110) is determined according to the deviation between at least each detected track (101) and the track preset (102), and the travel route preset (100) is determined at least according to the track preset (102) and the deviation area (110). & #10; In addition, the present invention relates to a travel route system for a vehicle (300), comprising: a receiving module (301), for receiving the track (101) detected in the route section (150) to be travelled, a calculating unit (302), the receiving module (301) sends the detected track (101) to the calculating unit, -the computing unit is adapted to determine a trajectory preset (101) from the detected trajectory (101) and to determine a deviation area (110) based on at least the deviation of each detected trajectory (101) from the trajectory preset (102), and determining a travel route preset (100) based at least on the trajectory preset (102) and the deviation area (110).|1. A method for providing a travel route presetting (100) for a travel route system of a vehicle (300), the method comprising the steps of: providing a plurality of detected tracks (101) of other vehicles (320) in the route section (150) to be travelled, determining track presets (102) from the detected tracks (101), determining a deviation area (110) according to the detected trajectory (101), wherein the deviation area (110) is determined according to the deviation between at least each detected trajectory (101) and the trajectory preset (102), wherein the deviation area (110) is set as The deviation area (110) surrounds the track presetting (102), and thus forms the following area in the route section (150) to be travelled: when the vehicle (300) should move from the first route point (30) to the second route point (40), the vehicle (300) can preferentially stay in the area, wherein the detected track (101) and the track preset (102) extend between the first route point (30) and the second route point (40), determining the travel route preset (100) at least according to the track preset (102) and the deviation area (110).
| 2. The method according to claim 1, wherein the detected trajectory (101) is transmitted by moving data and/or by vehicle-to-vehicle communication and/or between the vehicle (300) and/or infrastructure (400).
| 3. The method according to claim 1 or 2, wherein the track presetting (102) is the average track of the detected track (101), wherein the deviation area is calculated by the standard deviation of the detected track (101).
| 4. The method according to claim 1 or 2, wherein the following steps are further executed: detecting the sensor information about the environment of the vehicle (300) in the driving route, determining the track preset (103) based on the sensor according to the sensor information, and adapting the travel route presetting (100) according to the sensor-based trajectory presetting (103) and the deviation area (110).
| 5. The method according to claim 1 or 2, wherein the weighting factor is considered when determining the track presetting (102) and/or the deviation area (110). wherein the weighting factor takes into account the time between determining the detected trajectory (101) and the time at which the vehicle (300) plans to travel through the route section (150), and/or the type of the other vehicle (320) of the detected trajectory (101).
| 6. The method according to claim 1 or 2, wherein determining the interval of the travel route presetting (100) depends on the travel speed of the vehicle (300).
| 7. The method according to claim 1 or 2, wherein the vehicle (300) is pre-set (100) by the at least partially autonomous vehicle controller using the travel route so as to guide the vehicle (300) in the transverse and/or longitudinal direction.
| 8. The method according to claim 3, wherein the mean trajectory is determined by averaging through an arithmetic mean value of the detected trajectory (101).
| 9. The method according to claim 4, wherein the travel route presetting (100) is adapted when the sensor information is specific to an obstacle (50) in a route section (150) to be travelled by the vehicle (300).
| 10. A travel route system for a vehicle (300), the travel route system comprising: a receiving module (301), for receiving the track (101) detected in the route section (150) to be travelled, a calculating unit (302), the receiving module (301) sends the detected track (101) to the calculating unit, the calculation unit is adapted to determine a trajectory preset (102) from the detected trajectory (101) and determine a deviation area (110) based on at least a deviation of each detected trajectory (101) from the trajectory preset (102), and at least according to the track preset (102) and the deviation area (110) determining the driving route preset (100), wherein the deviation area (110) is set to, the deviation area (110) surrounds the track preset (102), and thereby forming the following areas in the route section (150) to be travelled: when the vehicle (300) should move from the first route point (30) to the second route point (40), the vehicle (300) can preferentially stay in the area, wherein the detected trajectory (101) and the trajectory presetting (102) extend between the first route point (30) and the second route point (40).
| 11. The travel route system according to claim 10, wherein the sensor (303) suitable for detecting the environment around the vehicle (300) is connected with the calculating unit (302) so as to send the sensor information to the calculating unit (302). wherein the calculating unit (302) is adapted to determine a sensor-based trajectory presetting (103) based on the sensor information so as to additionally determine a travel route presetting (100) based on the sensor-based trajectory presetting (103).
| 12. The travel route system according to claim 10 or 11, wherein the travel route system is adapted to perform the method for providing travel route presetting (100) according to any one of claims 1 to 8. | The method involves providing detected trajectories of vehicles in a path section to be traveled. A trajectory specification is determined from the detected trajectories. A deviation zone is determined from the detected trajectories. The deviation zone is determined on the basis of deviation of individual detected trajectories from the trajectory specification. The route specification is determined based on the trajectory specification and the deviation zone. An INDEPENDENT CLAIM is included for route system of vehicle. Method for providing route specification for route system (claimed) of vehicle e.g. car. The significance and the benefit of the trajectory specification is improved. The provision of a particularly safe and precise route specification is enabled. The quality of the route specification and the safety is increased. The unnecessary arithmetic operations are avoided in sections in which few changes are required. The unnecessary adaptation of the route is avoided. The drawing shows a schematic view of the route section to be traveled with different trajectories. 10,20First and second axes30First waypoint |
Please summarize the input | Method for the autonomous or partly autonomous execution of a cooperative driving maneuverA method for autonomously or semi-autonomously carrying out a cooperative driving maneuver and a vehicle. Provision is made for a maneuvering vehicle which plans the execution of a driving maneuver to determine a maneuvering area of a road in which the driving maneuver is potentially executed, to communicate with one or more vehicles via vehicle-to-vehicle communication to detect one or more cooperation vehicles which will presumably be inside the maneuvering area during the execution of the driving maneuver, and to adapt its own driving behavior to the presumable driving behavior of the one or more cooperation vehicles to execute the planned driving maneuver. The disclosure provides a possibility which, by vehicle-to-vehicle communication, allows vehicles for jointly carrying out a cooperative driving maneuver to be identified and then allows the cooperative driving maneuver to be executed.The invention claimed is:
| 1. A method for autonomously or semi-autonomously carrying out a cooperative driving maneuver, wherein a maneuvering vehicle plans execution of a driving maneuver, wherein during the method the maneuvering vehicle executes operations for planning and execution of a single driving maneuver comprising:
determining a maneuvering area of a road in which the driving maneuver is potentially executed;
communicating with other vehicles via vehicle-to-vehicle communication during the planning of the execution of the cooperative driving maneuver;
filtering communications received by the maneuvering vehicle via vehicle-to-vehicle communication from the vehicles, wherein the filtering is performed to determine which vehicles are relevant to carrying out the maneuvering vehicle's planned driving maneuver to detect cooperation vehicles which are presumed to be inside the maneuvering area during the execution of the driving maneuver;
determining message formats of the messages received by the maneuvering vehicle via vehicle-to-vehicle communication;
determining potential cooperation vehicles based on the message formats transmitted by the vehicles, and
adapting the maneuvering vehicle's own driving behavior to presumable driving behavior of the one or more cooperation vehicles of the potential cooperation vehicles to execute the planned driving maneuver,
wherein, in response to a determination that transmitted message formats are Environmental Perception Messages from the potential cooperation vehicles, wherein the Environmental Perception Messages contain information about free areas between the potential cooperation vehicles from the potential cooperation vehicles received by the maneuvering vehicle, the potential cooperation vehicles are determined to be one or more cooperation vehicles.
| 2. The method of claim 1, wherein the maneuvering vehicle determines an approach period in which the maneuvering vehicle is presumed to reach the maneuvering area.
| 3. The method of claim 1, wherein the maneuvering vehicle executes at least one of the following operations to detect the cooperation vehicles:
determining approach areas of the road, from which the maneuvering area is theoretically reached within the approach period;
determining potential cooperation vehicles which are in the approach areas based on the data received via vehicle-to-vehicle communication, predicting the driving behavior of each potential cooperation vehicle based on the data received via vehicle-to-vehicle communication;
predicting its own driving behavior; and
comparing the predicted driving behavior of the one or more potential cooperation vehicles with the maneuvering vehicle's own predicted driving behavior to determine the plurality of cooperation vehicles.
| 4. The method of claim 1, wherein the maneuvering vehicle continuously detects and evaluates the development of free areas between vehicles and, for this purpose, executes the following operations:
determining the minimum size of a free area for carrying out the driving maneuver, the vehicle dimensions of the maneuvering vehicle and/or a safety distance;
comparing the size of newly detected free areas with the determined minimum size; and
selecting a suitable free area presumed to be inside the maneuvering area during the planned execution of the maneuver and to have at least the minimum size for executing the driving maneuver.
| 5. The method of claim 4, wherein the adaptation of the driving behavior of the maneuvering vehicle to the presumable driving behavior of the cooperation vehicles comprises adapting the trajectory of the maneuvering vehicle to reach the selected free area in response to the selected free area being inside the maneuvering area, wherein executability being cyclically checked as the free area is approached.
| 6. The method of claim 1, further comprising:
determining a maneuvering area of a road in which a driving maneuver of a vehicle is expected;
communicating between the cooperation vehicle and one or more vehicles via vehicle-to-vehicle communication to detect a maneuvering vehicle which plans the execution of a driving maneuver and is presumed to be inside the maneuvering area during the execution of the driving maneuver; and
adapting the driving behavior of the cooperation vehicle to the presumable driving behavior of the maneuvering vehicle to assist with the planned driving maneuver of the maneuvering vehicle.
| 7. The method of claim 6, wherein the cooperation vehicle determines the maneuvering area and/or an approach period in which it will presumably reach the maneuvering area.
| 8. The method of claim 6, wherein the cooperation vehicle executes at least one of the following operations to detect the maneuvering vehicle:
determining the message formats of the messages received via vehicle-to-vehicle communication;
determining one or more potential maneuvering vehicles based on the message format transmitted by these vehicles;
determining approach areas of the road, from which the maneuvering area is theoretically reached within the approach period;
determining one or more potential maneuvering vehicles which are in the approach areas by the data received via vehicle-to-vehicle communication;
predicting the driving behavior of each potential maneuvering vehicle by the data received via vehicle-to-vehicle communication;
predicting its own driving behavior; and
comparing the predicted driving behavior of the one or more potential maneuvering vehicles with the maneuvering vehicle's own predicted driving behavior to determine one or more maneuvering vehicles.
| 9. The method of claim 6, wherein the adaptation of the driving behavior of the cooperation vehicle to the presumable driving behavior of the maneuvering vehicle comprises the following operations:
adapting the trajectory of the cooperation vehicle to enlarge a free area in which the driving maneuver of the maneuvering vehicle is executed inside the maneuvering area.
| 10. A transportation vehicle comprising:
a communication device for communicating with other transportation vehicles by vehicle-to-vehicle communication; and
the vehicle being configured to, operate as a maneuvering vehicle and/or as a cooperation vehicle, autonomously or semi-autonomously carrying out a cooperative driving maneuver, wherein in operating as a maneuvering vehicle, which plans the execution of a driving maneuver, the vehicle executes the following operations:
determining a maneuvering area of a road in which the driving maneuver is potentially executed;
communicating with vehicles via vehicle-to-vehicle communication with each of a plurality of other vehicles that are presumed to be inside the maneuvering area of the road during execution of the driving maneuver, filtering the communications received via vehicle-to-vehicle communication from the other vehicles according to vehicles which are relevant to carrying out the planned driving maneuver to detect cooperation vehicles;
determining message formats of the messages received via vehicle-to-vehicle communication from these vehicles;
determining potential cooperation vehicles based on the message formats transmitted by these vehicles; and
adapting the maneuvering vehicle's own driving behavior to the presumable driving behavior of the cooperation vehicles to execute the planned driving maneuver,
wherein in response to a determination that transmitted message formats are Environmental Perception Messages from the potential cooperation vehicles containing information about free areas between the potential cooperation vehicles, the potential cooperation vehicles are determined to be cooperation vehicles for the maneuvering vehicle.
| 11. The method of claim 1, wherein information of each Environmental Perception Message is determined by sensors in each potential cooperation vehicle.
| 12. The method of claim 11, wherein the sensors comprise radar sensors and the Environmental Perception Messages are transmitted several times a second.
| 13. The vehicle of claim 10, wherein information of each Environmental Perception Message is determined by sensors in each potential cooperation vehicle.
| 14. The vehicle of claim 13, wherein the sensors comprise radar sensors and the Environmental Perception Messages are transmitted several times a second.
| 15. The method of claim 1, wherein the driving maneuver comprises merging into a flow of vehicles, wherein the communicating with the other vehicles includes communicating with a plurality of vehicles presumed to be in a maneuvering area during the execution of the merging.
| 16. The transportation vehicle of claim 10, wherein the driving maneuver comprises merging into a flow of vehicles, wherein the communicating with the other vehicles includes communicating with a plurality of vehicles presumed to be in a merging area during the execution of the merging. | The method involves determining a maneuvering area (28) of a road, in which the driving maneuver is potentially executed, and communicating with the vehicles by vehicle-to-vehicle communication. The filtering is carried out according to vehicles, which are relevant to carrying out the planned driving maneuver to detect the cooperation vehicles (26). The maneuvering vehicle own driving behavior is adapted to the presumable driving behavior of the cooperation vehicles to execute the planned driving maneuver. Method for autonomously or semi-autonomously carrying out a cooperative driving maneuver for vehicle (Claimed). The method involves determining a maneuvering area of a road, in which the driving maneuver is potentially executed, and communicating with the vehicles by vehicle-to-vehicle communication, and hence ensures safe and reliable driving maneuver carrying out method. The drawing shows a schematic representation of traffic situation. 26Cooperation vehicles28Maneuvering area30Road34Maneuvering vehicle36Approach areas |
Please summarize the input | Method and control system for determining a traffic gap between two vehicles for changing lanes of a vehicleThe present invention relates to a vehicle-to-vehicle communication system and a method for determining a traffic gap between two vehicles for changing lanes of a vehicle. The method includes identifying 110 a traffic gap based on a first detection and based on a second detection. The first detection is based on at least one vehicle-to-vehicle status message of at least one other vehicle 200. The second detection is based on the on-board sensor system of the vehicle 100.
|1. A method for determining a traffic gap between two vehicles for lane change of a vehicle 100, the method comprising: identifying (110) a traffic gap based on a first detection and based on a second detection, the second detection 1 detection is based on at least one vehicle-to-vehicle status message of at least one other vehicle 200, and the second detection is based on an on-board sensor system of the vehicle 100, the step of identifying ( 110);
Detecting (155) that the identifying (110) did not identify a traffic gap; And a driving intention message based on the step 155 of detecting that the identifying step 110 does not identify a traffic gap.- The driving intention message includes information on a request for a future lane change of the vehicle 100. Including the step of transmitting 160, wherein the rough detection of the traffic gap is performed in the first detection, and the precise detection of the traffic gap detected in the first detection in the second detection is performed. , A method for determining a traffic gap between two vehicles.
| 2. The vehicle-to-vehicle status message according to claim 1, wherein the at least one vehicle-to-vehicle status message includes information on at least one of a location and a trajectory of the at least one other vehicle (200), and the first detection is performed on the at least one other vehicle ( 200) based on information about at least one of the location and trajectory.
| 3. The method according to claim 1 or 2, wherein the identifying step (110) is further based on a third detection based on a vehicle-to-vehicle message comprising environmental information of the at least one other vehicle (200), and the environment The information is based on a sensor record of the environment of the at least one other vehicle 200 by at least one on-board sensor of the at least one other vehicle 200.
| 3. The method of claim 1 or 2, further comprising: longitudinally adjusting (120) the vehicle parallel to the identified traffic gap; And transversely adjusting (130) the vehicle by changing lanes parallel to the identified traffic gap.
| 5. The method of claim 4, wherein the longitudinal adjustment step (120) corresponds to the adjustment of the speed or position of the vehicle (100) in the driving direction, or the longitudinal adjustment step (120) is a speed for an adaptive cruise control system. -Including the step of providing a time curve, or the longitudinal adjustment step 120 includes displaying a longitudinal adjustment aid for the driver of the vehicle 100, or the vehicle 100 is automatically The method corresponds to the driving vehicle 100, wherein the longitudinal adjustment step 120 corresponds to the longitudinal control of the autonomous driving vehicle 100 based on the identified traffic gap.
| 6. The method of claim 4, wherein the transverse adjustment step (130) corresponds to the adjustment of the position of the vehicle (100) in the horizontal direction with respect to the driving direction, or the longitudinal adjustment step (120) is performed by the vehicle (100). When the position is set parallel to the identified traffic gap, the lateral adjustment step 130 is performed, or the lateral adjustment step 130 includes a driver-led automatic lane change, or the lateral adjustment step ( 130) includes the step of displaying a lateral direction adjustment assistance means for a driver of the vehicle 100, or the vehicle 100 corresponds to an autonomous vehicle 100, and the lateral direction adjustment step 130 Is corresponding to the lateral control of the self-driving vehicle 100.
| 3. A method according to any of the preceding claims, further comprising the step (150) of determining the driving intention of a driver of the vehicle with respect to a lane change.
| 8. The method of claim 7, further comprising transmitting (160) a driving intention message based on the step of determining the driving intention (150).
| 9. A method for a vehicle (205), the method comprising: receiving (210) a driving intention message including a lane change request from an inquiry vehicle (100);
As a step 220 of detecting information on cooperation during cooperative driving control with the inquiry vehicle 100, the information on cooperation includes cooperation in consideration of whether the vehicle 205 is possible as a cooperation partner and traffic conditions. The step of detecting 220 is to present whether the movement is possible based on the driving intention message;
To make it possible to calculate whether the interruption request can be met within the range of possible cooperation, information about at least one gap for at least one of the front vehicle and the rear vehicle is detected (232), and the driving control is performed. Based on the information, the information on the at least one interval, the speed of the vehicle 205 and the distance to the possible cooperation range, the execution of driving control is detected (234), and whether driving control is possible in consideration of the traffic situation By calculating whether or not (236), determining (230) information about the driving control; And providing (240) a driving assistance for executing driving control, wherein the method includes exchanging a vehicle-to-vehicle adjustment message for coordinating cooperative driving control with at least one other vehicle 200 Further comprising, upon receipt of a message regarding acceptance of a lane change request from the at least one other vehicle 200, the detecting step 220, the determining step 230 and the providing step 240 The method for vehicle 205, at least one of which is interrupted.
| 10. The vehicle according to claim 9, wherein the providing (240) of the driving assistance corresponds to an automatic or semi-automatic execution of driving control, or the providing (240) of the driving assistance is performed by means of a human-machine interface. The method corresponding to the step of providing guidance for the driver of 205 regarding the implementation of the driving maneuver.
| 11. The method of claim 9 or 10, wherein the providing step further comprises providing a message regarding acceptance of a lane change request between the inquiry vehicle 100 and the at least one other vehicle 200. Way.
| 12. In the control system 10 for vehicle 100, identifying a traffic gap does not identify a traffic gap, so as to identify a traffic gap between two vehicles based on a first detection and a second detection. In order to detect, if identifying the traffic gap does not identify the traffic gap, the vehicle in parallel to the identified traffic gap to transmit a driving intent message containing information regarding a future lane change request of the vehicle 100. Is formed to adjust longitudinally and to adjust the vehicle laterally by changing lanes parallel to the identified traffic gap, wherein the first detection is at least one vehicle-to-vehicle status message of at least one other vehicle 200 Based on, and the second detection is based on the on-board sensor system of the vehicle 100, the rough detection of the traffic gap is performed in the first detection, and the detection in the first detection in the second detection A control system for a vehicle, in which precise detection is performed on the resulting traffic gap.
| 13. In the control system 20 for the vehicle 205, the vehicle 205 when cooperative driving control with the inquiry vehicle 100 is received to receive a driving intention message including a lane change request from the inquiry vehicle 100 Calculate whether the interruption request can be met within the scope of possible cooperation, to detect information on cooperation that suggests based on the driving intention message whether it is possible as this cooperation partner and whether cooperation behavior is possible taking into account traffic conditions. In order to enable the detection of information on at least one interval for at least one of a front vehicle and a rear vehicle, information on driving control, information on the at least one interval, and speed of the vehicle 205 And a driving assistant for executing driving maneuvering to determine information about driving maneuvering by detecting the execution of driving maneuvering based on the distance for the possible cooperation range, and calculating whether driving maneuvering is possible in consideration of the traffic condition. To provide stance, And it is formed to exchange a vehicle-to-vehicle adjustment message for coordinating cooperative driving control with at least one other vehicle 205, upon receiving a message regarding acceptance of a lane change request from the at least one other vehicle 200 , At least one of the detecting (220), the determining (230) and the providing (240) is interrupted. | The method involves identifying (110) the traffic gap based on a first detection and based on a second detection. The first detection is based on a vehicle-to-vehicle status message of a vehicle (200). The second detection is based on a board sensor system of a vehicle (100). The vehicle-to-vehicle status message comprises information about a position and/or a trajectory of the vehicle (200). The first detection is based on the information about the position and/or the trajectory of the vehicle (200). An INDEPENDENT CLAIM is included for a control system for determining traffic gap between vehicles for lane change for vehicle. Method for determining traffic gap between vehicles for lane change for vehicle e.g. car. The traffic gap between vehicles for lane change for vehicle is determined effectively. The cooperative driving functions of the vehicle are supported efficiently. The drawings show the flow diagrams illustrating the process for determining traffic gap between vehicles for lane change for vehicle, and block diagram of the control system for determining traffic gap between vehicles for lane change for vehicle. (Drawing includes non-English language text) 100,200Vehicles110Step for identifying traffic gap120Step for performing longitudinal regulation corresponding to regulation of speed of vehicle130Step for performing transverse regulation for threading into selected gap150Step for determining driving intention of driver of vehicle |
Please summarize the input | METHOD FOR RESOURCE ALLOCATION IN A MOBILE COMMUNICATION SYSTEM AND BASE STATION, AND PARTICIPANT COMMUNICATION MODULE FOR THE USE IN THE METHODFor the scenario of vehicles (30) equipped with wireless communication modules (31) that communicate directly with each other on public roads, either for a cooperative or autonomous driving scenario, a very high reliability is very important. With LTE-V, the 3GPP standardization organization has specified a technique called sidelink communication with which the direct communication between cars is possible in the LTE frequency bands. The resources are scheduled in a base station (20) of a mobile communication cell. Since different mobile communication providers are available, there is the problem how to make it possible that participants from different providers can communicate with each other for a cooperative awareness traffic scenario with LTE-V communication. The solution proposed is that each provider will assign a dedicated spectrum (V, T, E, O) that is controlled by each provider itself for resource allocation for its own participants and the participants of other providers. The resource allocation management functionality for the direct communication among the participants from the plurality of providers is shifted from provider to provider from time slice (t_0, t_1, t_2, t_3) to time slice (t_0, t_1, t_2, t_3). This provides for a fair distribution of the resource management functionality among the different providers. At the same time, it avoids the provision of multiple transceiver chains in the communication modules with which the vehicles are equipped.|1. Method for resource allocation in a mobile communication system, comprising a plurality of base stations (20) from a plurality of mobile communication providers and a plurality of participants from the plurality of mobile communication providers, wherein each provider has assigned a dedicated spectrum (V, T, E, O) for resource allocation for its own participants, wherein the participants from the plurality of providers communicate directly among each other, wherein a given provider allocates a part (V2V) of its dedicated spectrum for the direct communication among the participants from the plurality of providers,
* ? wherein either said given provider will schedule the resources in the part (V2V) of the dedicated spectrum (V, T, E, O) for its own participants and the participants of the other providers by means of a scheduler (225) in a provider owned base station (20), or
* ? wherein the part (V2V) of a dedicated spectrum (V, T, E, O) of said given provider for the direct communication among the participants from the plurality of providers is divided into sections (V2V_V, V2V_T, V2V_E, V2V_O), with each provider of the plurality of providers having been assigned at least one section (V2V_V, V2V_T, V2V_E, V2V_O) of said part (V2V) of the dedicated spectrum (V, T, E, O) of the given provider, and where a base station (20T, 20V) of each of the plurality of providers other than said given provider will schedule the resources in its assigned section (V2V_V, V2V_T, V2V_E, V2V_O) of said dedicated spectrum (V,T, E, O) for the direct communications of its own participants, wherein the resource allocation management functionality for allocating a part of its dedicated spectrum for the direct communication among the participants from the plurality of providers is shifted from provider to provider from time slice (t_0, t_1, t_2, t_3) to time slice (t_0, t_1, t_2, t_3).
| 2. Method according to claim 1, wherein the resource allocation functionality is shifted from provider to provider from time slice (t_0, t_1, t_2, t_3) to time slice (t_0, t_1, t_2, t_3) in a round robin fashion, maximum rate queuing fashion or proportionally fair queuing fashion.
| 3. Method according to claim 1 or 2, wherein each provider announces to all other providers which part (V2V) of its dedicated spectrum (V, T, E, O) is reserved for the direct communication among the participants from the plurality of providers.
| 4. Method according to claim 3, wherein each provider announces to its own participants which section of the announced part (V2V) of the dedicated spectrum (V, T, E, O) is reserved for the direct communication among its own participants.
| 5. Method according to claim 3 or 4, wherein each provider will schedule resources in its section (V2V_V, V2V_T, V2V_E, V2V_O) of the part of (V2V) the dedicated spectrum (V, T, E, O) for its own participants by means of a scheduler in said provider owned base station (20). | The method involves providing base stations from multiple mobile communication providers and multiple participants from the multiple mobile communication providers in which each provider has assigned a dedicated spectrum (V,T,E,O) for resource allocation for its own participants and participants from the providers communicate directly among each other in particular with cooperative awareness messages. The resource allocation management functionality for the direct communication among the participants from the multiple providers is shifted from provider to provider from time slice (t-0-t-3) to time slice. INDEPENDENT CLAIMS are included for the following:a participant communication module; anda base station. Method for resource allocation in mobile communication system. The wireless vehicle communication network can help to reduce the weight of the vehicle by eliminating the need to install cables between the components which communicate. The drawing shows a schematic view illustrating how a portion of a dedicated spectrum in the LTE frequency bands which is allocated for communication is shifted from provider spectrum to provider spectrum per time slice. V,T,E,OSpectrumt-0-t-3Time slice |
Please summarize the input | METHOD FOR PLANNING A COOPERATIVE DRIVING MANEUVER, CORRESPONDING CONTROL UNIT AND VEHICLE EQUIPPED WITH A CONTROL UNIT AS WELL AS COMPUTER PROGRAMThe proposal concerns a method for planning a cooperative driving maneuver which may be used in the scenario of cooperative driving or autonomous driving. The method comprises the steps of observing the surroundings of a vehicle (10A), determining a planned trajectory (PT) the vehicle (10A) drives on for a certain amount of time, determining a desired trajectory (DT) different from the planned trajectory (PT) requiring a cooperative driving maneuver with at least one of the surrounding vehicles (10B, 10C). The solution according to the invention comprises the steps of determining a timeout value for the cooperative driving maneuver, starting a negotiation phase with the vehicles (10B, 10C) involved in the cooperative driving maneuver by sending a cooperative driving maneuver request message (MCM), waiting for the response messages from the involved vehicles (10B, 10C) and changing to the desired trajectory (DT) if the involved vehicles (10B, 10C) have accepted the desired trajectory (DT) before the negotiation phase has expired according to the timeout value.|1. Method for planning a cooperative driving maneuver, comprising the steps of observing the surroundings of a vehicle (10A), determining a planned trajectory (PT) the vehicle (10A) drives on for a certain amount of time, determining a desired trajectory (DT) different from the planned trajectory (PT) requiring a cooperative driving maneuver with at least one of the surrounding vehicles (10B, 10C), characterized by the steps of determining a timeout value (TO) for the cooperative driving maneuver, starting a negotiation phase with the vehicles (10B, 10C) involved in the cooperative driving maneuver by sending a maneuver coordination message (MCM), waiting for the response messages from the involved vehicles (10B, 10C) and changing to the desired trajectory (DT) if the involved vehicles (10B, 10C) have accepted the desired trajectory (DT) before the negotiation phase has expired according to the timeout value (TO).
| 2. Method according to claim 1, further comprising a step of determining a branch point (BP) corresponding to a point lying on the planned trajectory (PT) and the desired trajectory (DT) at which the planned (PT) and the desired trajectory (DT) separate and checking if the vehicle (10A) will reach the branch point (BP) before the negotiation phase is over according to the determined timeout value (TO) and if yes, terminating the planning of the cooperative driving maneuver and not sending out said maneuver coordination message (MCM).
| 3. Method according to claim 1 or 2, wherein for the step of determining a timeout value (TO) a step of determining the number of vehicles involved in the cooperative driving maneuver is performed and wherein the typical one-way trip time required for sending a message from one vehicle to another multiplied by the number of vehicles involved in the cooperative driving maneuver is added to the typical time for deciding on the acceptance or rejection of the cooperative driving maneuver to calculate the negotiation time (NT) for the cooperative driving maneuver.
| 4. Method according to claim 3, wherein the typical round trip time for the internal network transfer in the vehicle (10A) having sent out the maneuver coordination message (MCM) is added to the negotiation time (NT) in order to determine the total negotiation time.
| 5. Method according to claim 3 or 4, wherein in the vehicle (10A) having sent out the maneuver coordination message (MCM) the typical one-way trip time required for sending a message from one vehicle to another is adapted to the current estimation of the quality of service of the vehicle-to-vehicle radio communication system.
| 6. Method according to one of claims 3 to 5, wherein in the vehicle (10A) having sent out the cooperative driving maneuver request message the timeout value (TO) is set to the negotiation time (NT) when it is found that the requesting vehicle (10A) will reach the branch point (BP) before the negotiation time (NT) is over.
| 7. Method according to one of the previous claims, wherein the timeout value (TO) is entered into the payload field of the maneuver coordination message (MCM) to inform the involved vehicles (10B, 10C) about the timeout value for the negotiation phase of the cooperative driving maneuver.
| 8. Method according to one of the previous claims, wherein the planned trajectory (PT) and the desired trajectory (DT) is entered into the payload field of the maneuver coordination message (MCM) to inform the involved vehicles (10B, 10C) about the planned cooperative driving maneuver.
| 9. Method according to one of claims 6 to 8, wherein an involved vehicle (10B, 10C) performs a step of checking the timeout value (TO) in the received maneuver coordination message (MCM) and when it finds that the typical time for deciding on the acceptance or rejection of the cooperative driving maneuver is longer than the reported timeout value (TO), the involved vehicle (10B, 10C) will stop negotiating about the cooperative driving maneuver and transmit back to the requesting vehicle (10A) a message in which the cooperative driving maneuver is rejected.
| 10. Computing unit, characterized in that, the computing unit (180) is adapted to perform the steps of one of the previous claims.
| 11. Vehicle, characterized in that, the vehicle (30) is equipped with a computing unit (180) according to claim 10.
| 12. Computer program, characterized in that, the computer program comprises program steps, which when the program is processed by a computing unit (180), cause it to carry out the method according to one of claims 1 to 9. | The method involves observing the surroundings of a vehicle (10A). A planned trajectory (PT) is determined that the vehicle drives on for a certain amount of time. A desired trajectory (DT) id different from the planned trajectory requiring a cooperative driving maneuver with one of the surrounding vehicles (10B, 10C, 10D). A negotiation phase is started with the vehicles involved in the cooperative driving maneuver in response to the steps of determining a timeout value for the cooperative driving maneuver by sending a maneuver coordination message. Waiting is done for the response messages from the involved vehicles and changes are made to the desired trajectory if the involved vehicles have accepted the desired trajectory before the negotiation phase has expired according to the timeout value. INDEPENDENT CLAIMS are included for the following:a computing unit;a vehicle; anda computer program for planning a cooperative driving maneuver. Method for planning a cooperative driving maneuver in a vehicle e.g. mobile robot and driverless transport system, that is utilized in a motorway. Improves efficiency and comfort of automated driving. Ensures simple, reliable and efficient solution for cooperative driving maneuvers supported by vehicle-to-vehicle communication. The drawing shows a schematic view of the cooperative driving scenario. 10AVehicle10B, 10C, 10DSurrounding vehiclesDTDesired trajectoryPTPlanned trajectory |
Please summarize the input | System and method for using global electorate using regional certificate trust listThe invention claims a system, method and component for managing trust of a plurality of root certificate authority (CA) using both a voter and a regional certificate trust list (CTL). Accordingly, providing a system and method, the system and method is used for managing trust of multiple root CA, and in a more effective manner than the traditional known or can be used for the management. More specifically, the invention claims a system and a method for realizing V2I and/or V2X PKI technology.|1. A system for managing trust of a plurality of root certificate issuing mechanisms, which is used for using in the communication between at least two vehicles and vehicle (V2V) of a plurality of transport vehicles in the form of continuous broadcast of the basic safety (BSM), the system comprising: a transport vehicle device located on a transport vehicle of the plurality of transport vehicles; the transport vehicle device comprises a transceiver and at least one processor controlling the transceiver, wherein the at least one processor is configured to control the transceiver; so as to provide V2V communication through at least one communication link between the transport vehicle device of the transport vehicle and the transport vehicle device of other transport vehicles in the plurality of transport vehicles, wherein the communication link is provided by direct radio link, or through the communication of the mobile radio network; wherein the method uses the root certificate associated with the transport vehicle sending the BSM to perform digital signature to each BSM; the root certificate is used for protecting the transmission of the BSM on the communication link; and at least one area root certificate issuing mechanism in a plurality of area root certificate issuing mechanism; the area root certificate issuing mechanism determines whether the identity of the root certificate associated with the corresponding transport vehicle in the plurality of transport vehicles is legal for the jurisdiction of at least one area issuing mechanism.
| 2. The system according to claim 1, wherein at least two of the plurality of region awarding mechanisms use a certificate trust list that includes at least one common root certificate in each of at least two of a plurality of respective jurisdictions identified as a legitimate root certificate.
| 3. The system according to claim 2, wherein each region awarding mechanism is configured for modifying a certificate trust list listing legal certificates in their jurisdictions using an elector-based root management.
| 4. The system according to claim 3, wherein the voter-based root management is performed using a ballot having a endorsement, wherein the majority of the voters identified by the area awarding mechanism of adding or deleting the root certificate in the certificate trust list are sought to sign the ballot; so as to carry out endorsement or revocation to the root certificate.
| 5. The system according to claim 1, wherein the plurality of region awarding mechanisms are associated with respective jurisdictions that share a common border.
| 6. The system according to claim 1, wherein each of the root certificate issuing mechanisms issues a digital certificate including a root certificate, wherein the digital certificate certiates the ownership of the public key through the named body of the digital certificate.
| 7. The system according to claim 1, wherein each of the BSM comprises data specific to a transport vehicle, and the data includes a time, a position, a speed, and a forward direction of a transport vehicle to which the BSM is transmitted.
| 8. The system according to claim 1, wherein the digital signature of the BSM is used as an authentication of the correctness and reliability of the data contained in the BSM.
| 9. The system according to claim 1, wherein a digital signature of the BSM is analyzed by the transport vehicle safety application, prior to data prior to accessing the BSM by one or more transport vehicles safety on a transport vehicle in the plurality of transport vehicles that receive the BSM through V2V communication.
| 10. The system according to claim 9, wherein the one or more transport vehicle safety applications are autonomous or auxiliary driving applications.
| 11. The system according to claim 1, wherein a digital signature of the BSM is analyzed by the transport vehicle safety application, prior to receiving data of the BSM by one or more of the transport vehicles safety on the transport vehicle in the plurality of transport vehicles that are received through the V2V communication. .
| 12. The system according to claim 11, wherein the one or more transport vehicle safety applications are autonomous driving or auxiliary driving applications.
| 13. A method for managing trust of a plurality of root certificate issuing mechanisms, which is used for using in the form of continuous broadcast of the basic safety message (BSM) between at least two of the plurality of transport vehicles and the vehicle (V2V) communication, the method comprising: controlling the transmission of the V2V communication from the transport vehicle device located on the transport vehicle of the plurality of transport vehicles; the transport vehicle device comprises a transceiver and at least one processor controlling the transceiver, wherein the at least one processor controls the transceiver; so as to provide V2V communication through at least one communication link between the transport vehicle device of the transport vehicle and the transport vehicle device of other transport vehicles in the plurality of transport vehicles, wherein the communication link is provided by direct radio link, or through the communication of the mobile radio network; wherein the method uses the root certificate associated with the transport vehicle sending the BSM to perform digital signature to each BSM; the root certificate is used for protecting the transmission of the BSM on the communication link; wherein at least one of the plurality of area root certificate issuing mechanism determines whether the identity of the root certificate associated with the corresponding transport vehicle in the plurality of transport vehicles is legal to the jurisdiction of the at least one area issuing authority.
| 14. The method according to claim 13, wherein at least two of the plurality of region awarding mechanisms use a certificate trust list that includes at least one common root certificate in each of at least two of a plurality of respective jurisdictions identified as a legitimate root certificate.
| 15. The method according to claim 14, wherein each region awarding mechanism uses the root management based on the electorate to modify the certificate trust list listing the legal certificate in the jurisdiction.
| 16. The method according to claim 15, wherein the voter-based root management is performed using a ballot having a endorsement, wherein the majority of the voters identified by the area awarding mechanism of the root certificate are sought to be added or deleted in the certificate trust list. to sign the vote, so as to carry out endorsement or revocation to the root certificate.
| 17. The method according to claim 13, wherein the plurality of region awarding mechanisms are associated with respective jurisdictions that share a common border.
| 18. The method according to claim 13, wherein each of the root certificate issuing mechanisms issues a digital certificate including a root certificate, wherein the digital certificate certiates the ownership of the public key through the named body of the digital certificate.
| 19. The method according to claim 13, wherein each of the BSM comprises data specific to a transport vehicle, and the data includes a time, a position, a speed, and a forward direction of a transport vehicle transmitting the BSM.
| 20. The method according to claim 13, wherein the digital signature of the BSM is used as an authentication of the correctness and reliability of the data contained in the BSM.
| 21. The method according to claim 13, wherein the digital signature of the BSM is analyzed by the transport vehicle safety application, before one or more transport vehicles safety the transport vehicle of the plurality of transport vehicles receiving the BSM through the V2V communication are used to access the data of the BSM. .
| 22. The method according to claim 21, wherein the one or more transport vehicle safety applications are autonomous or auxiliary driving applications.
| 23. The method according to claim 13, wherein prior to receiving data of the BSM, one or more transport vehicles safety on a transport vehicle in the plurality of transport vehicles through V2V communication receiving the BSM; A digital signature of the BSM is analyzed by the transport vehicle safety
| 24. The method according to claim 23, wherein the one or more transport vehicle safety applications are autonomous driving or auxiliary driving applications.
| 25. A non-transitory computer readable medium, comprising a computer program with computer software code instruction, when the at least one computer processor to realize the code instruction computer software the computer software code instruction executes a method for managing trust of a plurality of root certificate issuing mechanism, which is used for using in the form of continuous broadcast of basic safety message (BSM) between at least two vehicles in a plurality of transport vehicles and vehicle (V2V) communication; the method comprises: controlling the transmission of the V2V communication from the transport vehicle device located on the transport vehicle of the plurality of transport vehicles; the transport vehicle device comprises a transceiver and at least one processor controlling the transceiver, wherein the at least one processor controls the transceiver; so as to provide V2V communication through at least one communication link between the transport vehicle device of the transport vehicle and the transport vehicle device of other transport vehicles in the plurality of transport vehicles, wherein the communication link is provided by direct radio link, or through the communication of the mobile radio network; wherein the method uses the root certificate associated with the transport vehicle sending the BSM to perform digital signature to each BSM; the root certificate is used for protecting the transmission of the BSM on the communication link; wherein at least one of the plurality of area root certificate issuing mechanism determines whether the identity of the root certificate associated with the corresponding transport vehicle in the plurality of transport vehicles is legal to the jurisdiction of the at least one area issuing authority.
| 26. The non-transitory computer-readable medium according to claim 25, wherein at least two of the plurality of area issuing mechanisms use a certificate trust list; The certificate trust list includes at least one common root certificate that is identified as a legal root certificate in each of at least two of a plurality of corresponding jurisdictions.
| 27. The non-transitory computer-readable medium according to claim 26, wherein each region awarding mechanism modifies a certificate trust list listing legal certificates in their jurisdictions using an elector-based root management.
| 28. The non-transitory computer-readable medium according to claim 27, wherein the voter-based root management is performed using a ballot having a endorsement, wherein the voter-based root management is performed by using a rewritten vote. seeking to add or delete the area issuing mechanism of the root certificate in the certificate trust list, inquiring the majority of the voters identified by the area issuing mechanism, to sign the vote, so as to carry out endorsement or revocation to the root certificate.
| 29. The non-transitory computer-readable medium according to claim 25, wherein the plurality of region awarding mechanisms are associated with respective jurisdictions that share a common border.
| 30. The non-transitory computer-readable medium according to claim 25, wherein the root certificate authority issues a digital certificate including a root certificate, wherein the digital certificate certiates the ownership of the public key through the named body of the digital certificate.
| 31. The non-transitory computer-readable medium according to claim 25, wherein the BSM includes data specific to a transport vehicle, and the data includes a time, a position, a speed, and a forward direction of a transport vehicle to which the BSM is transmitted.
| 32. The non-transitory computer-readable medium according to claim 25, wherein the digital signature of the BSM is used as an authentication of the correctness and reliability of the data contained in the BSM.
| 33. The non-transitory computer-readable medium according to claim 25, wherein the data of one or more transport vehicles safety on the transport vehicle of the plurality of transport vehicles receiving the BSM through the V2V communication is prior to the data of the BSM being accessed by the V2V communication; A digital signature of the BSM is analyzed by the transport vehicle safety
| 34. The non-transitory computer-readable medium according to claim 25, wherein the one or more transport vehicle safety applications are autonomous or auxiliary driving applications.
| 35. The non-transitory computer-readable medium according to claim 25, wherein prior to receiving data of the BSM by one or more transport vehicles safety on a transport vehicle in the plurality of transport vehicles received through the V2V communication, the transport vehicle is used to access the BSM; A digital signature of the BSM is analyzed by the transport vehicle safety
| 36. The non-transitory computer-readable medium according to claim 25, wherein the one or more transport vehicle safety applications are autonomous driving or auxiliary driving applications. | The system has a transportation vehicle equipment that is located on a transportation vehicle of multiple transportation vehicles. The transportation vehicle equipment includes a transceiver and a processor controlling the transceiver. The processor is configured to control the transceiver to provide V2V communication through communication link between the transportation vehicle equipment of the transportation vehicle and transportation vehicle equipment of other transportation vehicles of the multiple transportation vehicles. The communication link is provided through either direct radio link or communication or mobile- radio network. A regional root CA of multiple regional root CA dictate whether identities that associate root certificates with respective transportation vehicles of multiple transportation vehicles are legitimate for the jurisdiction for the regional authority. INDEPENDENT CLAIMS are included for the following:a method for managing trust across multiple root CA in V2V communication; anda non-transitory computer readable medium storing program for managing trust across multiple root CA in V2V communication. System for managing trust across multiple root certificate authorities (CA) in vehicle-to- vehicle (V2V) communication between transportation vehicles in the form of continuous broadcast of basic safety message (BSM). The system manages trust across multiple root CA using both electors and regional certificate trust lists (CTL) in a inventive way. The drawing shows the schematic diagram of the system for managing trust across multiple root CA in V2V communication. 110,120Jurisdictions115Credential management system117,127CTL125Security credential management system manager |
Please summarize the input | Method for planning the track of the vehicleA method for operating a navigation system with the method the destination of the autonomous vehicle of the driver guide to a desired path along a selected route, comprising: a step of obtaining information about the vehicle in the area around the autonomous vehicle, step to determine the trajectory of the vehicle based on the obtained information, and the vehicle of the track with the selected route path to the step of comparing. If it is determined that one vehicle in the vehicle once travel along the selected route path, currently travelling or will be travelling along the selected route path along the selected route path, then generates and outputs to the autonomous vehicle of the driver of the following instruction of the one vehicle.|1. A method for operating a navigation system with the method the destination for the autonomous vehicle of the driver guide to a desired path along a selected route, the method comprising: the information of the vehicle obtained in the region around the autonomous vehicle; based on the obtained information to determine the track of the vehicle around the autonomous vehicle of the; comparing the trajectory of the vehicle around the autonomous vehicle with the selected route path, and if it is determined that one vehicle in the vehicle around the autonomous vehicle: -been traveling along the selected route path, - currently running along the selected route path, or-will travel along the selected route path, then generates and outputs to the autonomous vehicle of the driver of the following instruction of the one vehicle.
| 2. The method according to claim 1, wherein the step of obtaining information about the other vehicle comprises: using sensor data generated by at least one sensor for detecting at least one position in the vehicle around the autonomous vehicle, speed, heading, steering signal and/or lane allocation, and based on at least one of in the vehicle of the autonomous vehicle around the at least one position of at least one of the speed, the heading, the turn signal and/or the lane in the assignment to determine the trajectory of the vehicle around the autonomous vehicle.
| 3. The method according to claim 1 or 2, wherein said step of obtaining the information about the vehicle around the autonomous vehicle comprises detecting, from the vehicle of the autonomous vehicle at the periphery of at least one of color and/or brand and/or manufacturer and and/or a turn signal and/or at least one of the type, and generating the instruction, said instruction comprises the detection of the vehicle in the autonomous vehicle to be followed to the color, the brand, the company, at least one of the turn signal and the type.
| 4. The process according to any one of said claims, wherein said step of obtaining the information about the vehicle around the autonomous vehicle includes using the vehicle-to-vehicle (V2V) interface in the step of data receiving to the autonomous vehicle from the vehicle.
| 5. The process according to any one of said claims, wherein using an audible signal and/or optical signal outputs said instruction to the driver.
| 6. The process according to any one of said claims, further comprising the step the obtained information about the around the autonomous vehicle of the vehicle transmitted to the server, wherein the server performs the step of determining the track of the vehicle around the autonomous vehicle of the.
| 7. The method according to claim 6, further comprising the step of transmitting to the server the selected route path, wherein the server performs the step comparing the trajectory of the vehicle around the autonomous vehicle with the selected route path.
| 8. A program, the program implementing at least one of the method according to claim 1 to 7, the program is executed on the computer.
| 9. The program according to claim 8, wherein the program is stored on a non-transitory computer readable medium accessible by the server.
| 10. A method for autonomous vehicle navigation system (1), the system comprising: a routing unit (11), the route selecting unit for selecting the autonomous vehicle to the destination route path, a sensor unit (12); the sensor unit for obtaining sensor data of the vehicle in the region around the autonomous vehicle of from a plurality of sensors, and a processing unit (13), said processing unit is used for determining the track of the vehicle around the autonomous vehicle and the for the determined track with the selected route path to compare; instruction unit (14), said instruction unit for generating instructions for following the route path, wherein if matching the selected route path of the track of a vehicle around the autonomous vehicle, then the instruction unit is configured to generate following instructions of the one vehicle, and output unit (15), said output unit outputs said instruction for the driver to the autonomous vehicle to follow the one vehicle.
| 11. The said system (1) according to claim 10, wherein the plurality of sensors comprises at least one of a camera, a radar, a laser radar, an inertial measurement unit (IMU) and a GNSS receiver, said GNSS receiver is used for receiving position coordinate of the autonomous vehicle from a global navigation satellite system (GNSS).
| 12. The said system (1) according to claim 10 or 11, wherein the output unit (15) includes a head-up display.
| 13. at least one system (1) according to claim 10 to 12, further comprising a receiving unit (16), said receiving unit for receiving, from the other vehicle receives information about the vehicle in the area around the autonomous vehicle, wherein the processing unit is configured for based on the received information to determine the trajectory and/or said instruction unit is configured to generates the instruction based on the received information.
| 14. at least one system (1) according to claim 10 to 13, further comprising a server, the server being configured to communicate with the routing unit (11), the sensor unit (12), the processing at least one of communicate and exchange data in unit (13), said instruction unit (14) and the output unit (15).
| 15. at least one system (1) according to claim 10 to 14, wherein the processing unit (13) is located at the server. | The method involves obtaining information about vehicles in a region around the ego vehicle. Determine trajectories of the vehicles around the ego vehicle based on the obtained information. Compare the trajectories of the vehicles around the ego vehicle with the selected route path of the ego vehicle. If it is determined that one of the vehicles around the ego vehicle was driving, is currently driving, or will be driving along the selected route path then generate and output an instruction to the driver of the ego vehicle to follow the one vehicle. Detect one of a position, a velocity, a heading, a turn signal and a lane assignment of one of the vehicles around the ego vehicle using sensor data generated by one sensor;. INDEPENDENT CLAIMS are included for the following:a program implementing a method; anda navigation system for an ego vehicle. Method for operating a navigation system for guiding a driver of an ego vehicle to a desired destination along a selected route path. The server does not need to be a single centrally managed piece of hardware but may be implemented as a cloud computing network with the advantage of redundant components and simplified maintenance. The drawing shows a block representation of a navigation system. 11Routing unit12Sensor unit13Processing unit14Instruction unit16Reception unit |
Please summarize the input | Dynamically placing an internet protocol anchor point based on a user device and/or an applicationA device determines whether an application, utilized by a user device and associated with a network, is a low latency application or a best effort application. The device designates a first network device or a second network device as a designated network device to be an IP anchor point for the application based on a set of rules. The first network device is designated as the designated network device when the application is the low latency application, or the second network device is designated as the designated network device when the application is the best effort application. The device provides, to the user device, information informing the user device that the designated network device is to be the IP anchor point for the application, and provides, to the network, information instructing the network to utilize the designated network device as the IP anchor point for the application.What is claimed is:
| 1. A device, comprising:
one or more memories; and
one or more processors, communicatively coupled to the one or more memories, to:
receive information indicating that a user device is utilizing an application associated with a network;
determine whether the application is a low latency application or a best effort application;
designate a first network device of the network or a second network device of the network as a designated network device to be an Internet protocol (IP) anchor point for the application based on a set of rules and based on determining whether the application is the low latency application or the best effort application,
wherein the first network device is designated as the designated network device to be the IP anchor point for the application when the application is the low latency application, or
wherein the second network device is designated as the designated network device to be the IP anchor point for the application when the application is the best effort application,
wherein the set of rules includes two one or more of:
a rule indicating that the IP anchor point is to be as close to the user device as possible,
a rule indicating that the IP anchor point is to include a threshold amount of processing resources and memory resources,
a rule indicating that the IP anchor point is to be associated with a serving base station,
a rule indicating a timing advance distance between the IP anchor point and the user device, or
a rule indicating an operational pathloss between the IP anchor point and the user device, and
the one or more processors, when designating the first network device as the designated network device, are to:
apply a weight to each rule, of the set of rules, to generate a weighted set of rules,
determine scores for a plurality of first network devices based on the weighted set of rules, and
select the first network device, from the plurality of first network devices, based on a score for the first network device being greater than scores associated with one or more other network devices from the scores for the plurality of first network devices;
provide, to the user device, information informing the user device that the designated network device is to be the IP anchor point for the application; and
provide, to the network, information instructing the network to utilize the designated network device as the IP anchor point for the application to permit the user device to utilize the designated network device as the IP anchor point for the application.
| 2. The device of claim 1, wherein the one or more processors are further to:
provide, to the network, information instructing the network to utilize a third network device of the network as a control plane anchor point for the application of the user device.
| 3. The device of claim 1, wherein:
the first network device is a user plane function (UPF) device provided at an edge of the network, and
the second network device is a UPF device provided at a central location of the network.
| 4. The device of claim 1, wherein:
the low latency application includes one or more of:
an autonomous driving application,
a real-time vehicle-to-vehicle (V2V) communication application, or
an application that delivers video; and
the best effort application includes one or more of:
an application to enable a web download, or
an application to access the Internet.
| 5. The device of claim 1, wherein the one or more processors are further to:
receive, from the user device, information indicating that a user stopped utilizing the application; and
provide, to the network, information instructing the network to stop utilizing the designated network device as the IP anchor point for the application.
| 6. A non-transitory computer-readable medium storing instructions, the instructions comprising:
one or more instructions that, when executed by one or more processors of a device, cause the one or more processors to:
receive information indicating that a user device is utilizing an application associated with a network;
determine whether the application is a low latency application or a best effort application;
designate a first network device of the network or a second network device of the network as a designated network device to be an Internet protocol (IP) anchor point for the application based on a set of rules and based on determining whether the application is the low latency application or the best effort application,
the first network device being designated as the designated network device to be the IP anchor point for the application when the application is the low latency application, or
the second network device being designated as the designated network device to be the IP anchor point for the application when the application is the best effort application,
wherein the set of rules includes two one or more of:
a rule indicating that the IP anchor point is to be as close to the user device as possible,
a rule indicating that the IP anchor point is to include a threshold amount of processing resources and memory resources,
a rule indicating that the IP anchor point is to be associated with a serving base station,
a rule indicating a timing advance distance between the IP anchor point and the user device, or
a rule indicating an operational pathloss between the IP anchor point and the user device, and
the one or more instructions, that cause the one or more processors to designate the first network device as the designated network device, cause the one or more processors to:
apply a weight to each rule, of the set of rules, to generate a weighted set of rules,
determine scores for a plurality of first network devices based on the weighted set of rules, and
select the first network device, from the plurality of first network devices, based on a score for the first network device being greater than scores associated with one or more other network devices from the scores for the plurality of first network devices;
provide, to the user device, information informing the user device that the designated network device is to be the IP anchor point for the application; and
provide, to the network, information instructing the network to utilize the designated network device as the IP anchor point for the application to permit the user device to utilize the designated network device as the IP anchor point for the application.
| 7. The non-transitory computer-readable medium of claim 6, wherein the instructions further comprise:
one or more instructions that, when executed by the one or more processors, cause the one or more processors to:
provide, to the network, information instructing the network to utilize a third network device of the network as a control plane anchor point for the application of the user device.
| 8. The non-transitory computer-readable medium of claim 6, wherein:
the first network device is a user plane function (UPF) device provided at an edge of the network, and
the second network device is a UPF device provided at a central location of the network.
| 9. The non-transitory computer-readable medium of claim 6, wherein:
the low latency application includes one or more of:
an autonomous driving application,
a real-time vehicle-to-vehicle (V2V) communication application, or
an application that delivers video; and
the best effort application includes one or more of:
an application to enable a web download, or
an application to access the Internet.
| 10. The non-transitory computer-readable medium of claim 6, wherein the instructions further comprise:
one or more instructions that, when executed by the one or more processors, cause the one or more processors to:
receive, from the user device, information indicating that a user stopped utilizing the application; and
provide, to the network, information instructing the network to stop utilizing the designated network device as the IP anchor point for the application.
| 11. A method, comprising:
receiving, by a device, information indicating that a user device is utilizing an application associated with a network;
determining, by the device, whether the application is a low latency application or a best effort application;
designating, by the device, a first network device of the network or a second network device of the network as a designated network device to be an Internet protocol (IP) anchor point for the application based on a set of rules and based on determining whether the application is the low latency application or the best effort application,
the first network device being designated as the designated network device to be the IP anchor point for the application when the application is the low latency application, or
the second network device being designated as the designated network device to be the IP anchor point for the application when the application is the best effort application,
wherein the set of rules includes two one or more of:
a rule indicating that the IP anchor point is to be as close to the user device as possible,
a rule indicating that the IP anchor point is to include a threshold amount of processing resources and memory resources,
a rule indicating that the IP anchor point is to be associated with a serving base station,
a rule indicating a timing advance distance between the IP anchor point and the user device, or
a rule indicating an operational pathloss between the IP anchor point and the user device, and
designating the first network device as the designated network device comprising:
applying a weight to each rule, of the set of rules, to generate a weighted set of rules,
determining scores for a plurality of first network devices based on the weighted set of rules, and
selecting the first network device, from the plurality of first network devices, based on a score for the first network device being greater than scores associated with one or more other network devices from the scores for the plurality of first network devices;
providing, by the device and to the user device, information informing the user device that the designated network device is to be the IP anchor point for the application; and
providing, by the device and to the network, information instructing the network to utilize the designated network device as the IP anchor point for the application to permit the user device to utilize the designated network device as the IP anchor point for the application.
| 12. The method of claim 11, further comprising:
providing, to the network, information instructing the network to utilize a third network device of the network as a control plane anchor point for the application of the user device.
| 13. The method of claim 11, wherein:
the first network device is a user plane function (UPF) device provided at an edge of the network, and
the second network device is a UPF device provided at a central location of the network.
| 14. The method of claim 11, wherein:
the low latency application includes one or more of:
an autonomous driving application,
a real-time vehicle-to-vehicle (V2V) communication application, or
an application that delivers video; and
the best effort application includes one or more of:
an application to enable a web download, or
an application to access the Internet.
| 15. The method of claim 11, further comprising:
receiving, from the user device, information indicating that the user stopped utilizing the application; and
providing, to the network, information instructing the network to stop utilizing the designated network device as the IP anchor point for the application.
| 16. The device of claim 1, wherein the one or more processors, when determining whether the application is the low latency application or the best effort application, are to:
determine that the application is the low latency application based on a maximum allowed latency for the application.
| 17. The device of claim 1, wherein the one or more processors, when applying the weight to each rule of the set of rules, are to:
apply different weights to different rules based on one or more of:
information associated with the user device,
information associated with the application, or
information associated with the network.
| 18. The non-transitory computer-readable medium of claim 6, wherein the one or more instructions, that cause the one or more processors to determine whether the application is the low latency application or the best effort application, cause the one or more processors to:
determine that the application is the low latency application based on a maximum allowed latency for the application.
| 19. The non-transitory computer-readable medium of claim 6, wherein the one or more instructions, that cause the one or more processors to apply the weight to each rule of the set of rules, cause the one or more processors to:
apply different weights to different rules based on one or more of:
information associated with the user device,
information associated with the application, or
information associated with the network.
| 20. The method of claim 11, wherein determining whether the application is the low latency application or the best effort application comprises:
determining that the application is the low latency application based on a maximum allowed latency for the application. | The device has a memories and a processors is coupled to the memories to receive information (510) indicating that a user device is utilizing an application. The application is a low latency application or a best effort application is determined (520). A first network device of the network or a second network device of the network as a designated network device to be an internet protocol (IP) anchor point for the application is designated (530) based on determining whether the application is the low latency application or the best effort application. The information informing the user device that the designated network device is to be the IP anchor point is provided (540) to the user device for the application. The information instructing the network to utilize the designated network device as the IP anchor point is provided (550) to the network for the application which permits the user device to utilize the designated network device as the IP anchor point for the application. INDEPENDENT CLAIMS are included for the following:a non-transitory computer-readable medium storing instructions for dynamically placing an IP anchor point based on a user device and an application; anda method for dynamically placing an IP anchor point based on a user device and an application. Device such as user equipment, mobile phone e.g. smart phone and radiotelephone, laptop computer, tablet computer, desktop computer, handheld computer, gaming device, wearable communication device e.g. smart watch and pair of smart glasses, mobile hotspot device, fixed wireless access device, or customer premises equipment. The anchor point platform can apply a greater weight to rule R2 than rules R3-R5 since rule R2 can ensure that the IP anchor point is an edge user plane function (UPF) with sufficient resources to handle the low latency application. The different stages of the process for dynamically placing an IP anchor point based on a user device and an application are automated, which can remove human subjectivity and waste from the process, and which can improve speed and efficiency of the process and conserve computing resources. The drawing shows a flow chart illustrating a process for dynamically placing an IP anchor point based on a user device and an application. 510Step for receiving information520Step for determining application530Step for designating a first or second network device540Step for providing information informing the user device550Step for providing information instructing the network |
Please summarize the input | Systems and methods for transforming high-definition geographical map data into messages for vehicle communicationsA device may receive three-dimensional geographical map data for a geographical region associated with a vehicle device of a vehicle and may process the three-dimensional geographical map data, with a data model, to transform the three-dimensional geographical map data into transformed geographical map data with a format that corresponds to a particular standard. The device may generate a message based on the transformed geographical map data and may cause the message to be provided to the vehicle device. The device may perform one or more actions based on the message.What is claimed is:
| 1. A method, comprising:
receiving, by a device, three-dimensional geographical map data for a geographical region associated with a vehicle device of a vehicle;
processing, by the device, the three-dimensional geographical map data, with a data model, to:
analyze different layers of the three-dimensional geographical map data,
identify a portion of the three-dimensional geographical map data based on analyzing the different layers of the three-dimensional geographical map data, and
transform the portion of the three-dimensional geographical map data into transformed geographical map data with a format that corresponds to a particular standard;
generating, by the device, a message based on the transformed geographical map data;
causing, by the device, the message to be provided to the vehicle device; and
performing, by the device, one or more actions based on the message.
| 2. The method of claim 1, wherein the particular standard includes a Society of Automotive Engineers J2735 standard.
| 3. The method of claim 1, wherein causing the message to be provided to the vehicle device comprises one or more of:
causing the message to be provided to the vehicle device via a multi-access edge computing device associated with the vehicle device;
causing the message to be provided to the vehicle device via a registration representational state transfer application program interface; or
causing the message to be provided to the vehicle device via a cellular vehicle-to-everything message broker.
| 4. The method of claim 1, wherein performing the one or more actions comprises:
receiving new three-dimensional geographical map data associated with the geographical region;
updating the message based on the new three-dimensional geographical map data and to generate an updated message; and
causing the updated message to be provided to the vehicle device.
| 5. The method of claim 1, wherein performing the one or more actions comprises:
generating an alert based on the message; and
providing the alert to the vehicle device.
| 6. The method of claim 1, wherein performing the one or more actions comprises:
determining a location of the vehicle based on the message; and
causing an emergency service to be dispatched to the location of the vehicle.
| 7. The method of claim 1, wherein the transformed geographical map data includes map data identifying one or more of:
one or more traffic lanes associated with the geographical region;
one or more intersections associated with the geographical region;
one or more traffic signals associated with the geographical region;
one or more sidewalks associated with the geographical region; or
one or more pedestrian lanes associated with the geographical region.
| 8. A device, comprising:
one or more processors configured to:
receive three-dimensional geographical map data for a geographical region associated with a vehicle device of a vehicle;
process the three-dimensional geographical map data, with a data model, to:
analyze different layers of the three-dimensional geographical map data,
identify a portion of the three-dimensional geographical map data based on analyzing the different layers of the three-dimensional geographical map data, and
transform the portion of the three-dimensional geographical map data into transformed geographical map data with a format that corresponds to a particular standard;
generate a message based on the transformed geographical map data;
cause the message to be provided to the vehicle device via a multi-access edge computing device associated with the vehicle device, via a registration representational state transfer application program interface, or via a cellular vehicle-to-everything message broker; and
perform one or more actions based on the message.
| 9. The device of claim 8, wherein the one or more processors, to perform the one or more actions, are configured to:
receive feedback from the vehicle device based on the message; and
retrain the data model based on the feedback.
| 10. The device of claim 8, wherein the one or more processors, to perform the one or more actions, are configured to one or more of:
cause the vehicle device to determine an actual location of the vehicle based on the message;
cause the vehicle device to provide an actual location of the vehicle to one or more other vehicles based on the message;
cause the vehicle device to position the vehicle in a traffic lane based on the message; or
cause traffic analytics for the geographical region to be generated based on the message.
| 11. The device of claim 8, wherein the transformed geographical map data includes map data identifying one or more of:
one or more traffic lanes associated with the geographical region;
one or more intersections associated with the geographical region;
one or more traffic signals associated with the geographical region;
one or more sidewalks associated with the geographical region; or
one or more pedestrian lanes associated with the geographical region.
| 12. The device of claim 8, wherein the vehicle includes one or more of:
an autonomous robot,
a semi-autonomous vehicle,
an autonomous vehicle, or
a non-autonomous vehicle.
| 13. The device of claim 8, wherein the one or more processors, to perform the one or more actions, are configured to:
calculate traffic analytics for the geographical region based on the message; and
provide the traffic analytics to an entity associated with managing traffic for the geographical region.
| 14. The device of claim 8, wherein the one or more processors, to perform the one or more actions, are configured to:
receive new three-dimensional geographical map data associated with the geographical region; and
update the message based on the new three-dimensional geographical map data.
| 15. A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:
one or more instructions that, when executed by one or more processors of a device, cause the device to:
receive three-dimensional geographical map data for a geographical region associated with a vehicle device of a vehicle;
process the three-dimensional geographical map data, with a data model, to:
analyze different layers of the three-dimensional geographical map data,
identify a portion of the three-dimensional geographical map data based on analyzing the different layers of the three-dimensional geographical map data, and
transform the portion of the three-dimensional geographical map data into transformed geographical map data with a format that corresponds to a particular standard;
generate a message based on the transformed geographical map data;
cause the message to be provided to the vehicle device; and
perform one or more actions based on the message.
| 16. The non-transitory computer-readable medium of claim 15, wherein the one or more instructions, that cause the device to perform the one or more actions, cause the device to:
receive new three-dimensional geographical map data associated with the geographical region;
update the message based on the new three-dimensional geographical map data and to generate an updated message;
cause the updated message to be provided to the vehicle device; and
perform one or more additional actions based on the updated message.
| 17. The non-transitory computer-readable medium of claim 15, wherein the one or more instructions, that cause the device to perform the one or more actions, cause the device to:
generate an alert based on the message; and
provide the alert to a vehicle located in the geographical region.
| 18. The non-transitory computer-readable medium of claim 15, wherein the one or more instructions, that cause the device to perform the one or more actions, cause the device to:
determine a location of a vehicle in the geographical region based on the message; and
cause an emergency service to be dispatched to the location of the vehicle.
| 19. The non-transitory computer-readable medium of claim 15, wherein the one or more instructions, that cause the device to perform the one or more actions, cause the device to:
calculate traffic analytics for the geographical region based on the message; and
provide the traffic analytics to an entity associated with managing traffic for the geographical region.
| 20. The non-transitory computer-readable medium of claim 15, wherein the transformed geographical map data includes map data identifying one or more of:
one or more traffic lanes associated with the geographical region;
one or more intersections associated with the geographical region;
one or more traffic signals associated with the geographical region;
one or more sidewalks associated with the geographical region; or
one or more pedestrian lanes associated with the geographical region. | The method involves receiving three-dimensional geographical map data for a geographical region associated with a vehicle device of a vehicle (410). The 3D map data is processed with a data model to transform the map data (420) into transformed map data with a format that corresponds to a particular standard e.g. Society of Automotive Engineers J2735 standard. A message is generated based on the transformed data (430). The message is caused to be provided to the vehicle device (440). A set of actions is performed by the device based on message (450). INDEPENDENT CLAIMS are included for: (1) a device, comprising: one or more processors configured to: receive three dimensional geographical map data for a geographical region associated with a vehicle device of a vehicle\; (2) a non-transitory computer-readable medium storing a set of instructions. Method for generating a message based on the transformed geographical map data. The method utilizes a vehicle-to-vehicle (V2V) communication system to allow a user to communicate with the vehicle efficiently, and allows the V2V communications system to enable the user to receive information from the vehicle and the vehicle to communicate information to the user in a reliable manner. The drawing shows a flow diagram of the method for generating a message based on the transformed geographical map data.410Receiving Three Dimensional Geographical Map Data for a Geographical Region 420Processing the Three Dimensional Geographical Map Data with a Data Model 430Generating a Message based on the transformed geographical map data 440Causing the message to be provided to the vehicle device 450Performing one or more actions based on the message |
Please summarize the input | Autonomous vehicles and systems thereforAbstract Title: Seatless vehicle with mobile charging
A seatless autonomous vehicle which has the means to receive data, a battery 6 to power the vehicle and a charger arranged to charge a battery of another electrically powered vehicle. The vehicle may have a display for sending messages to other road users, the display may be mounted on the front or rear of the vehicle. The vehicle may have means to monitor its surroundings and send the information to other vehicles and/or back to a central hub. The vehicle may be a part of a fleet of vehicles which can communicate with each other (V2V) and a central hub. The hub may be cloud based. The battery of the vehicle may have apices at the hubs of the wheels. |
CLAIMS
1. A seatless road vehicle having an autonomous mode of operation, the vehicle including a wireless receiver for receiving data, a battery arranged to power the vehicle and a charger arranged to charge a battery of another, electrically powered, vehicle.
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2. A vehicle according to claim 1, including an electronic illuminated display for providing instructions or information to drivers of other vehicles.
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3. A vehicle according to claim 2, wherein the illuminated display is arranged on a rear surface and/or a front surface of the vehicle.
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4. A vehicle according to claim 1, 2 or 3, including monitoring means for monitoring traffic conditions, road conditions and/or environmental conditions, and a transmitter for transmitting information gathered by the monitoring means.
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5. A vehicle according to any preceding claim, including four wheels, wherein, viewed in plan, the battery occupies at least 60% of a rectangle having apices at hubs of the wheels.
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6, A system comprising a fleet of vehicles, each according to any preceding claim, wherein each vehicle is arranged to communicate data including at least a location of the vehicle to at least one other vehicle in the fleet.
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7. A system according to claim 6, including a control station arranged to coordinate control of the vehicles.
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8. A system according to claim 7, wherein the control station is cloud based. | The seatless road vehicle comprises a wireless receiver for receiving data, where a battery (6) is arranged to power the vehicle, and a charger is arranged to charge a battery of another electrically powered vehicle. An electronic illuminated display is used for providing instructions or information to drivers of other vehicles. The illuminated display is arranged on a rear surface and a front surface of the vehicle. A monitoring unit is provided for monitoring traffic conditions, road conditions or environmental conditions. A transmitter is provided for transmitting information gathered by the monitoring unit. An INDEPENDENT CLAIM is included for a system comprises a fleet of vehicles. Seatless autonomous road vehicle for use with autonomous vehicle system (claimed). The seatless road vehicle has an autonomous mode of operation, and can rescue an electric vehicle with a flat battery, and electronic illuminated display provides instructions or information to drivers of other vehicles, and reduces the traffic congestion on highways. The drawing shows a schematic view of a battery and a motor of the vehicle.4Wheels 6Battery 8Motor |
Please summarize the input | For example, the system and method for dynamic management and control of several WI-FI radio in the network of the moving object containing an autonomous vehicleA system for communication is provided, where the system comprises a context broker configured to gather context information for use in managing a plurality of radios, a Wi-Fi radio manager configured to manage the plurality of radio managers using the context information, and a plurality of radios, where each of the plurality of radio managers is configured to manage one of the plurality of radios for communication with another electronic device.|1. In a communication system, a context broker configured to collect context information to be used when managing multiple radios; The context information from the context broker is used; a Wi-Fi radio manager configured to manage a plurality of radio managers is provided; and a plurality of radio stations are provided. Each of the plurality of radio managers is configured to use the radio configuration information received from the Wi-Fi radio manager; to communicate with other electronic devices; and to manage each of the plurality of radio stations; and to provide a method for managing the radio communication system. The context information includes both the mobility information about the mobile vehicle associated with the context broker and the necessary items and/or requirements associated with the application or service; A communication system that manages a plurality of radio managers includes the configuration of at least one radio based on both the mobility information and the necessary items and/or the requirements associated with the application or the service.
| 2. In the system described in claim 1, the context broker, the Wi-Fi radio manager, the plurality of radio managers, and the plurality of radio stations are located in the moving vehicle.
| 3. In the system described in claim 2, the context information includes: the position of the mobile vehicle; the speed of the moving vehicle; the moving direction of the moving vehicle; the processing capability of the moving vehicle; and at least one of the resources for at least one vehicle outside the moving vehicle.
| 4. In the system described in Claim 2, the context information includes infrastructure information about one or more infrastructure.
| 5. In the system described in claim 4, the infrastructure information includes information about a neighbor access point (AP), a current path of the mobile vehicle, and one or more of the neighboring vehicles.
| 6. In the system described in Claim 2, at least one of the plurality of radios is configured to connect an electronic device in the mobile vehicle to a network outside of the mobile vehicle.
| 7. In the system described in Claim 2, the Wi-Fi radio manager is configured to provide the service of the mobile vehicle and the need for an application, and to supply one of the plurality of radio-specific power sources.
| 8. In the system described in Claim 2, the Wi-Fi radio manager is configured to determine whether or not to turn on a particular power supply that has been powered off because of a context trigger that requires the use of one or more Wi-Fi radios among the plurality of radio stations. The context trigger is the system by the context information from the inside of the moving vehicle; the neighborhood of the moving vehicle; one or more APs, or the cloud server.
| 9. In the system described in Claim 8, when one of the radio stations is powered up, the specific radio is based on the context information about the particular radio; vehicle-infrastructure (V2I) connection mode; A system that is set to vehicle-vehicle (V2V) connection mode; V2I scanning mode; V2V scanning mode; or access point (AP) mode.
| 10. In the system described in claim 9, the system is configured to use at least one threshold value to determine when the Wi-Fi radio manager changes the configuration of the particular radio.
| 11. In the system described in Claim 8, the respective weights are applied to the moving vehicle; the neighborhood of the surrounding of the moving vehicle; and the context information from the one or more APs and the cloud servers.
| 12. In the system described in Claim 1, at least a portion of the contextual information is received from the cloud server.
| 13. In the system described in claim 1, the Wi-Fi radio manager is configured to turn on or power off a specific one of the two or more radio stations.
| 14. A communication method; a step of collecting context information to be used when managing a plurality of radios by a context broker; By using context information from the context broker, by using context information from the context broker, by a Wi-Fi radio manager configured to manage multiple radio managers, a step to determine how one of the plurality of radios should be configured, and; By the Wi-Fi radio manager, based on context information from the context broker, the configuration of the specific radio is presented to the radio manager. The method includes the steps of: using the radio configuration information received from the Wi-Fi radio manager; and configuring the particular radio for communication with other electronic devices. The context information includes both the mobility information about the mobile vehicle associated with the context broker and the necessary items and/or requirements associated with the application or service; The method for communication includes managing the plurality of radio managers, including the configuration of at least one radio based on both the mobility information and the necessary items and/or the requirements associated with the application or the service.
| 15. In the method described in the claim 14, the configuration is a method for considering one or more of a signal power; a received signal strength indication (RSSI), an interference; a channel; and a frequency.
| 16. In the method described in the claim 14, the context broker, the Wi-Fi radio manager, the plurality of radio managers, and the plurality of radio stations are provided in the moving vehicle.
| 17. In the method described in the claim 16, the context information includes: the position of the moving vehicle; the speed of the moving vehicle; the moving method of the moving vehicle; the processing capability of the moving vehicle; and one or more of the resources for at least one vehicle outside the moving vehicle.
| 18. In the method described in the claim 16, the context information includes infrastructure information for one or more infrastructure.
| 19. In the method described in the claim 16, the Wi-Fi radio manager is configured to determine whether or not a power source is to be turned on for a context trigger that requires the use of one or more Wi-Fi radios within the plurality of radio stations. The context trigger is a method according to context information from one or more neighboring areas around the mobile vehicle; one or more neighboring areas around the mobile vehicle; or a cloud server.
| 20. In the method described in the claim 16, the respective weights are applied to the moving vehicle, the neighborhood of the surrounding of the moving vehicle, the one or more APs, and the context information from each of the cloud servers. | The system has a context broker that is configured to gather context information for use in managing multiple radios. A WiFi radio manager is configured to manage multiple radio managers using the context information from the context broker. Each of multiple radio managers is configured to manage a respective one of multiple radios for communication with another electronic device using radio configuration information received from the WiFi radio manager. The context broker, the WiFi radio manager, multiple radio managers, and multiple radios are in a mobile vehicle (700). The context information comprises a location of the mobile vehicle, a speed of the mobile vehicle, a direction of travel of the mobile vehicle, processing capabilities of the mobile vehicle, and resources for vehicle external to the mobile vehicle. An INDEPENDENT CLAIM is included for a method for dynamic management and control of WiFi radio in network of mobile vehicle. System for dynamic management and control of WiFi radio in network of mobile vehicle. Uses include but are not limited to bus, truck, boat, forklift, human-operated vehicle, autonomous and/or remote controlled vehicles, boat, ship, speedboat, tugboat, barges, submarine, drone, airplane, and satellite, used in port, harbor, airport, factory, plantation, and mine. The communication network allows a port operator to improve the coordination of the ship loading processes and increase the throughput of the harbor by gathering real-time information on the position, speed, fuel consumption and carbon dioxide emissions of the vehicles. The communication network can operate in multiple modalities comprising various fixed nodes and mobile nodes, provide connectivity in dead zones or zones with difficult access, and reduce the costs for maintenance and accessing the equipment for updating/upgrading. The overall cost consumption per distance, time and vehicle/fleet is reduced. The data from vehicle is offloaded in faster and/or cheaper transfer manner and the overall quality experienced per application, service, or user is increased. The vehicle increases the data offloaded and reduces the costs or time of sending data over expensive or slow technologies. The time to first byte (TTFB) where the next available WiFi network detected is reduced. The drawing shows a block diagram of the communication devices in a vehicle. 700Mobile vehicle702,704,710,712,714Communication devices |
Please summarize the input | Systems and methods for vehicular positioning based on wireless fingerprinting data in a network of moving things including, for example, autonomous vehiclesCommunication network architectures, systems and methods for supporting a network of mobile nodes. As a non-limiting example, various aspects of this disclosure provide communication network architectures, systems, and methods for supporting a dynamically configurable communication network comprising a complex array of both static and moving communication nodes (e.g., the Internet of moving things). For example, systems and method for vehicular positioning based on wireless fingerprinting data in a network of moving things including, for example, autonomous vehicles.What is claimed is:
| 1. A method of vehicular positioning of nodes of a radio frequency (RF) wireless network comprising a plurality of nodes disposed at respective fixed locations and a plurality of mobile nodes that reside in respective vehicles that move within a service area of the wireless network, and wherein each node of the plurality of nodes comprises one or more communication interfaces configured for scanning an RF wireless environment of the respective node, the method comprising:
periodically receiving respective wireless fingerprint sample data generated by each mobile node of the plurality of mobile nodes, the wireless fingerprint sample data comprising data elements characterizing RF signals received by the mobile node from RF signal sources during scanning of the RF wireless environment of the mobile node and a corresponding geographic location within the service area at which the RF signals were received;
forming a collection of the wireless fingerprint sample data received from the plurality of mobile nodes;
receiving a request for an estimated geographic location of a particular mobile node of the plurality of mobile nodes;
searching the collection using a wireless snapshot comprising data elements characterizing RF signals received in a current RF wireless environment of the particular mobile node, to identify wireless fingerprint samples of the collection that match the data elements of the wireless snapshot; and
calculating an estimated location of the particular mobile node using the identified wireless fingerprint sample data.
| 2. The method according to claim 1, wherein each mobile node of the plurality of mobile nodes comprises a wireless access point configured to provide wireless Internet access to end-user devices.
| 3. The method according to claim 1, wherein each node of the plurality of nodes periodically wirelessly broadcasts its current geographic location to other nodes of the network.
| 4. The method according to claim 1, wherein the scanning of RF signals within the service area of the wireless network is without regard to a route of travel of a vehicle in which the mobile node resides.
| 5. The method according to claim 1, the method further comprising:
adding the wireless snapshot and a respective estimated location of the particular mobile node to the collection as a wireless fingerprint sample, if the search fails to identify at least one wireless fingerprint sample that matches the wireless snapshot.
| 6. The method according to claim 1, wherein the collection is indexed according to one or more of the data elements of each wireless fingerprint sample that characterize a signal source.
| 7. The method according to claim 1, wherein the one or more communication interfaces are configured to scan and characterize RF signal sources comprising an RF signal of an IEEE 802.11p compliant vehicle to vehicle wireless communication standard and an RF signal compliant with a commercial cellular communication standard.
| 8. A non-transitory computer-readable medium on which is stored instructions executable by one or more processors, the executable instructions causing the one or more processors to perform a method of vehicular positioning of nodes of a radio frequency (RF) wireless network comprising a plurality of nodes disposed at respective fixed locations and a plurality of mobile nodes that reside in respective vehicles that move within a service area of the wireless network, and wherein each node of the plurality of nodes comprises one or more communication interfaces configured for scanning an RF wireless environment of the respective node, the method comprising:
periodically receiving respective wireless fingerprint sample data generated by each mobile node of the plurality of mobile nodes, the wireless fingerprint sample data comprising data elements characterizing RF signals received by the mobile node from RF signal sources during scanning of the RF wireless environment of the mobile node and a corresponding geographic location within the service area at which the RF signals were received;
forming a collection of the wireless fingerprint sample data received from the plurality of mobile nodes;
receiving a request for an estimated geographic location of a particular mobile node of the plurality of mobile nodes;
searching the collection using a wireless snapshot comprising data elements characterizing RF signals received in a current RF wireless environment of the particular mobile node, to identify wireless fingerprint samples of the collection that match the data elements of the wireless snapshot; and
calculating an estimated location of the particular mobile node using the identified wireless fingerprint sample data.
| 9. The non-transitory computer-readable medium according to claim 8, wherein each mobile node of the plurality of mobile nodes comprises a wireless access point configured to provide wireless Internet access to end-user devices.
| 10. The non-transitory computer-readable medium according to claim 8, wherein each node of the plurality of nodes periodically wirelessly broadcasts its current geographic location to other nodes of the network.
| 11. The non-transitory computer-readable medium according to claim 8, wherein the scanning of RF signals within the service area of the wireless network is without regard to a route of travel of a vehicle in which the mobile node resides.
| 12. The non-transitory computer-readable medium according to claim 8, the method further comprising:
adding the wireless snapshot and a respective estimated location of the particular mobile node to the collection as a wireless fingerprint sample, if the search fails to identify at least one wireless fingerprint sample that matches the wireless snapshot.
| 13. The non-transitory computer-readable medium according to claim 8, wherein the collection is indexed according to one or more of the data elements of each wireless fingerprint sample that characterize a signal source.
| 14. The non-transitory computer-readable medium according to claim 8, wherein the one or more communication interfaces are configured to scan and characterize RF signal sources comprising an RF signal of an IEEE 802.11p compliant vehicle to vehicle wireless communication standard and an RF signal compliant with a commercial cellular communication standard.
| 15. A system for vehicular positioning of nodes of a radio frequency (RF) wireless network comprising a plurality of nodes disposed at respective fixed locations and a plurality of mobile nodes that reside in respective vehicles that move within a service area of the wireless network, and wherein each node of the plurality of nodes comprises one or more communication interfaces configured for scanning an RF wireless environment of the respective node, the system comprising:
one or more processors operably coupled to storage and communicatively coupled to the plurality of nodes, the one or more processors operable to, at least:
periodically receive respective wireless fingerprint sample data generated by each mobile node of the plurality of mobile nodes, the wireless fingerprint sample data comprising data elements characterizing RF signals received by the mobile node from RF signal sources during scanning of the RF wireless environment of the mobile node and a corresponding geographic location within the service area at which the RF signals were received;
form a collection of the wireless fingerprint sample data received from the plurality of mobile nodes;
receive a request for an estimated geographic location of a particular mobile node of the plurality of mobile nodes;
search the collection using a wireless snapshot comprising data elements characterizing RF signals received in a current RF wireless environment of the particular mobile node, to identify wireless fingerprint samples of the collection that match the data elements of the wireless snapshot; and
calculate an estimated location of the particular mobile node using the identified wireless fingerprint sample data.
| 16. The system according to claim 15, wherein each mobile node of the plurality of mobile nodes comprises a wireless access point configured to provide wireless Internet access to end-user devices.
| 17. The system according to claim 15, wherein each node of the plurality of nodes periodically wirelessly broadcasts its current geographic location to other nodes of the network.
| 18. The system according to claim 15, wherein the scanning of RF signals within the service area of the wireless network is without regard to a route of travel of a vehicle in which the mobile node resides.
| 19. The system according to claim 15, wherein the one or more processors are further operable to:
add the wireless snapshot and a respective estimated location of the particular mobile node to the collection as a wireless fingerprint sample, if the search fails to identify at least one wireless fingerprint sample that matches the wireless snapshot.
| 20. The system according to claim 15, wherein the collection is indexed according to one or more of the data elements of each wireless fingerprint sample that characterize a signal source.
| 21. The system according to claim 15, wherein the one or more communication interfaces are configured to scan and characterize RF signal sources comprising an RF signal of an IEEE 802.11p compliant vehicle to vehicle wireless communication standard and an RF signal compliant with a commercial cellular communication standard. | The method involves receiving a request for an estimated geographic location of a particular mobile node of a set of mobile nodes. Collection of wireless fingerprint sample data is searched using a wireless snapshot that comprises data elements characterizing radio frequency (RF) signals received in a current RF wireless environment of the mobile node, to identify wireless fingerprint samples of the collection that match data elements of the wireless snapshot. An estimated location of the particular mobile node is calculated using identified wireless fingerprint sample data. INDEPENDENT CLAIMS are also included for the following:a non-transitory computer-readable medium comprising a set of instructions for vehicular positioning of nodes of an RF wireless networka system for vehicular positioning of nodes of an RF wireless network. Method for vehicular positioning of nodes i.e. internet of things nodes, of an RF wireless network e.g. city-wide vehicular network, shipping port-sized vehicular network and campus-wide vehicular network, associated with vehicles. Uses include but are not limited to a smartphone, tablet, smart watch, laptop computer, webcam, personal gaming device, personal navigation device, personal media device, personal camera and a health-monitoring device associated with automobiles, buses, lorries, boats, forklifts, human-operated vehicles and autonomous and/or remote controlled vehicles. The method enables the platform to be flexibly optimized at design/installation time and/or in real-time for different purposes so as to reduce latency, increase throughput, reduce power consumption and increase reliability with regard to failures based on the content, service or data. The method enables utilizing multiple connections or pathways that exist between distinct sub-systems or elements within the same sub-system to increase robustness and/or load-balancing of the network. The method enables gathering real-time information on position, speed, fuel consumption and carbon dioxide emissions of the vehicles, so that the communication network allows a port operator to improve the coordination of ship loading processes, increase throughput of the harbor and enhance performance of the positioning systems. The communication interfaces scan and characterize the RF signal sources with IEEE 802.11p compliant RF signals. The drawing shows a schematic block diagram of a communication network. 400Communication network |
Please summarize the input | System and method for telematics for tracking equipment usageSystems and methods are described for tracking information of an equipment including a telematics device configured to receive data from the equipment to determine a telematics information. The telematics information includes at least two of an equipment type, a location, a duration in the location, and miles travelled. A transmission device is configured to transmit the vehicle telematics information to at least one of a third party entity device, a government device and a mobile device.We claim:
| 1. A system for tracking local information of an equipment on a vehicle, comprising:
a telematics device in the vehicle configured to receive at variable data sampling rate, raw data of vehicle telematics information comprising two or more of: energy usage, rate of energy consumption, equipment type, vehicle owner's information, a vehicle location, a duration of vehicle in the location, parking and moving violation, vehicle fines, distance travelled on the vehicle, and weight and size of equipment; and
a transmission device configured to compress the raw data of the vehicle telematics information and directly transmit through a network, the compressed raw data of the vehicle telematics information to at least one of a third party entity device, a government device and a mobile device to determine a usage charge based on the vehicle telematics information, and wherein the telematics device is configured to receive one or a combination of: public emergency alert announcement, captured images and associated data for matching to an object of interest in the public emergency alert announcement, optical sensors data, on-board laser and sonar pulsed sensor and imaging camera data to render the captured images and associated data for remote analysis by the at least one of the third party entity device, the government device and the mobile device.
| 2. The system of claim 1, wherein the energy usage comprises total energy consumed by one or a combination of battery electric power, hydrogen fuel, natural gas, diesel fuel, solar power and gasoline, and the rate of energy consumption comprises per unit time measurement of one or a combination of battery electric power, hydrogen fuel, natural gas, diesel fuel, solar power and gasoline.
| 3. The system of claim 1, wherein the vehicle comprises one of: transportation vehicles, recreation vehicles, industrial or home equipment, autonomous vehicles, flying vehicles.
| 4. The system of claim 3, wherein the transportation vehicles comprise anyone of: a hybrid vehicle, an electric powered vehicle, a rental or a leased vehicle, a fleet managed vehicle, a car, a bus, a truck, wherein the recreation vehicles comprise anyone of: an all-terrain vehicle (ATV), an off-road vehicle, a drone, a boat, and the industrial/home equipment comprise anyone of: a power generator, a mining equipment, an agriculture equipment, a construction equipment.
| 5. The system of claim 3, wherein for autonomous self-driving vehicles, vehicle telematics information may be communicated to an infrastructure network communication on distance driven in autonomous mode to levy a usage tax on vehicle to infrastructure; and for flying cars, a flight tax may be levied per trip and based on amount of fuel consumed and distance flown.
| 6. The system of claim 1, wherein the telematics device, the government device and the mobile device associates a credit card, a debit card bank account, or through connection with a mobile device.
| 7. The system of claim 1, wherein the government device charges vehicle owner based on received vehicle telematics information, comprising: usage charges, parking metering, moving violations, vehicle fines, state lines, specified highways, crossing determined bridges and car sharing charges.
| 8. The system of claim 1, where the vehicle telematics information further includes information of a duration the equipment spends in determined geo-fenced locations.
| 9. The system of claim 8, further including an electronic control unit configured to restrict a use of a fuel source or switch to an alternate fuel source based on a determined geo-fenced area.
| 10. A method for tracking local information of an equipment in a vehicle, comprising:
receiving by a server, compressed raw data of vehicle telematics information which are compressed before being transmitted from a transmission device of a vehicle, the raw data of vehicle telematics information indicates energy and equipment use in the vehicle over a period of time, wherein the raw data of vehicle telematics information are received at variable data sampling rate by a telematics device, and the raw data of vehicle telematics information includes two or more of: energy usage, rate of energy consumption, equipment type, vehicle owner's information, a vehicle location, a duration of vehicle in the location, parking and moving violation, vehicle fines, distance travelled on the vehicle, and weight and size of equipment; and
processing the raw data of the vehicle telematics information to determine a usage charge or a tax; and
directly transmitting through a network, the usage charge or the tax to at least one of a third party entity device, a government device and a mobile device in order to determine a usage charge based on the vehicle telematics information, and wherein the telematics device is configured to receive one or a combination of: public emergency alert announcement, captured images and associated data for matching to an object of interest in the public emergency alert announcement, optical sensors data, on-board laser and sonar pulsed sensor and imaging camera data to render the captured images and associated data for remote analysis by the at least one of the third party entity device, the government device and the mobile device.
| 11. The method of claim 10, wherein the energy usage comprises total energy consumed by one or a combination of battery electric power, hydrogen fuel, natural gas, diesel fuel, solar power and gasoline, and the rate of energy consumption comprises per unit time measurement of one or a combination of battery electric power, hydrogen fuel, natural gas, diesel fuel, solar power and gasoline.
| 12. The method of claim 10, wherein the vehicle comprises one of: transportation vehicles, recreation vehicles, industrial or home equipment, autonomous vehicles, flying vehicles.
| 13. The method of claim 12, wherein the transportation vehicles comprise anyone of: a hybrid vehicle, an electric powered vehicle, a rental or a leased vehicle, a fleet managed vehicle, a car, a bus, a truck, wherein the recreation vehicles comprise anyone of: an all-terrain vehicle (ATV), an off-road vehicle, a drone, a boat, and the industrial/home equipment comprise anyone of: a power generator, a mining equipment, an agriculture equipment, a construction equipment.
| 14. The method of claim 12, wherein for autonomous self-driving vehicles, vehicle telematics information may be communicated to an infrastructure network communication on distance driven in autonomous mode to levy a usage tax on vehicle to infrastructure; and for flying cars, a flight tax may be levied per trip and based on amount of fuel consumed and distance flown.
| 15. The method of claim 10, wherein the telematics device, the government device and the mobile device associates a credit card, a debit card bank account, or through connection with a mobile device.
| 16. The method of claim 10, wherein the government device charges vehicle owner based on received vehicle telematics information, comprising: usage charges, parking metering, moving violations, vehicle fines, state or federal taxes, state lines, specified highways, crossing determined bridges and car sharing charges.
| 17. The method of claim 10, where the telematics information further includes information of a duration the equipment spends in determined geo-fenced locations.
| 18. The method of claim 10, further comprising restricting by an electronic control unit, a use of the fuel source or switching to an alternate fuel source based on a determined geo-fenced area. | The system comprises a telematics device (114) in the vehicle configured to receive at variable data sampling rate. The transmission device (115) configured to compress the raw data of the vehicle telematics information and directly transmit through a network. The compressed raw data of the vehicle telematics information to at least one of a third party entity device (104). A government device (150) and a mobile device (160) to determine a usage charge based on the vehicle telematics information. An INDEPENDENT CLAIM is included for a method for tracking local information of an equipment in a vehicle. System for tracking local information of an equipment on a vehicle. Minimizes cost of data transmission to the entity devices and/or other remote data locations. The drawing shows a block representation of a environment for tracking information. 104Third party entity device114Telematics device115Transmission device150Government device160Mobile device |
Please summarize the input | VEHICLE SYSTEM OF A VEHICLE FOR DETECTING AND VALIDATING AN EVENT USING A DEEP LEARNING MODELThe invention relates to a vehicle system (1) of a vehicle (2) configured to detect an event (E) and to broadcast said event (E) using a decentralized environmental notification message (DENM), wherein said vehicle system (1) comprises:
- at least one camera sensor (10) configured to capture images (I1) of an environment of said vehicle (2),
- an electronic control unit (11) configured to :
- detect an event (E) using a primary deep learning model (M1) based on said images (I1),
- apply an predictability level (A) on said event (E), said predictability level (A) being generated by said primary deep learning model (M1),
- transmit said event (E) to a telematic control unit (12) if its predictability level (A) is above a defined level (L1),
- said telematic control unit (12) configured to :
- receive said event (E) from said electronic control unit (10) and broadcast a related decentralized environmental notification message (DENM) via a vehicle to vehicle communication (V2V) and/or a vehicle to infrastructure communication (V2I),
- transmit at least one image (I1) and data details (D) of said event (E) to a server (3),
- receive a primary validation information (30) of said event (E) from said server (3), said primary validation information (30) being generated by a secondary deep learning model (M2), and cancel the broadcasting of said decentralized environmental notification message (DENM) if said event (E) is not validated,
- if said event (E) is validated, receive an updated instance (M3) of said primary deep learning model (M1) from said server (3) and transmit it to said primary electronic control unit (10) for updating said primary deep learning model (M1).
|1. A vehicle system (1) of a vehicle (2) configured to detect an external event (E) and to broadcast said event (E) using a decentralized environmental notification message (DENM), wherein said vehicle system (1) comprises:
* - at least one camera sensor (10) configured to capture images (11) of an environment of said vehicle (2),
* - an electronic control unit (11) configured to :
* - detect an event (E) using a primary deep learning model (M1) based on said images (11),
* - determine a predictability level (A) of said event (E), said predictability level (A) being generated by said primary deep learning model (M1), (M1) for categorizing the different events (E), said events comprising accidents, road-block, animals on the road or on the pavement, pedestrians on the road or on the pavement, obstacles on the road or on the pavement and ambulance vehicles on the road,
* - transmit said event (E) to a telematic control unit (12) of said vehicle system (1) if its predictability level (A) is above a defined level (L1),
* - said telematic control unit (12) configured to :
* - receive said event (E) from said electronic control unit (11) and broadcast a related decentralized environmental notification message (DENM) via a vehicle to vehicle communication (V2V) to other vehicles (6) in the environment of the vehicle (2) and/or via a vehicle to infrastructure communication (V2I), to infrastructures (7) in the environment of the vehicle (2),
* - transmit at least one image (11) and data details (D) of said event (E) to a server (3), said data details (D) of said event (E) comprising a label (LB) of said event (E), a location (LO) of said event (E), a timestamp (TI) of said event (E), and the predictability level (A) of said event (E),
* - receive a primary validation information (30) of said event (E) from said server (3), said primary validation information (30) being generated by a secondary deep learning model (M2), and cancel the broadcasting of said decentralized environmental notification message (DENM) if said event (E) is not validated,
* - if said event (E) is validated, receive an updated instance (M3) of said primary deep learning model (M1) from said server (3) and transmit it to said electronic control unit (11) for updating said primary deep learning model (M1).
| 2. A vehicle system (1) according to claim 1, wherein said electronic control unit (11) is configured to update said primary deep learning model (M1) with said updated instance (M3).
| 3. A vehicle system (1) according to claim 1 or claim 2, wherein said telematic control unit (12) is further configured to:
* - broadcast a periodic cooperative awareness message (CAM) based on said images (11) for stating the road conditions (R1) where said vehicle (2) is,
* - receive a secondary validation information (31) of said road conditions (R1) from said server (3), said secondary validation information (31) being generated by said secondary deep learning model (M2),
* - if said road conditions (R1) are validated, receive an updated instance (M3) of said primary deep learning model (M1) from said server (3) and transmit it to said electronic control unit (11) for update.
| 4. A vehicle system (1) according to any of the preceding claims, wherein if the predictability level (A) of said event (E) is between a threshold (Th1) below the defined level (L1), the electronic control unit (11) is further configured to transmit a control signal (11a) to a human interface machine (20) of said vehicle (2) in order to have a confirmation of the predictability level (A) of said event (E).
| 5. A vehicle system (1) according to any one of the preceding claims, wherein said event (E) is an accident, a road-block, an animal, a pedestrian, an obstacle, or an ambulance vehicle.
| 6. A vehicle system (1) according to any of the preceding claims, wherein said primary deep learning model (M1) is associated to a geographical location (L3).
| 7. A vehicle system (1) according to the preceding claim, wherein said vehicle system (1) comprises a plurality of primary deep learning models (M1) associated to different geographical locations (L3).
| 8. A vehicle system (1) according to any of the preceding claims, wherein said at least one camera sensor (10) is a front camera.
| 9. A vehicle system (1) according to any of the preceding claims, wherein said primary deep learning model (M1) and said secondary deep learning model (M2) are convolutional neural network (CNN) based.
| 10. A vehicle system (1) according to any one of the preceding claims, wherein said vehicle (2) is an autonomous vehicle.
| 11. A vehicle system (1) according to any one of the preceding claims, wherein if said electronic control unit (11) fails to detect an event (E), said telematic control unit (12) is further configured to send the images (11) captured by said at least one camera sensor (10) to said server (3).
| 12. A method (4) comprising:
* - a capture (E1) by at least one camera sensor (10) of a vehicle system (1) of a vehicle (2), of images (11) of the environment of said vehicle (2),
* - a detection (E2) by an electronic control unit (11) of said vehicle system (1) of an external event (E) using a primary deep learning model (M1) based on said images (11),
* - a determining (E3) by said electronic control unit (11) of an predictability level (A) of said event (E), said predictability level (A) being generated by said primary deep learning model (M1) for categorizing the different events (E), said events comprising accidents, road-block, animals on the road or on the pavement, pedestrians on the road or on the pavement, obstacles on the road or on the pavement and ambulance vehicles on the road,
* - a transmission (E4) by said electronic control unit (11) of said event (E) to a telematic control unit (12) of said vehicle system (1) if its predictability level (A) is above a defined level (L1),
* - the reception (E5) by said telematic control unit (12) of said event (E),
* - the broadcasting (E6) by said telematic control unit (12) of a decentralized environmental notification message (DENM) related to said event (E) via a vehicle to vehicle communication (V2V) to other vehicles (6) in the environment of the vehicle (2) and/or via a vehicle to infrastructure communication (V2I), to infrastructures (7) in the environment of the vehicle (2)
* - the transmission (E7) by said telematic control unit (12) of at least one image (11) and of data details (D) of said event (E) to a server (3), said data details (D) of said event (E) comprising a label (LB) of said event (E), a location (LO) of said event (E), a timestamp (TI) of said event (E), and the predictability level (A) of said event (E),
* - the reception (E8) by said telematic control unit (12) of a primary validation information (30) from said server (3), said primary validation information (30) being generated by a secondary deep learning model (M2), and the cancellation (E9) by said telematic control unit (12) of said broadcasting if said event (E) is not validated,
* - if said event (E) is validated, the reception (E10) by said telematic control unit (12) of an updated instance (M3) of said primary deep learning model (M1) from said server (3) and the transmission (E11) of said updated instance (M3) to an electronic control unit (11) of said vehicle system (1) for updating said primary deep learning model (M1). | The vehicle system (1) comprises one camera sensor (10) used to capture images of an environment of the vehicle (2, 6). The electronic control unit used to detect an event using a primary deep learning model based on the images. The predictability level on the event is applied. The predictability level is generated by the primary deep learning model. The event is transmitted to a telematic control unit (12) if its predictability level is above a defined level. The telematics control unit is used to receive the event from the electronic control unit (10) and broadcast a related decentralized environmental notification message through the vehicle to vehicle communication and vehicle to infrastructure communication. The image and data details of the event are transmitted to a server. INDEPENDENT CLAIMS are included for the following:a server comprises a secondary deep learning model; anda first method; anda second method. Vehicle system of a vehicle used to detect an event and to broadcast the event using a decentralized environmental notification message. The system obtains a better accuracy of the primary deep learning model and uses less memory in the vehicle and avoids going to a service center to update a vehicle's deep learning model and enhance the training of the secondary deep learning model. The drawing shows a schematic block diagram of a vehicle system. 1Vehicle system2, 6Vehicle10Camera sensor10Electronic control unit12Telematic control unit |
Please summarize the input | Defining and delivering parking zones to vehiclesTechniques are described for defining and delivering parking area information to a vehicle. The parking area information can be sent by a parking assistant device associated with a parking area and in response to receiving one or more messages from a vehicle system of the vehicle. Messages from the vehicle system indicate a location of the vehicle and are used by the parking assistant device to track a movement of the vehicle. The parking area information is sent in one or more responses messages from the parking assistant device and can include a rule for determining whether the vehicle is permitted to park in an unoccupied parking zone within the parking area or indicate a result of applying the rule.What is claimed is:
| 1. A system in a vehicle, the system comprising:
a communications interface; and
a vehicle control system including one or more processors configured to:
transmit, to a computer device associated with a parking area and through the communications interface, one or more messages indicating a location of the vehicle;
receive, through the communications interface, one or more response messages from the computer device, wherein the one or more response messages include information about the parking area, and wherein the information includes a rule comprising one or more conditions that must be satisfied in order for the vehicle to be permitted to park in an unoccupied parking zone within the parking area;
decode the one or more response messages to extract the information, including the one or more conditions; and
process the information in connection with a parking operation, wherein to process the information, the vehicle control system is configured to:
present the information on an audio or visual output device of the vehicle, the information being presented prior to performance of the parking operation, during performance of the parking operation, or both; or
determine, using the information, an autonomous driving maneuver performed as part of the parking operation.
| 2. The system of claim 1, wherein the one or more messages indicating the location of the vehicle comprise a vehicle-to-everything (V2X) message broadcasted by the system, and wherein the one or more response messages comprise a V2X message broadcasted by the computer device.
| 3. The system of claim 1, wherein the information included in the one or more response messages indicates whether the vehicle is permitted, based on a result of applying the rule, to park in the unoccupied parking zone within the parking area.
| 4. The system of claim 1, wherein the parking area includes multiple unoccupied parking zones, and wherein the information indicates a particular unoccupied parking zone as being preferred.
| 5. The system of claim 1, wherein the one or more conditions include a time-based restriction on parking.
| 6. The system of claim 1, wherein the one or more conditions include a parking restriction relating to an attribute of the vehicle or relating to an identity of an owner or driver of the vehicle.
| 7. The system of claim 1, wherein:
to process the information, the vehicle control system is configured to determine, using the information, the autonomous driving maneuver performed as part of the parking operation; and
the vehicle control system is configured to perform the parking operation autonomously as a self-parking operation that parks the vehicle into the unoccupied parking zone.
| 8. The system of claim 1, wherein:
to process the information, the vehicle control system is configured to determine, using the information, the autonomous driving maneuver performed as part of the parking operation; and
the vehicle control system is configured to perform the parking operation autonomously as a self-parking operation that parks the vehicle into a different parking zone than the unoccupied parking zone.
| 9. A method comprising:
transmitting, from a vehicle system of a vehicle to a computer device associated with a parking area, one or more messages indicating a location of the vehicle;
receiving, by the vehicle system, one or more response messages from the computer device, wherein the one or more response messages include information about the parking area, and wherein the information includes a rule comprising one or more conditions that must be satisfied in order for the vehicle to be permitted to park in an unoccupied parking zone within the parking area;
decoding, by the vehicle system, the one or more response messages to extract the information, including the one or more conditions; and
processing, by the vehicle system, the information in connection with a parking operation, wherein the processing comprises:
presenting the information on an audio or visual output device of the vehicle, the information being presented prior to performance of the parking operation, during performance of the parking operation, or both; or
determining, using the information, an autonomous driving maneuver performed as part of the parking operation.
| 10. The method of claim 9, wherein the one or more messages from the vehicle system comprise a vehicle-to-everything (V2X) message broadcasted by the vehicle system, and wherein the one or more response messages comprise a V2X message broadcasted by the computer device.
| 11. The method of claim 9, wherein the information included in the one or more response messages indicates whether the vehicle is permitted, based on a result of applying the rule, to park in the unoccupied parking zone within the parking area.
| 12. The method of claim 9, wherein the parking area includes multiple unoccupied parking zones, and wherein the information indicates a particular unoccupied parking zone as being preferred.
| 13. The method of claim 9, wherein the one or more conditions include a time-based restriction on parking.
| 14. The method of claim 9, wherein the one or more conditions include a parking restriction relating to an attribute of the vehicle or relating to an identity of an owner or driver of the vehicle.
| 15. The method of claim 9, wherein the parking operation is an autonomously performed self-parking operation that parks the vehicle into the unoccupied parking zone, and wherein the processing of the information in connection with the parking operation comprises determining, using the information, the autonomous driving maneuver performed as part of the parking operation.
| 16. The method of claim 9, wherein the parking operation is an autonomously performed self-parking operation that parks the vehicle into a different parking zone than the unoccupied parking zone, and wherein the processing of the information in connection with the parking operation comprises determining, using the information, the autonomous driving maneuver performed as part of the parking operation.
| 17. The method of claim 9, wherein the information about the parking area indicates a boundary of the unoccupied parking zone.
| 18. The method of claim 9, wherein the one or more conditions are determined based on identification, by the computer device, of a pattern in usage of the parking area.
| 19. A non-transitory computer-readable storage medium containing instructions that, when executed by one or more processors in a vehicle system of a vehicle, configure the vehicle system to:
transmit, to a computer device associated with a parking area, one or more messages indicating a location of the vehicle;
receive one or more response messages from the computer device, wherein the one or more response messages include information about the parking area, and wherein the information includes a rule comprising one or more conditions that must be satisfied in order for the vehicle to be permitted to park in an unoccupied parking zone within the parking area;
decode the one or more response messages to extract the information, including the one or more conditions; and
process the information in connection with a parking operation, wherein the processing comprises:
presenting the information on an audio or visual output device of the vehicle, the information being presented prior to performance of the parking operation, during performance of the parking operation, or both; or
determining, using the information, an autonomous driving maneuver performed as part of the parking operation. | The method involves sending one or more messages that indicate the vehicle's location from the vehicle system 110 to a computer device connected to the parking area. One or more response messages are received from the computer device by the vehicle system. The one or more response messages has information about the parking area. The information has a rule to determine whether the vehicle is permitted to park in an unoccupied parking zone within the parking area. Also, the information indicates a result of applying the rule. The one or more response messages are decoded to extract the information. The information regarding parking operations is processed by the vehicle system. INDEPENDENT CLAIMS are included for: a computer-readable storage medium containing instructions. Method for defining parking zones and conveying information about the parking zones to a vehicle in order to assist in parking of the vehicle. The parking zones are predefined and generally comprise uniformly shaped spaces that are well-marked so as to make the parking zones easily identifiable to the driver even without the aid of the map data. The drawing shows a block diagram of a parking system. 100Parking system 110Vehicle system 130Communication network 140Computer system 142Datastore 144Parking area information |
Please summarize the input | a network connection for automatically driving the vehicle dynamic behavior decision method in the environmentThe invention claims an automatic driving vehicle dynamic behavior decision method of network connection environment. the method comprises the following steps: step S1, in a V2X network environment, the surrounding road users gaining the surrounding environment information, and from the vehicle mass centre as the centre, performing region division with different radius, predicting the risk area, step S2 based on the surrounding road user surrounding environment information and the predicted risk area, performing the first stage of behavior decision, determining vehicle driving safety to ensure feasible action could take a set, step S3, behavior decision for the second stage: considering the un-safety constraint condition, from the feasible set of actions, final execution of the optimization selection action, driving behaviour decision.|1. A network dynamic behavior decision method of automatic driving vehicle environment, the vehicle is an automatic driving vehicle, wherein the method comprises the following steps: step S1, in a V2X network environment. gaining the surrounding environment information around the road user, and from the vehicle mass centre as the centre, performing region division with different radius, predicting the risk area, step S2, based on the surrounding road user surrounding environment information and the predicted risk area, performing the first stage of behavior decision. determining vehicle driving safety to ensure feasible action could take a set, step S3, behavior decision for the second stage: considering the non-safety constraint condition, from the feasible set of actions, final execution of the optimization selection action, driving behaviour decision.
| 2. The automatic driving vehicle dynamic behavior decision method according to claim 1, wherein, in the step S1, predicting the risk region defined in the following way: from the vehicle mass centre as the centre, the risk area is a circular area of radius in a safe braking distance Lrisk, in the formula, vi is the own vehicle current speed, amax is the vehicle acceleration value, L is the vehicle length, if a surrounding road users located in the risk area, the surrounding road users defined as risk road users, defined from the vehicle centroid as centre, the security early warning distance Lp-risk is a circular area of radius risk region after removing the annular region is a potential risk region, wherein, adec is the maximum value of the self-vehicle deceleration, if some surrounding road users located in the potential risk area around the road users defined as a risk potential road users, defined from the vehicle the centroid as centre; safety pre-warning distance Lp-risk area outside the circular area of radius is safe area around, if some road users located outside the potential risk areas, or in the vehicle is out of communication range, the surrounding road users defined as safe road users.
| 3. The automatic driving vehicle dynamic behavior decision method according to claim 2, wherein the step S2 comprises the following steps: step S21, surrounding road users in the risk area and risk potential region, calculating the risk degree C, wherein firstly calculating the surrounding road user in the risk area, then calculating the surrounding road users potential risk in the area risk degree C representing the automatic driving vehicle probability of conflict between the current state and the surrounding road user state. t is expected of the estimated collision time, if the risk area and risk potential region has two or more of the surrounding road users, t is minimum value of the predicted collision time of two or more estimated predicted collision occurs with each surrounding road users, if the risk area and risk potential region in not around the road user, t is greater than the setting value of tc, tc to avoid collision of the critical time, is set constant, when C=0, it is determined that there is no traffic conflict in this state. determining the risk metric value frisk is zero, and turning to step S23, when C=1, it is determined that potential traffic conflict exists in this state, turning to step S22, step S22, to calculate the risk metric value frisk, step S23. The risk metric value frisk for action selection, determining the feasible set of actions.
| 4. The automatic driving vehicle dynamic behavior decision method according to claim 3, wherein the estimated, predicted collision time t is calculated by the following formula, t=min (TTC, PET, TTB), wherein Ξ is the vehicle position, Xj is the position of surrounding road followed by the user is from the vehicle current speed vi, vj is the other vehicle current speed, Li is the length from the vehicle, PET is the difference between the time the vehicle enters the conflict of ti time reach the conflict to other road users of tj, PET=t = | ti-tj | TTB for evaluating forward area, suitable for the vehicle, other vehicle the front scene, Ξ is the vehicle position, Xj is followed by other vehicle position, vi is the own vehicle current speed, Li is the length from the vehicle.
| 5. The automatic driving vehicle dynamic behavior decision method according to claim 3, wherein the estimated, predicted collision time t calculated in the following way, when capable of distinguishing the scene, calculate the estimated only for estimation of the scene the collision time t, wherein, for the straight vehicle-following scene, t = TTC for intersection scene, t = PET for the vehicle after the forward collision scene, t = TTB; when the scene is complex, t=min (TTC, PET, TTB). wherein, Ξ is the vehicle position, Xj is the position of surrounding road followed by the user is from the vehicle current speed vi, vj is the other vehicle current speed, Li is the length from the vehicle, PET is the difference between the time the vehicle enters the conflict of ti time reach the conflict to other road users of tj, PET=t = | ti-tj | TTB for evaluating forward area, suitable for the vehicle, other vehicle the front scene, Ξ is the vehicle position, Xj is followed by other vehicle position, vi is the own vehicle current speed, Li is the length from the vehicle.
| 6. The automatic driving vehicle dynamic behavior decision method according to claim 3, wherein, in the step S2, calculating the risk measurement value frisk by the following formula, or
| 7. The automatic driving vehicle dynamic behavior decision method according to claim 1-6, wherein, in the step S3 of the second stage action decision, not include any influence on the security of the decision attribute, but considering the constraint function efficient soft, comfortable soft constraint function fc and traffic flow soft constraint function for the optimal decision.
| 8. The automatic driving vehicle real-time trajectory planning method according to claim 7, wherein the efficient soft constraint function is defined as: wherein, t0 is vehicle initial departure time, tf is the destination arrival time, v (t) is the self-vehicle speed.
| 9. The automatic driving vehicle real-time trajectory planning method according to claim 7, wherein the comfortable soft constraint function is defined as: wherein a is the vehicle acceleration, WorleeSol is the transverse acceleration, alon is the longitudinal acceleration.
| 10. The automatic driving vehicle real-time trajectory planning method according to claim 7, wherein the traffic flow soft constraint function ft is defined as: minft= α (vave-vder) 2 + β (dave-dder) 2, wherein vave is the average speed levels of peripheral traffic flow before the decision, vder is average speed level of desired peripheral traffic flow decision after dave is peripheral traffic flow before the decision of an average vehicle distance, dder is the average vehicle distance decision periphery after the desired traffic flow, α, β is weight coefficient, is more than 0 less than 1.
| 11. The automatic driving vehicle real-time trajectory planning method according to claim 7, wherein the second phase behavior decision step S3 is defined in the cost function J as follows: w1, w2, w3 is the weighting coefficient, all rates are more than 0 less than 1, and w1 +, w2 + w3=1, fe0, fc0, ft0 respectively represent the hypothesis according to the security decision state before continuing to execute after efficient, comfortable, and traffic flow function. | The method involves obtaining surrounding environment information by surrounding road from a vehicle mass center for performing region division with different radius to predict a risk area (S1). A first stage of behavior decision is performed (S2) to determine a vehicle driving safety to ensure feasible action based on the surrounding environment information of the surrounding road users and the estimated risk area. A non-safety constraint condition is considered (S3) from the feasible set of actions for selecting final execution of the optimization selection action for a driving behavior decision. Automatic driving vehicle network connection environment dynamic behavior decision method. The drawing shows a flow diagram illustrating an automatic driving vehicle network connection environment dynamic behavior decision method. '(Drawing includes non-English language text)' S1Step for obtaining surrounding environment information by surrounding road from a vehicle mass center for performing region division with different radius to predict a risk areaS2Step for performing first stage of behavior decision to determine a vehicle driving safety to ensure feasible action based on the surrounding environment information of the surrounding road users and the estimated risk areaS3Step for considering non-safety constraint condition from the feasible set of actions for selecting final execution of the optimization selection action for a driving behavior decision |
Please summarize the input | An intelligent network connection vehicle parallel driving control method based on ACP theoryThe invention relates to the technical field of parallel driving, more specifically, relates to an intelligent network parallel driving control method based on ACP theory, comprising: S10, establishing a parallel system based on ACP theory; collecting the intelligent traffic information in the actual space; and uploading to the parallel system; S20, the parallel system uses the intelligent traffic information to optimize and calculate the traffic flow and establish a virtual space which tends to be equivalent to the actual space; S30, optimizing and analyzing the intelligent traffic information in the virtual space, and optimizing the traffic flow and the driving vehicle: if there is better result verification passes, then turning to step S40; if there is no better result, keeping the current traffic flow vehicle driving instruction is not changed; S40, the virtual vehicle information passing through the new verification controls each vehicle in the actual space to execute the control command. The invention uses virtual traffic control and real vehicle driving control combination, virtual combination realizes intelligent and network control of vehicle and autonomous driving, optimizing traffic flow and reducing traffic accident risk.|1. An intelligent network-linked vehicle parallel driving control method based on ACP theory, the ACP theory is composed of artificial society, calculating experiment and parallel executing three parts; wherein the control method comprises the following steps: S10, establishing a parallel system based on ACP theory; collecting each intelligent traffic information in the actual space intelligent traffic system; the intelligent traffic information comprises vehicle speed, vehicle position, front and back vehicle distance, road information and road surface interaction information; the collected intelligent traffic information is uploaded to the parallel system; S20, the parallel system uses the intelligent traffic information collected in step S10 to optimize and calculate the traffic flow and establish a virtual space tending to the equivalent actual space; the virtual space comprises virtual intelligent traffic, road, information coupling the vehicle and the road and the vehicle; S30, in the virtual space established in step S20, optimizing and analyzing the intelligent traffic information, and using the early warning distance, braking distance and traffic accident rate to optimize the intelligent traffic information: if there is better result verification passes, then turning to step S40; if there is no better result, then keeping the current driving instruction is not changed, and continuously collecting and analyzing and adjusting the intelligent traffic information in the virtual space until the optimization result verification is passed, and then turning to step S40; S40, according to the intelligent traffic information verified in step S30, controlling each vehicle executing control command in the actual space, the control command comprises speed control, steering control and brake control; The intelligent traffic optimization in step S20 is calculated according to the following method: S21, calculating according to the following formula to obtain the speed difference of two adjacent vehicles, front and back vehicle distance and vehicle longitudinal distance: in the formula, SpdDif, i is the speed difference of the adjacent two vehicles, DistDif, i is the front and back distance, SLong, i is the ith vehicle longitudinal distance, ui represents the ith vehicle speed, ui-1 represents the i-1 vehicle speed, Sldi-1 is the position of the i-1 vehicle; Sldi is the position of the ith vehicle, SLong, 0 is the initial longitudinal distance of the vehicle; S22. The average traffic flow rate and the average traffic flow density solve follows: in the formula, SpdTrc, flow is average traffic flow speed, DensTrc, flow is average traffic flow density; S23. The average traffic flow rate according to the average traffic flow rate and average traffic flow density solve as follows: VolTrc, flow = SpdTrc, flow *DensTrc, flow type, VolTrc, flow is average traffic; calculated by the vehicle dynamics principle: in the formula, SLong, i is the ith vehicle longitudinal distance, ui represents the ith vehicle speed, FLj, i is four-wheel longitudinal force, mi is the vehicle quality; in the step S20, the coupling vehicle and the virtual road between the magic formula for modelling: μ L, ICV = DL, ICVsim [CL, ICVarctan (BL, ICV λ -EL, ICV (BL, ICV λ -arctan (BL, ICV λ)))] in the formula, μ L, ICV is longitudinal friction coefficient, BL, ICV is the rigidity coefficient; CL, ICV is shape parameter; DL, ICV is peak value parameter; EL, ICV is curvature parameter; λ is slip rate; using the upper type solve vehicle acceleration, as follows: in the formula, α i is acceleration, g is gravity acceleration, μ L, i, min, ICV and μ L, i, max, ICV is determined by vehicle-road coupling μ L, i, min, ICV represents the ith vehicle longitudinal friction coefficient lower limit; μ L, i, max, ICV represents the i vehicle longitudinal friction coefficient upper limit; in step S30, the pre-warning distance and braking distance is calculated according to the following method: S31, the performance evaluation index of the S31 /multi - vehicle interactive traffic flow system uses the collision time quantitative index TTCi, which is expressed as: in the formula, SLong, Dif, i represents the ith vehicle front and back vehicle distance, uDif, i represents the ith vehicle front and back speed difference; S32, using early warning index to judge whether it is possible to send traffic accident; the pre-warning index WIi is expressed as: S33, calculating the early warning distance SLong, Wr, i and braking distance SLong, Bk, i according to the following formula: in the formula, SLong, Bk, i is the braking distance, SLong, Wr, i is the early warning distance, uLong, 0 is the initial speed, uLong, i is i time speed; TBk, Delay is hardware delay time; TBk, Cmd is brake execution time; TResp, Delay is the response time of the driver; Select: taking is the threshold of TTC-1, so there is: in the formula, IdxNorm, TTC represents the normalized collision time index; taking WIThrd as the threshold of WI, comprising: in the formula, IdxNorm, WI represents normalized pre-warning index; In step S30, the traffic accident rate calculated by the following formula: in the formula; represents the average speed; E is the system parameter; adopting evaluation value evaluating the traffic flow; The evaluation values were affected by BL, ICV, CL, ICV, DL, ICV, EL, and ICV, i.e., ICV, ICV, ICV, ICV, and ICV. x = [BL, ICV, CL, ICV, DL, ICV, EL, ICV] Ts.t.xmin?x?xmax in the formula, ω i is a weighting factor; represents the ith vehicle ground friction estimation value, FZ μ represents ground friction force; represents the ith vehicle ground slip rate estimation value, xmin, xmax respectively BL, minimum boundary value of ICV, CL, ICV, DL, ICV, EL, ICV, the maximum boundary value; In the parallel system, the virtual vehicle satisfies the following dynamic balance: in the formula, Δ t is sampling time; and respectively is the target error of SLong, i, Disti and Spdi of the ith vehicle; Sdes, Long, i, Distdes, i and Spddes, i is the target value, Fi (k) is the control node force of the ith vehicle; ψ i, 1, ψ i, 2 and ψ i, 3 respectively weighting factor, Sldi, 0 represents the position of the 0 vehicle; in the parallel system, introducing multi-target cost function, for optimizing the traffic flow speed, vehicle distance, pre-warning distance and collision quantitative index, the multi-target cost function is expressed as: uCon = [F1, F2, ..., FN] Ts.t.uCon, Lim, min ?uCon, i?uCon, Lim, max formula, Q> 0 is a weighting factor; uCon, Lim, min and uCon, Lim, max is input limit value; and QPI is a control energy penalty function; ω TTC, i, ω WI, i and δ WI, i is the weight coefficient; ω WI, i and δ WI, i is the weight factor of IdxNorm, WI;
| 2. The intelligent network parallel driving control method based on ACP theory according to claim 1, wherein in the step S10, the parallel system comprises a parallel intelligent traffic system for communication through V2X; a parallel driving management system and a parallel driving control system; the parallel driving management system collects and analyzes the intelligent traffic information of the parallel intelligent traffic system, evaluates and simulates the verification and sends the control command to the driving control system; the driving control system controls the vehicle speed in the actual space, steering or braking. | The method involves establishing a parallel system based on an algebra of communicating processes (ACP) theory. Intelligent traffic information is collected in an actual space intelligent traffic system, where the intelligent traffic information comprises vehicle speed, vehicle position, front and back vehicle distance, road information, and road surface interaction information. The collected intelligent traffic information is uploaded to the parallel system. A virtual space tending is established to an equivalent actual space. The actual space is controlled to execute a control command, where the control command comprises speed control, steering control, and brake control. ACP theory-based intelligent network connection vehicle parallel driving control method. The method enables optimizing the traffic flow and reducing traffic accident risk. The drawing shows a schematic representation of the ACP theory-based intelligent network connection vehicle parallel driving control method. (Drawing includes non-English language text). |
Please summarize the input | A networked autonomous fleet scheduling and cooperative control method based on event-triggeredThe invention claims a networked autonomous fleet scheduling and cooperative control method based on event-triggered. the method comprises: in autonomous fleet the vehicle controller receiving the status information relating to vehicle and vehicle-pacesetting wireless network transmission to generate a control signal, the vehicle to vehicle longitudinal dynamic mechanical analysis to establish mathematical model; considering the lead vehicle acceleration disturbance and based on the lead vehicle-following policy to establish a primary longitudinal structure of fleet model; considering the vehicle engine parameter uncertainty and performing discretization, and establishing a final motorcade longitudinal structure model, introducing an event triggering mechanism. establishing a controller structure model and solving the vehicle controller gain according to the controller gain of the vehicle and status information received by solving the acceleration at any time the vehicle so as to control the whole longitudinal vehicles. The invention improves the robustness of the network autonomous vehicles, effectively inhibit the frequent acceleration and deceleration of the vehicle to increase the comfort of passengers and reduce oil consumption.|1. A networked autonomous fleet based on event-triggered scheduling and cooperative control method, wherein it comprises the following steps: S1. autonomous controller of vehicle in the fleet related to receive state information of the pacesetting of the wireless network transmission vehicle and to generate a control signal, S2, a vehicle-to-vehicle for mechanical analysis to establish the linear longitudinal dynamics model, considering the acceleration of the lead vehicle disturbance and based on the lead vehicle-following policy to establish the primary train longitudinal structure model; S3, considering the vehicle engine parameter uncertainty and performing discretization, and establishing a final motorcade longitudinal structural model, S4, on considering the fleet of the uncertainty model based on longitudinal structure, into the event triggering mechanism, establishing a controller structure model; time delay system of S5, introducing autonomous fleet model, solving the vehicle controller gain. The controller gain of the vehicle and status information received by solving the acceleration of the vehicle at any time, obtained according to any time of acceleration control the entire longitudinal fleet.
| 2. The method according to claim 1, the mentioned a kind of networked autonomous fleet scheduling and cooperative control method based on event-triggered, wherein, in the step S1, pacesetting related state information of the vehicle and vehicle comprising a front position, the relative speed pacesetting and the acceleration of the vehicle and the front vehicle.
| 3. The method according to claim 1, the mentioned a kind of networked autonomous fleet scheduling and cooperative control method based on event-triggered, wherein in step S2, comprising the following steps: S2.1, for non-linear vehicle dynamics model, described as a first-order differential equation: wherein q0 is the position of the pacesetting vehicle, qi is the ith vehicle relative to the position of the reference point, vi is the speed of the ith vehicle acceleration ai is the ith car, respectively is a derivative of qi, vi, ai, engine input mi is the mass of the i-th vehicle, ci is the ith vehicle, σ is the air mass density and drag coefficient Ai is the ith vehicle cross-sectional area, cdi is the ith vehicle, dmi is the mechanical drag of the ith vehicle, engine power Fi is the ith car, is the air resistance of the ith vehicle, ξ i is the ith (i=1, 2. the number of the engine time constant .., n) vehicle, n is the vehicle in the fleet of S2.2, additional control input ui is the ith vehicle, then using feedback linearization method for nonlinear vehicle model, the the vehicle dynamics model of the nonlinear vehicle to obtain the ith vehicle linearization of longitudinal dynamics model: S2.3, autonomous expectation of fleet vehicle distance and the actual distance error can be described as: in the formula, Li is the length of the ith car, is the desired vehicle distance, δ i is the desired vehicle distance and the actual distance of the error, S2.4, x (t) = (δ, vi-1-vi, ai-1-ai] T, yi (t) = (δ, vi-1-vi, ai-1-ai, v0-vi, a0-ai] T, wherein v0 and a0 are the speed and the acceleration of the lead vehicle, the ui (t) is the ith vehicle additional control input at t time, defining state variables, measuring output and control amount are as follows: Assuming the engine constant ξ = ξ (i=1, 2,. .., n), then it can be known by the formula (3): wherein is the third derivative δ i; is the derivative of x (t), can be obtained: instruction: wherein, can be known by the analysis, if i=1, then: make can be obtained: in the formula, g=[001]T. is the derivative of the pacesetting vehicle acceleration a0; therefore, the primary longitudinal structure model of the fleet engine constant uncertainty is not introduced autonomous fleet longitudinal structural model is available state space expression primarily expressed as: wherein, G = (g 0 ... 0] T,
| 4. The method according to claim 1, the mentioned a kind of networked autonomous fleet scheduling and cooperative control method based on event-triggered, wherein in step S3, considering the vehicle engine parameter uncertainty and performing discretization. The vehicle longitudinal dynamics model and the primary vehicle longitudinal structure model, and establishing a final motorcade longitudinal structure model, comprising: the uncertainty factor if considering the uncertainty of the engine parameter, introducing time-variant Δξ, the dynamic model of the i-th vehicle can be described as: in the formula | =fi (t) | Δξ, and fi (t) is continuously capable of measuring function, and satisfies fi2 (t) ? Di Lebesgue, Di>, 0, Di is a known matrix, and the absolute value | Δξ | of the lower boundary, the time-varying factor Δξ can influence the system, at this time, on the basis of state space expression (9), considering the engine constant uncertainty, then the autonomous vehicle longitudinal structure model usable state space expression further expressed as: is the uncertainty factor in the formula, the constant considering engine representing uncertainty autonomous fleet longitudinal structural model of the state space expression (11) performing discretization to obtain the final autonomous fleet longitudinal structure model as follows: in the formula, k is a positive integer, is an uncertainty factor absolute value | Δξ | of the deterministic boundary, represents the state space expression (11) performing discretization coefficient matrix corresponding.
| 5. The method according to claim 1, the mentioned a kind of networked autonomous fleet scheduling and cooperative control method based on event-triggered, wherein in step S4, comprising: recording a vehicle state of the current time is x (k), the latest transmission state is x (sj), wherein sj represents the time of the current event-triggered, event triggering mechanism controller next sj (j=0, 1, 2, ...) time update control command, when x (k) and x (sj) satisfies :[x(k)-x(sj)]T Ω [x (k) - x (sj)] > μ electrotransfer (k) Ω x (k); (13), in the formula, Ω is the positive definite weighting matrix, k, sj is the positive integer, Osr (0, 1), constructing the output feedback controller to the vehicle: in the formula, is the controller gain after calculating, and respectively is a controller for the ith vehicle and vehicle distance of car, gain of the speed difference and the acceleration difference. respectively is the controller gain of the speed difference and the acceleration difference of the ith vehicle and pacesetting vehicle. information of sj time transmission time delay in a wireless network; the autonomous controller structure model of the vehicle as follows: in the formula,
| 6. The method according to claim 1, the mentioned a kind of networked autonomous fleet scheduling and cooperative control method based on event-triggered, wherein in step S5, the self time delay system expression of the fleet model as follows: in the formula, the formula (13) is satisfied, β k=k-sj, ej (k) =0, when formula (13) is not met, the M=covering, 1, τm, +, τm is information through the delay bound of wireless network transmission, then wherein l is a non-zero positive integer, n is a non-zero positive integer not less than.
| 7. The method according to claim 1, the mentioned a kind of networked autonomous fleet scheduling and cooperative control method based on event-triggered, wherein step S5 comprises the following steps: S5.1, established according to the step S3 of the final longitudinal structure of fleet model selects the Lyapunov-Krasovskii function: in the formula, δ (l) = x (l + 1) - x (l), P, Q, R are positive definite symmetric matrix to be solved, S5.2, calculating the forward differential selection of the Lyapunov-Krasovskii function. conditions the Δ V < 0, and introducing heat performance index and the H infinite property index, then the system has asymptotic stability as follows: given the parameters μ > 0 and the known time M, and positive definite weighting matrix W 0, Vc 0, there are >, 0, γ, >, 0, and suitable dimension matrix make the LMI is satisfied, in the formula marked then then satisfy the cost function J with bound J* and H-infinity performance | | y | | 2? | |ω | γ2 | 2; S5.3, by formula (3) to obtain: the formula (4) the third derivative, to obtain the following formula: The autonomous fleet controller structure model (15) to obtain: equations (18), (19) and (20) to obtain: putting the equation discretization of the sampling period is T, as follows: supposing the initial state condition of the autonomous vehicle control system is δ i (0) = 0, combining formula (21) and formula (22) and Z-transformation, the ith vehicle with the vehicle of the vehicle distance error transfer function is expressed as: wherein, S5.4, defined by the Z-transform of the known z=ej ω, and j=1 for all ω, 2, 3. .., n, so that the autonomous vehicle system satisfies the condition of the system with queue stability as follows: S5.5, by the (18) algebraic operation and Schur theorem, and (24) the queue stability in asymptotic stability condition of the in the step (18) to obtain the control gain of the controller satisfies bound via status information relating to vehicle and vehicle-pacesetting wireless communication network transmission, solving the acceleration of the vehicle at any time so as to control the whole longitudinal vehicles. | The method involves receiving relevant state information of a leading vehicle and a preceding vehicle by a vehicle controller through a wireless network to generate control signal. Mechanical analysis on the vehicle is carried out to establish a linearized longitudinal dynamics model of the vehicle. A preliminary longitudinal structure model of vehicle fleet is established based on lead vehicle-front vehicle following strategy. Uncertainty of vehicle engine parameters is considered. A final fleet longitudinal structure model is established. An event triggering mechanism is introduced based on the final fleet longitudinal structure model. Longitudinal fleet is controlled according to determined acceleration. Event triggering based vehicle networked autonomous fleet scheduling and co-controlling method. The method enables improving robustness of a network autonomous vehicle and effectively inhibiting frequent acceleration and deceleration of the vehicle to increase comfortableness of passengers and to reduce oil consumption. The drawing shows a block diagram illustrating a event triggering based networked autonomous fleet scheduling and co-controlling method. '(Drawing includes non-English language text)' |
Please summarize the input | Intersection autonomous vehicle dispatching and control method based on dynamic priorityThe invention claims an intersection autonomous vehicle scheduling and control method based on dynamic priority, comprising the following steps: obtaining the location information of the autonomous vehicle in the current intersection range; calculating the dynamic priority of the autonomous vehicle according to the positioning information; setting the planning motion state of the autonomous vehicle according to the dynamic priority; using the planned motion state as the reference to guide the corresponding autonomous vehicle to control the actual motion state. The intersection autonomous vehicle scheduling and control method based on dynamic priority, calculating the dynamic priority of the vehicle through the positioning information of each main vehicle, setting the planning motion state of each autonomous vehicle according to the autonomous vehicle priority of the high dynamic priority, avoids the high priority vehicle to avoid the low priority vehicle to decelerate, so as to improve the passing speed of the vehicle, reduce the passing time, has good real-time and high passing efficiency.|1. An intersection autonomous vehicle scheduling and control method based on dynamic priority, wherein it comprises the following steps: obtaining the location information of the autonomous vehicle in the current intersection range; calculating the dynamic priority of the autonomous vehicle according to the positioning information; setting the planning movement state of the autonomous vehicle according to the dynamic priority; guiding the corresponding autonomous vehicle to control the actual motion state by taking the planned motion state as the reference; wherein the step of obtaining the location information of the autonomous vehicle in the range of the current intersection further comprises the following steps: according to the trend of each lane of the current intersection, establishing a two-dimensional coordinate system intersection model of the current intersection at the position of entering the intersection and leaving the intersection; the step of calculating the dynamic priority of the autonomous vehicle according to the positioning information comprises the following steps: according to the speed of the autonomous vehicle, the maximum speed limit of the current intersection, the residence time of the current intersection and the distance calculating the value of the dynamic priority level to the nearest collision point, in the formula, PRi is the dynamic priority of the i-th autonomous vehicle Vi, vi is the speed of the i-th autonomous vehicle Vi, is the maximum speed limit of the current intersection, t is the current time, is the time of the autonomous vehicle Vi entering the current intersection, is the distance from the autonomous vehicle Vi to the nearest rush point; the step of planning operation state of autonomous vehicle according to the dynamic priority comprises the following steps: constructing a target function about a motion state plan; the target function is wherein si is the one-dimensional position of the autonomous vehicle Vi on the track, ai is the acceleration of the autonomous vehicle Vi, T is the period of the discrete system, Np is the planning time domain, si (Np-1) - si (0) is the advancing distance of the planning time domain Np, is the acceleration square in the planning time domain Np, k is the time of the autonomous vehicle Vi to the rush point; solving the target function by using the constraint condition as the optimization object; the constraint condition comprises one-dimensional motion equation of autonomous vehicle along the track, initial position and speed, speed and acceleration range constraint, and lane avoiding rear-end collision and different lanes avoid collision; planning the motion state of the autonomous vehicle according to the solving result.
| 2. The intersection autonomous vehicle scheduling and control method based on dynamic priority according to claim 1, wherein the intersection model comprises a lane boundary, a parking line, a driving track line, and a punching point formed by intersecting each driving track line. the length between the collision point coordinate of each conflicting point in the two-dimensional coordinate system and the adjacent collision point on the same travelling track line and the length between the parking line and the conflict point.
| 3. The intersection autonomous vehicle dispatching and control method based on dynamic priority according to claim 1, wherein it comprises the following steps before the step of obtaining the location information of the autonomous vehicle in the current intersection range. setting intersection scheduling center for processing intersection autonomous vehicle scheduling task, the intersection scheduling center uses V2X to communicate.
| 4. The intersection autonomous vehicle scheduling and control method based on dynamic priority according to claim 1, wherein the step of obtaining the location information of the autonomous vehicle in the current intersection range comprises the following steps: collecting the autonomous vehicle positioning information by the sensor, the sensor comprises one or more of vehicle GPS, UWB, IMU and road side vision, road survey radar, the positioning information comprises autonomous vehicle real-time position, real-time orientation angle, real-time speed, real-time acceleration, real-time front wheel deflection angle and the time information of the vehicle.
| 5. The intersection autonomous vehicle scheduling and control method based on dynamic priority according to claim 1, wherein the step of guiding the corresponding autonomous vehicle to control the actual motion state by taking the planned motion state as the reference comprises the following steps: mapping the planning motion state of the autonomous vehicle to the intersection model, the reference motion state and the reference control obtain in the intersection model; according to the reference motion state and the reference control input, combining the real-time motion state of the autonomous vehicle obtain the optimal desired motion input, controlling the actual motion state of the autonomous vehicle according to the expected motion input.
| 6. The intersection autonomous vehicle dispatching and control method based on dynamic priority according to claim 5, wherein the reference movement state comprises a reference coordinate, a reference angle and a reference speed, and the reference control input comprises a reference acceleration and a reference front wheel deflection angle.
| 7. The intersection autonomous vehicle scheduling and control method based on dynamic priority according to claim 6, wherein the step of controlling the actual motion state of the autonomous vehicle according to the expected motion input comprises the following steps: according to the real-time coordinate and the real-time speed of the autonomous vehicle, and the reference coordinate and the reference speed in the reference motion state, performing the state prediction and rolling optimization of the control time domain, and executing the first frame result after optimization. | The method involves obtaining location information of an autonomous vehicle in a current intersection range. A dynamic priority of the autonomous vehicle is calculated according to the location information. A planning movement state of the autonomous vehicle is set according to the dynamic priority. The corresponding autonomous vehicle is guided to control an actual motion state by taking the planned motion state as a reference. A two-dimensional coordinate system intersection model of the current intersection is established at a position of entering intersection. An intersection scheduling center for processing intersection autonomous vehicle scheduling task is set. Method for scheduling and controlling an intersection autonomous vehicle based on dynamic priority. The method enables calculating the dynamic priority of the vehicle through the positioning information of each main vehicle, setting planning motion state of each autonomous vehicle according to the autonomous vehicle priority of the high dynamic priority, and avoiding high priority vehicle to avoid low priority vehicle to decelerate so as to improve passing speed of the vehicle, reduce passing time, and ensure better real-time and high passing efficiency. The drawing shows a flow diagram illustrating a method for scheduling and controlling an intersection autonomous vehicle based on dynamic priority. (Drawing includes non-English language text). |
Please summarize the input | Method and apparatus of networked scene rendering and augmentation in vehicular environments in autonomous driving systemsA system and method is taught for vehicles controlled by automated driving systems, particularly those configured to automatically control vehicle steering, acceleration, and braking during a drive cycle without human intervention. In particular, the present disclosure teaches a system and method for generation situational awareness and path planning data and transmitting this information via vehicle to vehicle communications where one vehicle has an obstructed view to objects not within an obstructed view of a second vehicle.What is claimed is:
| 1. A method comprising:
receiving a data related to a field of view of a sensor on a first vehicle;
generating a first point cloud having a first granularity in response to the data;
determining a bandwidth and a latency of a transmission channel;
generating a second point cloud having a second granularity in response to the first point cloud and the bandwidth and latency of the transmission channel; and
transmitting the second point cloud to a second vehicle for use by an autonomous control system.
| 2. The method of claim 1 further comprising determining a location of a first object within the field of view.
| 3. The method of claim 2 further comprising determining a velocity of the first object.
| 4. The method of claim 1 wherein the first granularity is higher than the second granularity.
| 5. An apparatus comprising:
a sensor for receiving data related to a field of view of the sensor on a first vehicle;
a processor for generating a first point cloud having a first granularity in response to the data, determining a bandwidth and a latency of a transmission channel, for generating a second point cloud having a second granularity in response to the first point cloud and the bandwidth and latency of the transmission channel; and
a transmitter for transmitting the second point cloud to a second vehicle for use by an autonomous control system.
| 6. The apparatus of claim 5 wherein the processor is further operative to determine a location of a first object within the field of view.
| 7. The apparatus of claim 6 wherein the processor is further operative to determine a velocity of the first object.
| 8. The apparatus of claim 5 wherein the first granularity is higher than the second granularity. | The method involves receiving data related to a field of view of a sensor on a first vehicle (305). A first point cloud with a first granularity is generated in response to the data. Bandwidth and latency of a transmission channel are determined. A second point cloud with a second granularity is generated in response to the first point cloud and the bandwidth and latency of the transmission channel. The second point cloud is transmitted to a second vehicle (315) for use by an autonomous control system. A location of a first object within the field of view is determined. Velocity of the first object is determined, where the first granularity is higher than the second granularity. An INDEPENDENT CLAIM is also included for an apparatus for facilitating networked scene rendering and augmentation in vehicular environments in autonomous driving systems. Method for facilitating networked scene rendering and augmentation in autonomous driving systems of a passenger car. Can also be used for motorcycle, lorry, sport utility vehicle (SUV), recreational vehicle (RV), marine vessel and aircraft. The method enables extending and augmenting three-dimensional (3D) vision beyond line-of-sight perception range through vehicle to everything (V2X) communication and providing efficient object rendering solution using suites of adaptive information retrieval protocols based on available V2X bandwidth and latency requirements. The method enables augmenting existing 3D sensing ability by sharing different perspectives to avoid line-of-sight obstruction so as to provide extended vision for better surrounding awareness and path planning of future vehicles. The method enables re-sampling particles for selecting weights with numerical magnitude, thus increasing accuracy of predicted and sensor-corrected object position. The drawing shows a schematic view of a system for facilitating networked scene rendering and augmentation in vehicular environments in autonomous driving systems. 300System for facilitating networked scene rendering and augmentation in vehicular environments in autonomous driving systems305, 310Vehicles315, 320Regions of coverage335Pedestrian |
Please summarize the input | Parking service privacy protection system and method based on v2pThe invention claims a v2p-based parking service privacy protection system and method, comprising: a blind signature certificate generating part (PKI), a parking lot terminal (PLT), a parking lot service provider (PSP), an automatic driving vehicle (AV) and an intelligent mobile phone (SM). the user registers PKI; PKI returns the blind certificate; PLT registers to PSP; PSP returns signature key to PLT; the user submitting the application service request to the PSP through SM; returning the request token SESS; the user uses the SESS to query the parking lot information to the PSP; the PSP returns to the parking lot according to the pseudo position of the user; the user selects the parking lot, and sends the subscription request and signature to the PSP; the PSP forwards the request and the signature to the corresponding PLT, after the PLT is verified, the parking permission code is generated and sent to the PSP; the PSP signs the license code and returns to the user. the user sends the return information to the AV through SM, the AV reaches the appointed PLT, after checking the information, the parking is finished. The advantages of the invention are as follows: user experience is better, more safety, higher efficiency.|1. A v2p-based parking service privacy protection system, wherein it comprises: a blind signature certificate generating part (PKI), a parking lot terminal (PLT), a parking lot service provider (PSP), an automatic driving vehicle (AV) and an intelligent mobile phone (SM); the blind signature certificate generating position (PKI): PKI is responsible for auditing the specific information of the user, specifically representing the validity of the registered file submitted by the user; PKI can access the port of the public security department; the identity information of the user is submitted to the public security department for authentication by means of homomorphic encryption; if the authentication is passed, the PKI generates a suitable blind signature certificate for the user; Automatic driving automobile (AV): AV has autonomous capability, and also has communication capability based on cellular network, making it can be directly connected with other entities in the network; AV can accept the order of user; the intelligent mobile phone (SM) is bound with the AV; SM is owned by the user and under the control of the user, the user can install the parking application program and use the application program to finish the subscription process; A parking service provider (PSP): PSP is a online server, providing a parking service on demand for the user, comprising: searching the nearby parking space; booking the parking space and subscribing service; the subscription service is the registered user who pays the membership fee to enjoy these convenient services; the service can be used as the intelligent mobile phone application program to be issued to the user; parking lot terminal (PLT): PLT is the terminal of the parking lot owner deployment, responsible for monitoring and managing the parking lot through IoT device; In addition, PLT loads the real-time state of the parking lot to the PSP, so as to attract more vehicles; the real-time state comprises: parking fee, vacant parking space and high altitude map.
| 2. The working method of parking service privacy protection system based on v2p according to claim 1, wherein it comprises the following steps: 1: the user registers with the PKI; after the registration, the PKI returns the blind certificate certuficate; to 2.PLT PSP registration; after the registration is successful, the PSP returns the signature key Rab; 3, the user submitting the application service request to the PSP through the SM; after the PSP verification is successful, returning the request token SESS to the user; 4, the user uses the SESS to inquire the parking lot information to the PSP; the PSP returns a parking lot in a certain range according to the pseudo position of the user; 5, the user selects one parking lot, and sends the reservation request Req and signature σ to the PSP; 6.PSP Req | |σ to the corresponding PLT, after PLT verification, generating parking permission code c, and sending the c to the PSP; 7.PSP is Sigc, and the c | | sigc is returned to the user; 8. The user sends the c | | Timestamp | SESS | Sigc to AV through SM, AV reaches the appointed PLT, after checking the information, the parking is finished.
| 3. The method according to claim 2, further comprising: system installation, registration, service, parking and malicious user exposure and key revocation; The following definitions of the symbols used are as follows: λ is a safety parameter; G, GT bit bilinear cyclic mapping group; p is a mass number whose length is λ; g1 is generating element; H (), H ' (); is three non-encryption hash functions; is private key and public key of PSP; X, Y, Z is and x, y, z belongs to Zp; e (.) is bilinear mapping pairing function μ; μ is a daily authentication key; Ω; Ψ is three data storage sets; is the private key of PLT and public key; Rab is the signature key of PLT; the certificate is the user blind signature certificate; Timestamp is the current time slot; the SESS is the voucher for parking every time; The system installation includes: PKI initialization: PKI selecting one parameter g as its own identification code, and using the RSA algorithm to generate own public and private key pair; according to the RSA algorithm, PKI selection: random prime number b, c, and b is not less than 2512, c is not less than 2512, making n = b * c, a random number As the PKI self-public key, wherein and then PKI calculation through the same equation equation obtaining d, taking d as its own private key; PKI publishing system parameters (e, n, g), and secret storing (b, c, d), PSP operation registration algorithm; a bilinear mapping group (G, GT) of a large prime number p> 2 λ is created, wherein λ is a safety parameter and e (...) represents a bilinear mapping such as e: G * G-GT; in the form, g1 is the generating element of G and e (g1, g1) is defined as gT; H: (0, 1) *, ZP, H ' apos;: (0, 1) *, G and ZP-ZP is three encrypted hash functions; Public key of PSP is set as a belongs to Zp is randomly selected, and a is a private key; PSP randomly selected prime number p, q wherein q | p-1, p is not less than 2512, q is not less than 2160, and p is not less than g; PSP selecting x, y, z belongs to Zp and calculating and μ is a key selected by the PSP and changed daily; then Yuan group the public parameter is published in the system; Finally, PSP using Bloom filter initialization three empty sets and attention, μ, Ω, Ψ, Ψ is reset by PSP, so as to ensure the subscription credential of the user is only effective on the day; The registration includes: 1. User registration: (1.1) before using the parking service provided by the PSP, the user registering registration to the designated PKI through the identity card; (1.2) PKI verifying the user information, returning to the user a blind certificate issued by PKI = ((M 'apos;, j), (Y', U ', z', j ', S' 1, S ' 2), B); 2.PLT: (1.1) PLT creating user name and password, and registering in the terminal; (1.2) PLT uploads the identity information (e.g., electronic commercial parking lot license) to the PSP, and PSP verifies the qualification of the parking lot; (1.3) after the verification is passed, PLT will create a key pair wherein b is randomly selected in the ZP, calculating the signature key and the public key B is sent to the PSP; (1.4) PSP storing B, parking information and finishing the registration; The service includes: 1. Verification of User Certificate (1.1) the vehicle user Vi submitting the application and certificate to the PSP, wherein 1 ?i?s is the total s of the vehicle user, and Vi represents the ith user; Firstly, PSP verifies the legality of the blind certificate; in the proof process, the user acts as a certifier; the PSP acts as a verifier: BV sends the PSP, T6, HMACk2 (certificate | T6), yi, H (xi); wherein xi is the private key selected by the user and stored in the local; (1.2) if the certificate is legal and in the validity period, then the verification is successful, PSP in Ω searching H (xi), if it does not exist in Ω, receiving yi1 ?i?s sent by SM, allowing the vehicle user Vi to join in the group, and generating a temporary session token SESS; sending it back to the user, and storing the blind certificate in the database; if there is Ω, then the user through SM re-selecting xi, until the H (xi) does not exist in the library; For the vehicle user Vi, the PSP safety (yi, tn) to PKI, and PKI stores (yi, tp) in the local database; otherwise, the PSP returns the failure; (1.3) the user stores the session token SESS; (1.4) if there is new added user needs to use the group public key updating algorithm to the public key of the PSP of the group public key; 2. Spark inquiry: (2.1) by using the geographic undifferentiated mechanism to interfere the user current real position (latitude, longitude, radius) (lat 'lon' rng ' rng) = DP (lat, lon, rng, ε); (2.2) the user through sending (lat ', lon', rng ') and SESS to the PSP to set parking requirement and request nearby parking information; (2.3) PSP screening parking lot not meeting the condition, and returning the parking lot list in the query range; 3. Advance of the Carpark: (3.1) the user selects one parking lot from the returned list; sending the reservation request Req and the signature σ to the PSP, wherein Req=Info | | SESS | | Timestamp information relates to the trivial reservation information; the timestamp represents the current timestamp; (3.2) User Computing as a subscription token, sending the U to the PSP, and performing non-interactive zero knowledge knowledge with the PSP, wherein the user plays the certifier; the PSP plays the verifier: (3.3) after receiving the request, if the certificate is successful and the token U is not present in the step, the PSP receives the request and adds the U to the step; otherwise, the PSP rejects the request; (3.4) the PSP sends the Req | |σ to the corresponding PLT; (3.5) PLT through signature σ and public parameters (g, m, u, c, h) to verify the validity of the signature, after successful verification, PLT will generate a unique random character string as temporary parking license code c, storing it in the local database, and sending it back to PSP; (3.6) PSP marking the c as Sigc=H ' (c | | Timestamp | SESS) a, storing the SESS in the token pool, and returning the c | | sigc back to the user; The parking includes: 1. Sparking Request: (1.1) the user through SM the c | | Timestamp | SESS | Sigc and parking information is sent to AV; (1.2) the AV is switched to the automatic driving mode and is driven to the selected parking lot according to the received information; 2. Inspection: (2.1) when connected to the PLT, AV the c | | Timestamp | SESS | | Sigc is sent to the PLT; (2.2) PLT by checking verifying the signature Sigc; if it is correct, the PLT searches c in the database and ensures whether the AV has reserved the parking space; If c is found in its local database, PLT deletes c and allows AV to be parked therein; otherwise, PLT returns failure and refuses to provide service; (2.3) PLT by selecting the random θ?Zp re-signature Sigc is and the Sig'c is sent to the AVs as the acknowledgement receipt; 3. Reprovisioning of the subscription information: (3.1) the AV forwards the receipt Sig'c to the SM of the user, and notifies the parking confirmation message on the SM of the user; (3.2) after waiting for random delay, the user by sending c | | Timestamp | | SESS | | Sig'c | | U to the PSP to apply for resetting its own subscription information, so as to realize the second subscription; (3.3) the PSP receives the reset request, checking the validity condition of the credential reset request through the following two conditions: Condition 1: PSP verifies the signature by the following formula: if the equation is satisfied, then satisfying the condition; condition 2: the PSP searches Uin in the sum Ψ; if the U is present in the S, and does not exist in Ψ, then the PSP adds U to the Ψ and deletes the U in the S, then the condition is satisfied; if any one is not completed, the PSP rejects the request and returns a failure; otherwise, the PSP is successfully returned, the user can perform the parking space reservation by U; The malicious user exposure and key revocation are: if the anonymous identity wants PSP to initiate attack, in this case, PSP combined PLT applies to PKI to open the identity of malicious user, PSP collecting related subscription request sent by malicious user (π, ζ, p, Req), using the same equation c = yk (mod pk) to calculate the public key yk of the malicious user; searching the self database to find the blind signature certificate of the malicious user and submitting it to the PKI; PKI according to the blind signature certificate submitted by the PSP, searching the real identity of the malicious user in the library, and penalty, such as refusing to generate a new blind signature and so on.
| 4. The method according to claim 3, wherein the first and second data are the same. The user registration is implemented by the following algorithm: PKI blind signature generating user certificate, assuming that the user uses smart mobile phone SM at PKI registration, PKI randomly selecting 3 random generating elements R, R1, R2 belongs to G11) SM selecting a random number ξ SM, and calculating M=ASM = ξ SMR1 + R2; ρ = e (R, QPKI), ρ 1 = e (R1, QPKI), ρ 2 = e (R2, QPKI), y=e (Ppub, QPKI); then SM sends IDSM, M, T1 to LTA; 2) selecting random number Q?G1 by PKI; and calculating e = (M, Γ PKI), a=e (R, Q), δ = e (M, Q), U=rR, Y=rQPKI; then PKI sends z, a, δ to the registered user, U, Y, T2HMACK1 (z | |a | |δ | | U | | Y | | T2) 3) SM selecting random number α; β; γ; λ; μ; σ, u; and calculating M '= α M, A=e (M', QPKI) δ '= δ u α Av, z' = z α, a '= au ρ v, Y' = λ Y + λ μ QPKI-γ Hi (j); U '= λ U + γ Ppubl = λ -1H2 (M', Y ', U', A, B, z ', a', δ ') + μ, j' = lu, k1 = e (Γ SM, QPKI) then SM sending 1, T3 to PKI; HMACk1 (1 | | T3) 4) PKI calculates S1 = Q + 1 Γ PKI, S2 = (r + 1) Γ PKI + rH1 (j) and sending S1, S2, T4, HMACk1 (S1 | S2 | | T4) to SM; if the formula e (R, S1) = ayl, e (M, S1) = δ zl, SM calculating S' 1 = uS1 + VQPKIS' 2 = αS2; then the limited part blind signature of (M 'apos;, j) is (Y', U ', z', j ', S' 1, S '2) and the blind signature generated by the vehicle user SM is = ((M', j), (Y ', U', z ', j', S '1, S' 2); B) J is the expiration time of the blind certificate, and Ti is a time stamp for preventing double attacks.
| 5. The method according to claim 3, wherein the first and second data are the same. the user certificate verification is realized by the following algorithm: the PSP verifies the user certificate issued by the PKI and establishes a group: the PSP establishes the group formed by the user using the service and is a group administrator; according to the Chinese remaining theorem, based on the public key of the group member, the PSP can calculate to generate a group public key; the PSP can use the group public key to verify the validity of the signature when the parking service request is requested; when there is a member in the group adding or exiting, PSP updating the group public key according to the Chinese remaining theorem algorithm, using the Schnorr signature algorithm; 1) PSP calculating A=e (M ' apos;, QPKI); if A is not equal to 0, calculating i = H4 (A, B, QPSP, time), wherein time is the binary representation of the current time; PSP to SM sending the gei2) SM calculating r1 = i (ξ x α) + β, r2 = i α + σ then SM sending r1, r23) PSP, respectively formula a '= e (P, S' 1) y-j ', δ' = e (M ', S' 1) z ' -j ', if formula e (S' 2, R) = e (Y '+ H3 (M', Y ', U', A, z ', a', δ) QPKI, Ppub) × e (H1 (j), U') is established, the signature is legitimate; when and only when when the PSP receives the certificate, the certificate is legal.
| 6. The method according to claim 3, wherein the first and second data are the same. The PSP generation group public key algorithm is as follows: the PSP uses the received public key of s user; calculating the group public key by the same equation equations: The value of the equation of the same equation is wherein P=p1p2 ... ps=p1P1 = p2P2 = ... = psPs; i= 1, 2, ..., s, and p'i is the positive integer solution of the same equation p'ipi = 1 (mod pi) i= 1, 2, ...; C is the group public key, RSU selects one of safety hash function h and publishing parameter (g, m, u, c, h); Table 1 the existing group public key
y1 y2…yi…ysAs shown in Table 1, the group public key generated for PSP.
| 7. The method according to claim 3, wherein the first and second data are the same. the parking lot reservation (3.1) SM signature algorithm is as follows: using Scjnorr signature algorithm to sign the message, if the user SM wants to sign the message Req, firstly, SM selecting a random number and calculating f=g ω (mod p), π = h (f | |Req), ζ = ω-xk π (modq), wherein g is the identity identification code of the PKI, xk is the private key of the vehicle user SM, p, q is the prime number selected by the vehicle user SM of the PSP; then σ = (π, ζ, pk) is the signature of the vehicle user to the message Req; The knowledge proof algorithm in the parking lot reservation (3.2) is as follows: certifier 1) formula rewriting is 2) selecting ρ, ρ v?Zp, calculating Δ = U p, η = H (X, Y, Z); 3) μ, η, Δ; sending to the PSP; verifier 1) PSP receiving μ, Δ; calculating n = H (X, Y, Z); Inspection if it is established, it is proved that it is known; The algorithm of the PLT verification SM signature message in the parking lot reservation (3.5) is as follows: PLT can pass the signature σ = (π, ζ, pk) and public parameters (g, m, u, c, h) verifies the validity of the message: 1) calculating c = yk (mod pk), obtaining the public key yk of the vehicle user Vk; 2) checking whether the public key yk is in the middle; if so, executing the step 33), 4) if formula = h (f ' | Req) is established, then the signature message is the vehicle user Vk signature, and opening the message; 5) ending.
| 8. The method according to claim 3, wherein in the user authentication service (1.4) group public key updating algorithm is as follows: 1) for the new user Vs + 1 verified by the user certificate, PSP stores the vehicle user Vs + 1 and the corresponding blind certificate in the database, and updating table 1 is table 2: Table 2 after updating the group member public key
y1 y2…yi…ysys+12) PSP calculating new group public key through the same equation equations: the value of the same equation equation is wherein Pnew=p1p2 ... psps + 1 = Pps + 1; the calculation formula of Pinew and P ' inew is as follows: Input: Pi, Pi ' apos;, pi (1 ?i?s + 1) 1) if 1 ?i?s, then calculating Pinew=Pips + 1; wherein Because P 'inewPinew = 1 (mod pi) and PiPi' = 1 (mod pi); 2) if i=s + 1, calculating 3) Output: Pinew and P ' inew (1 ?i?s + 1) under the scheme, can realize the high-efficient adding of the new member, at the same time, does not affect the key of the existing member, only needs to update the group public key; after updating, PSP publishing new parameter group (g, m, u, c, h).
| 9. The method according to claim 3, wherein The malicious user disclosure and the specific member revocation algorithm in the key revocation are as follows: setting the current group there are s vehicle user; Vk represents any one group member; if the vehicle user Vk (1 ?k?s) wants to exit the group, Vk only needs to exit the application for sending to the PSP; the PSP updating the public key yk of the database Vk is y'k, and making the same equation y'k = yk (mod pk) is not established; and calculating a new group public key by the same equation set: the solution of the same equation set is: formula refers to FUFUFU; The updated current member public key table is shown in Table 3: Table 3 after revocation of the group member public key
y1 y2…yk-1yk+1…ys+1after the member revocation is completed, the same equation c' = yk (mod pk) and π = h (f | | M) are not established, the subscription request of the user cannot be verified, but in this process, the key of the original vehicle user will not be changed.
| 10. The method according to claim 3, wherein The algorithm of geographical indistinguishable in the safety given parameter (i.e., default privacy level can be set as low " = = O: 01, " = = = 004, high " = = 001), the actual position any point generated after processing the probability density function of the noise mechanism (planar Laplacian) is the Euclidean distance between the two can be expressed as it also can be expressed as polar coordinate model wherein rad and theta are the distance and angle between the real position and the blur position; in order to fuzzy real position θ should be randomly selected from [0, 2 π), rad is preferably set as wherein W-1 is Lambert W function (-1 branch) and p should be [0; 1) randomly selecting; In addition, there are two conversion functions: LatLonToCartesian and CartesianToLatLon; Realization and (x, y)-(lat ', lon') conversion; Therefore, and in addition; wherein τ is the precision parameter, default value τ = 0.95. | The system has a smart phone (SM) that is owned by a user and under a control of the user. The user installs a parking application and uses an application to complete a booking process. A PSP is an online server that provides the users with on-demand parking services and including finding nearby parking spaces, making parking reservations and subscribing to services. A subscription service is provided for registered users who pay membership fees to enjoy convenient services and released to the users as a SM application. A PLT is a terminal deployed by a parking lot owner and responsible for monitoring and managing a parking lot through IoT equipment. The PLT uploads a real-time status of the parking lot to the PSP to attract more vehicles. The real-time status includes parking fees, vacant parking spaces and high altitude maps. An INDEPENDENT CLAIM is included for a privacy protection method for parking services based on v2p. Privacy protection system for parking services based on v2p. The system includes better user experience, more safety and higher efficiency. The drawing shows a block diagram of a privacy protection system for parking services based on v2p. (Drawing includes non-English language text) |
Please summarize the input | Ecological driving method of automatic driving vehicle through signal intersectionThe invention claims an automatic driving vehicle through signal intersection ecological driving method, collecting road traffic information and self information through V2V/V2I technology, obtaining the control condition, then according to the current signal lamp state judging can pass through the uniform speed, if it can, uniform speed through the signal intersection area, otherwise, performing ecological driving control to the automatic driving vehicle, in the ecological driving control stage, constructing automatic driving vehicle motion state equation and cost function, using the Ponteri gold minimum value principle to respectively optimal control track solving for the intersection upstream area and the intersection downstream area, making the automatic driving vehicle pass through the signal intersection area according to the optimal track; The advantages are as follows: it can meet the actual driving requirement of the driver, meets the actual road traffic environment condition, and the control process is simple, the calculation amount is small, the control real-time performance can be ensured, so as to effectively reduce the automatic driving vehicle energy consumption, improve the travel efficiency, reduce the pressure caused by each vehicle-road device.|1. An automatic driving vehicle through signal intersection ecological driving method, wherein it comprises the following steps: step 1: dividing the signal intersection area into an intersection upstream area, intersection central area and the intersection downstream area, the intersection upstream area represents the area where the automatic driving vehicle starts to be controlled, the road section distance of the intersection upstream area is equal to the distance from the starting position of the automatic driving vehicle to obtain the intersection signal lamp configuration and the state information to the intersection stopping line, the distance of the section of the intersection upstream area is marked as l1, the specific value of l1 is defined according to the communication range of V2V/V2I technology; intersection central area is the signal intersection physical area, intersection central area of the road distance is equal to the intersection stop line to the intersection central area ending position of the distance, intersection central area of the road distance is marked as 1; the intersection downstream area represents the area where the automatic driving vehicle is controlled; the intersection downstream area is started from the intersection central area ending position; the road distance of the intersection downstream area is recorded as 12, and 12 is determined according to the parking distance safety the road section of the automatic driving vehicle passing through the signal intersection area is marked as L, L=l1 + 1 + l2; step 2: when the automatic driving vehicle runs into the upstream area of the signal intersection, using V2V/V2I technology to obtain the road traffic information of the signal intersection area, the road traffic information comprises intersection upstream area route l1, intersection central area route 1, intersection downstream area route l2, intersection signal lamp timing and state information and road information; the automatic driving vehicle obtains the current vehicle position in real time by GPS technology, using the vehicle-mounted sensor device to obtain the current vehicle speed, current acceleration and road traffic condition information; step 3: starting from the automatic driving vehicle driving signal intersection area, controlling the automatic driving vehicle according to the obtained intersection signal lamp timing and state information, making the automatic driving vehicle not to stop and leave the intersection as much as possible; the specific control process is as follows: when the automatic driving vehicle enters the signal intersection upstream area, the current vehicle speed is v0, if the current signal state is green light, and the current green light to the next red light time interval not less than the time when the automatic driving vehicle drives to the intersection stop line at uniform speed at the current speed v0, namely controlling the automatic driving vehicle to pass through the signal intersection according to the current speed v0 at uniform speed, ending the control; if the current signal state is green light, and the current green light to the next red light time interval TG is less than the time when the automatic driving vehicle drives to the intersection stop line at uniform speed at the current speed v0, namely then entering the step 4 to control the ecological driving of the signal intersection; if the current signal state is a red light, and the time of the vehicle running to the stop line at a uniform speed of the current vehicle speed v0 is not less than the current red light remaining time TR, i.e. then controlling the automatic driving vehicle to pass through the intersection according to the current speed v0 at uniform speed, ending the control; if the current signal state is a red light, and the current red light remaining time TR is greater than the time when the vehicle travels to the intersection stop line at a uniform speed at the current vehicle speed v0, i.e. then entering the step 4 to control the ecological driving of the signal intersection; step 4: performing ecological driving control to the signal intersection, specifically as follows: 4.1. the automatic driving vehicle enters the intersection upstream area or the intersection downstream area to restart timing, the intersection upstream area and the intersection downstream area starting position are marked as 0, the timing starting time is marked as 0, the automatic driving vehicle at the intersection upstream area or the intersection downstream area running at a certain time is marked as t ; when driving on the intersection upstream area, the position of the automatic driving vehicle t time is denoted as s (t), the speed is recorded as v (t), the acceleration is marked as u (t), u (t) is used as the control output of the automatic driving vehicle at the time t when the vehicle is running at the upstream area of the intersection; when running at the intersection downstream area, the position of the automatic driving vehicle t time is recorded as s' (t), the speed is recorded as v '(t), the acceleration is marked as u' (t), u ' (t) is used as the control output of the automatic driving vehicle at the time t when the vehicle is running at the downstream area of the intersection; according to the optimal control theory, the signal intersection upstream area running of the automatic driving vehicle motion state vector is described as: the motion state equation is obtained by motion state vector x (t), expressed as: wherein, represents the derivative of the motion state vector x (t), f (x (t), u (t)) represents the motion state equation function, represents the change rate of the driving position s (t) at t time, represents the change rate of the driving speed v (t) at t time, namely the acceleration u (t); 4.2, constructing cost function for ecological driving of the automatic driving vehicle at the signal intersection: wherein F represents the cost function, tf represents the intersection upstream area control process or the intersection downstream area control process of the terminal time; L (x (t), u (t)) is the cost function of the optimal control target, the cost function first item in order to realize the optimal control of the travel time cost, the second item is the energy consumption cost of the automatic driving vehicle, q (t) is the instantaneous energy consumption of the automatic driving vehicle t time, | | is the absolute value symbol; η 1 represents the cost corresponding to the time cost weight, η 2 represents the energy consumption cost corresponding cost weight, η 1, η 2 the value range is [0, 1], and the two cannot be simultaneously the value is 0; wherein the instantaneous energy consumption q (t) of the automatic driving vehicle t time is represented by the following formula: in the formula (3), Pm (t) is the motor power loss of the automatic driving vehicle, Pt (t) is the power loss caused by the resistance of the automatic driving vehicle, Pg (t) is the energy obtained by the automatic driving vehicle of the accelerating or deceleration, m is the mass sum of the automatic driving vehicle and the vehicle personnel, g is the gravity coefficient, fr1 is the rolling friction coefficient of the automatic driving vehicle, r is the motor equivalent resistance of the automatic driving vehicle, K is the product of the armature constant and magnetic flux of the automatic driving vehicle, k is the air resistance coefficient of the automatic driving vehicle, R is the tire radius of the automatic driving vehicle; 4.3, solving the signal intersection area automatic driving vehicle driving track, as follows: 4.3.1, according to the Ponteri gold minimum principle, determining the Hamiltonian function H [x (t), u (t), λ], as shown in formula (4): H [x (t), u (t), λ] = L (x (t), u (t)) + λ f (x (t), u (t)) = η 1 + η 2 | q (t) | + λ 1v (t) + λ 2u (t) (4), wherein the state equation λ is the co-state vector, λ 1 and λ 2 are co-state vector elements, the relational expression is constraint f (x (t), u (t)) is less than or equal to 0; 4.3.2, intersection upstream region optimal control solving, specifically as follows: the initial time of the automatic driving vehicle entering the intersection upstream area is 0, the intersection upstream area automatic driving vehicle initial time movement state vector is when the automatic driving vehicle reaches the intersection stop line time is marked as tf1, the intersection upstream area automatic driving vehicle terminal time of the motion state vector vf1 is the speed of the automatic driving vehicle at the time tf1; when the current signal state is green light, the intersection traffic efficiency is not affected, the speed of the automatic driving vehicle reaching the intersection stop line time tf1 is vmax, vmax is the road limit speed maximum obtained by the automatic driving vehicle, then vf1 = vmax, when the current signal state is a red light, when the automatic driving vehicle reaches the intersection stop line time tf1 signal state becomes green, tf1 = TR; to solve the intersection upstream area automatic driving vehicle optimal control output, it needs to satisfy the following formula: at this time, obtaining the expression of λ 1 and λ 2: then putting the expression of λ 1 and λ 2 into the Hamilton function H [x (t), u (t), λ], and then so as to obtain u (t) of the general expression; so as to automatically drive the motion state vector of the vehicle initial time and the motion state vector of the terminal time according to the intersection upstream region, calculating to obtain the intersection upstream region terminal time tf1, terminal time speed vf1 and control output u (t), the judging condition umin is less than or equal to u (t) ?umax is satisfied, umin is the minimum acceleration of automatic driving vehicle performance, umax is the maximum acceleration of automatic driving vehicle performance, if the condition is satisfied, then the u (t) calculated at this time is the intersection upstream area optimal control output if u (t) is less than umin, then u (t) = umin, u (t) as the intersection upstream area optimal control output if u (t) is greater than umax, then u (t) = umax, u (t) as the intersection upstream area optimal control output 4.3.3, the intersection downstream area optimal control solving, specifically as follows: the intersection downstream area distance is determined safety the parking distance, then wherein fs is the sliding friction coefficient of the driving road; the road information obtained by V2V/V2I technology is provided, the motion state vector of the automatic driving vehicle at the intersection downstream area t moment Motion state equation wherein s' (t) is the position of the intersection downstream area automatic driving vehicle at time t, v't) is the speed of the automatic driving vehicle at the time t at the intersection downstream area, u't) is the acceleration of the automatic driving vehicle at the time t at the intersection downstream area, namely the control output of the intersection downstream area, the initial time when the automatic driving vehicle reaches the downstream area of the intersection is 0, and the state vector of the initial time of the automatic driving vehicle at the downstream area of the intersection in order to make the automatic driving vehicle to finally recover the initial speed of the signal intersection area, namely the speed of the terminal time tf2 of the automatic driving vehicle driving out the intersection downstream area is v0, namely the state vector of the automatic driving vehicle at the intersection downstream area terminal time, λ 'the intersection downstream area co-state vector, λ' 1 and λ ' 2 are the intersection downstream area co-state vector element, the relational expression is to the intersection downstream area automatic driving vehicle optimal control output, it needs to satisfy: at this time, obtaining the expression of λ '1 and λ' 2: then bringing the expression of λ '1 and λ' 2 into the Hamilton function H [x '(t), u' (t), λ '], and then so as to obtain u ' (t) of the general expression; so as to automatically drive the motion state vector of the vehicle initial time and the motion state vector of the terminal time according to the intersection downstream region, calculating to obtain the intersection downstream region terminal time tf2 and control output u't), the judging condition umin is not more than u't) ?umax, if the condition is satisfied, then calculating the u ' (t) is the intersection downstream area optimal control output if u't) is less than umin, then u '(t) = umin, u' (t) as the intersection downstream area optimal control output if u't) is greater than umax, then u '(t) = umax, u' (t) as the intersection upstream area optimal control output 4.4, controlling the automatic driving vehicle to acceleration passing through the intersection upstream area, when the automatic driving vehicle drives the intersection upstream area, entering the intersection central area, the speed is vf1, at the intersection central area, controlling the automatic driving vehicle to pass through at uniform speed vf1, when the automatic driving vehicle drives the intersection central area, entering the intersection downstream area, controlling the automatic driving vehicle to acceleration passing through the downstream region of the intersection; when the automatic driving vehicle driving out signal intersection area, ending the ecological driving control. | The method involves dividing a signal intersection area into an intersection upstream area, an intersection central area and an intersection downstream area. The intersection upstream and intersection downstream areas are determined as an area. Road traffic information of the signal intersection is obtained by using a vehicle-to-vehicle/vehicle-to-infrastructure (V2V/V2I) technology when the automatic drive vehicle runs into the upstream area of the intersection area of signal intersection. The current vehicle speed, current acceleration and road traffic condition information are obtained using a vehicle-mounted sensor device. An automatic driving vehicle is controlled to pass through the intersection according to the current speed at uniform speed to end the control. An ecological driving of the signal intersection is controlled. The ecological driving control is performed to the signal intersection. Method for ecologically driving automatic driving vehicle through signal intersection. The method enables satisfying the actual driving requirement of the driver, and meeting the road traffic environment condition, and the control process is simple, so that the calculation amount is small, and the control real-time performance can be ensured, thus effectively reducing the automatic driving vehicle energy consumption, improving the travel efficiency, and reducing the pressure caused by each vehicle-road device. The drawing shows a schematic view of the method for ecologically driving automatic driving vehicle through signal intersection (Drawing includes non-English language text). |
Please summarize the input | Inter-vehicle communication system using NFT authenticationThe present invention detects vehicle and pedestrian signals from images collected through a vision sensor mounted on the vehicle, then transmits the signal status and location information of the vehicle to surrounding vehicles in a broadcast manner, and transmits the signal status and location information of the vehicle to surrounding vehicles based on the location information from the following vehicle. This is about a vehicle-to-vehicle communication system using NFT authentication that allows the signal information to be confirmed after specifying the preceding vehicle on the same route, but also ensures the reliability of the information through authentication of the issued NFT information.|1. In an autonomous vehicle-to-vehicle communication system that communicates with a relay station and surrounding vehicles, the first communication unit 111 communicates with the vehicle through the relay station 150, and provides information on the vehicle number and owner through authentication of vehicle and owner information. An issuing unit 112 that issues and manages NFTs to enable tracking of information, registration, and renewal date, and an authentication unit that performs authentication of the requested NFT from a vehicle that has received an NFT of another vehicle and notifies the result to the vehicle requesting authentication. Authentication server 110 having a unit 113;
A second communication unit 121 that is mounted on an autonomous vehicle and communicates with the relay station 150 and surrounding vehicles, and a first storage unit that receives and stores NFTs from the authentication server 110 through the second communication unit 121. Unit 122, a signal transmission unit 123 that transmits the NFT of the first storage unit 122 to a surrounding vehicle through a beacon to request authentication, and the authentication server 110 of the NFT received from the surrounding vehicle An authentication module 120 including a second storage unit 124 that is authenticated from and stores the NFT received upon completion of authentication for a set time;
An information transmission unit 141 that is mounted on an autonomous vehicle and transmits the acquired traffic information along with the NFT stored in the first storage unit 122 to surrounding vehicles as a message, and the NFT of the message received from the surrounding vehicle is In the case of NFT stored in the second storage unit 124, an information transmission and reception module 140 including an information processing unit 142 that processes the traffic information included; It is mounted on an autonomous vehicle and consists of a vision sensor 131 that photographs the front of the vehicle, a location confirmation unit 132 that generates current location information of the vehicle, and an image obtained through the vision sensor 131. It further includes an information analysis unit 133 that analyzes and acquires vehicle and pedestrian signal information, and an information acquisition module 130 that acquires the location information and signal information as traffic information; Further comprising, the information acquisition module 130 includes a distance calculation unit 134 that calculates distance information to itself by analyzing location information of surrounding vehicles corresponding to the traffic information being processed, and analyzes the distance information. It further includes a determination unit 135 that determines whether there is a preceding vehicle on the same route, and a traffic analysis unit 136 that analyzes processed traffic information and generates traffic flow information, and the information processing unit 142 is configured to monitor surrounding vehicles. If the NFT of the message received from is not stored in the second storage unit 124, authentication is requested by the authentication server 110, and if the NFT is authenticated, the message is stored in the second storage unit 124 and then traffic The information acquisition module 130 processes the information, and the information acquisition module 130 compares the traffic information included when receiving the message with a plurality of preceding vehicles on the same route at a distance of a set range, and the information verification unit (113) A vehicle-to-vehicle communication system using NFT authentication, characterized in that it further includes.
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| 5. delete | The system has a communication unit communicated with a vehicle through a relay station, and an authentication server provided with an authentication unit that performs authentication of a near field communication (NFT) requested in the vehicle. Another communication unit is mounted on an autonomous vehicle, and communicates with the relay station. A storage unit receives and stores the NFTs from the authentication server through the latter communication unit. An information transmission and reception module includes an information processing unit that processes traffic information. Autonomous vehicle-to-vehicle communication system. The system achieves safe and rapid communication between driving vehicles by obtaining signal information, prevents unreasonable passing and tailgating at intersections, and prevents accidents, traffic jams and accidents. The system quickly processes traffic information messages through pre-reception authentication process, thus reducing information processing time and providing reliable communication between certified vehicles. The signal and traffic flow can be safely identified, and various traffic situation information such as occurrence of accident or congestion, as well as signal information can be delivered. The drawing shows a schematic view of an autonomous vehicle-to-vehicle communication system(Drawing includes non-English language text). |
Please summarize the input | Vehicle trajectory prediction near or at traffic signalA system and method for determining a predicted trajectory of a human-driven host vehicle as the human-driven host vehicle approaches a traffic signal. The method includes: obtaining a host vehicle-traffic light distance d x and a longitudinal host vehicle speed vx that are each taken when the human-driven host vehicle approaches the traffic signal; obtaining a traffic light signal phase Pt and an traffic light signal timing Tt; obtaining a time of day TOD; providing the host vehicle-traffic light distance dx, the longitudinal host vehicle speed vx, the traffic light signal phase Pt, the traffic light signal timing Tt, and the time of day TOD as input into an artificial intelligence (AI) vehicle trajectory prediction application, wherein the AI vehicle trajectory prediction application implements an AI vehicle trajectory prediction model; and determining the predicted trajectory of the human-driven host vehicle using the AI vehicle trajectory prediction application.The invention claimed is:
| 1. A method for determining a predicted trajectory of a human-driven host vehicle as the human-driven host vehicle approaches a traffic signal, wherein the method is carried out by one or more electronic controllers, and wherein the method comprises the steps of:
obtaining a host vehicle-traffic light distance d x and a longitudinal host vehicle speed vx that are each taken when the human-driven host vehicle approaches the traffic signal;
obtaining a traffic light signal phase P t and a traffic light signal timing Tt, wherein the traffic light signal phase Pt represents a phase of the traffic signal taken when the human-driven host vehicle approaches the traffic signal, and wherein the traffic light signal timing Tt represents an amount of time elapsed since a last phase change of the traffic signal taken when the human-driven host vehicle approaches the traffic signal;
obtaining a time of day TOD;
providing the host vehicle-traffic light distance d x, the longitudinal host vehicle speed vx, the traffic light signal phase Pt, the traffic light signal timing Tt, and the time of day TOD as input into an artificial intelligence (AI) vehicle trajectory prediction application, wherein the AI vehicle trajectory prediction application implements an AI vehicle trajectory prediction model; and
determining the predicted trajectory of the human-driven host vehicle using the AI vehicle trajectory prediction application.
| 2. The method of claim 1, wherein the method further includes obtaining a front vehicle state XFV, wherein the front vehicle state includes a front-host vehicle distance rt and a front-host vehicle speed {dot over (r)}t, and wherein the providing step further includes providing the front vehicle state XFV as input into the AI vehicle trajectory prediction application.
| 3. The method of claim 2, wherein the front vehicle state XFV is obtained at the one or more electronic controllers based on front vehicle base information that is obtained at the front vehicle and then sent via vehicle-to-vehicle (V2V) communications to the one or more electronic controllers.
| 4. The method of claim 1, wherein the traffic light signal phase Pt and the traffic light signal timing Tt are obtained from a traffic light control system that is present at an intersection where the traffic light is located.
| 5. The method of claim 1, wherein the predicted trajectory is obtained at an autonomous vehicle that is approaching the traffic light and that is separate from the human-driven host vehicle.
| 6. The method of claim 5, wherein the method is carried out at the autonomous vehicle as the human-driven host vehicle approaches the traffic light.
| 7. The method of claim 6, wherein the autonomous vehicle obtains the traffic light signal phase Pt and the traffic light signal timing Tt from a traffic signal system located at an intersection where the traffic light is located.
| 8. The method of claim 7, wherein the autonomous vehicle receives the traffic light signal phase Pt and the traffic light signal timing Tt via vehicle-to-infrastructure (V2I) communications from roadside equipment that is a part of the traffic signal system.
| 9. The method of claim 6, wherein the autonomous vehicle receives the traffic light signal phase Pt and the traffic light signal timing Tt from a traffic signaling control system that is located remotely from the traffic light.
| 10. The method of claim 6, wherein the host vehicle-traffic light distance dx, the longitudinal host vehicle speed vx are obtained at the autonomous vehicle via V2V communications with the host vehicle.
| 11. The method of claim 1, wherein the vehicle-traffic light distance dx, the longitudinal host vehicle speed vx, the traffic light signal phase Pt, and the traffic light signal timing Tt are each associated with an associated time that is no more than a predetermined amount different than another one of the associated times.
| 12. The method of claim 1, wherein the AI vehicle trajectory prediction model is or includes a neural network.
| 13. The method of claim 12, wherein the AI vehicle trajectory prediction model is a deterministic or a model that predicts one or more most-probable trajectories.
| 14. The method of claim 12, wherein the AI vehicle trajectory prediction model is a probabilistic model that returns a probability distribution of predicted trajectories, and wherein the predicted trajectory is obtained by sampling a trajectory from the probability distribution of predicted trajectories.
| 15. The method of claim 14, wherein the neural network is a mixture density network.
| 16. The method of claim 12, wherein the neural network is a deep neural network.
| 17. The method of claim 1, wherein the method further includes the step of causing an autonomous vehicle to obtain the predicted trajectory of the human-driven vehicle, wherein the autonomous vehicle is configured to:
obtain the predicted trajectory of the human-driven vehicle, and
carry out an autonomous vehicle operation based on the predicted trajectory of the human-driven vehicle.
| 18. A method for determining a predicted trajectory of a human-driven host vehicle as the human-driven host vehicle approaches a traffic signal, wherein the method is carried out by one or more electronic controllers, and wherein the method comprises the steps of:
obtaining a host vehicle-traffic light distance d x and a longitudinal host vehicle speed vx that are each taken when the human-driven host vehicle approaches the traffic signal, wherein the host vehicle-traffic light distance dx and the longitudinal host vehicle speed vx each have an associated time;
obtaining a front vehicle state X FV, wherein the front vehicle state includes a front-host vehicle distance rt and a front-host vehicle speed {dot over (r)}t;
receiving one or more wireless signals that indicate a traffic light signal phase P t and a traffic light signal timing Tt, wherein the traffic light signal phase Pt represents a phase of the traffic signal taken when the human-driven host vehicle approaches the traffic signal, and wherein the traffic light signal timing Tt represents an amount of time elapsed since a last phase change of the traffic signal taken when the human-driven host vehicle approaches the traffic signal, wherein the traffic light signal phase Pt and the traffic light signal timing Tt each have an associated time, wherein the associated times of the host vehicle-traffic light distance dx, the longitudinal host vehicle speed vx, the traffic light signal phase Pt, the traffic light signal timing Tt, the front vehicle state includes a front-host vehicle distance rt, and the front-host vehicle speed {dot over (r)}t are within a maximum allowable time difference with respect to one another;
obtaining a time of day TOD;
providing the host vehicle-traffic light distance d x, the longitudinal host vehicle speed vx, the traffic light signal phase Pt, the traffic light signal timing Tt, the front vehicle state XFV, and the time of day TOD as input into an artificial intelligence (AI) vehicle trajectory prediction application, wherein the AI vehicle trajectory prediction application implements an AI vehicle trajectory prediction model, and wherein the AI vehicle trajectory prediction model is or includes a neural network; and
determining the predicted trajectory of the human-driven host vehicle using the AI vehicle trajectory prediction application.
| 19. The method of claim 18, wherein the host vehicle-traffic light distance dx and the longitudinal host vehicle speed vx are both obtained at an autonomous vehicle through receiving one or more wireless signals from the human-driven host vehicle via vehicle-to-vehicle (V2V) communications.
| 20. The method of claim 18, wherein the host vehicle-traffic light distance dx and the longitudinal host vehicle speed vx are both obtained at the autonomous vehicle through receiving one or more wireless signals from a remote server. | The method (200) involves obtaining (210) a host vehicle-traffic light distance and a longitudinal host vehicle speed that are taken when a human-driven host vehicle approaches a traffic signal. A traffic light signal phase and a traffic light timing are obtained (220). A time of day is obtained (240). The host vehicle traffic light distance, longitudinal vehicle speed, signal phase, timing and the time are provided (250) as input into an artificial intelligence (AI) vehicle trajectory prediction application. A predicted trajectory of the host vehicle is determined (260) using the AI vehicle trajectory application by electronic controllers. Method for determining predicted trajectory of human-driven host vehicle as vehicle approaches and departs from traffic signal by using artificial intelligence vehicle trajectory prediction model. The method enables predicting how human drivers respond to traffic signals for purposes of successfully carrying out and/or improving autonomous driving in areas having traffic signals. The method allows the AI vehicle trajectory prediction model to map states of human-driven vehicles and a corresponding state of a traffic signal. The drawing shows a flowchart illustrating the method for determining a predicted trajectory of a human-driven vehicle.200Method for determining a predicted trajectory of a human-driven vehicle 210Step for obtaining a host vehicle-traffic light distance and a longitudinal host vehicle speed 220Step for obtaining traffic light signal phase and a traffic light timing 240Step for obtaining time of day 250Step for providing host vehicle traffic light distance, longitudinal vehicle speed, signal phase, timing and the time 260Step for determining predicted trajectory of the host vehicle |
Please summarize the input | Apparatus for switching driving mode in autonomous driving vehicle, method thereof and computer recordable medium storing program to perform the methodThe present invention relates to an apparatus for switching a driving mode of an autonomous vehicle, a method therefor, and a computer-readable recording medium having recorded thereon a program for performing the method. The present invention relates to a camera unit for photographing a driver of the vehicle. A sensor unit including a plurality of sensors for dividing the outside of the vehicle into a plurality of areas and detecting objects of the plurality of divided areas, and a plurality of systems for controlling longitudinal and lateral driving of the vehicle; A control unit, a position information unit for deriving position information of the vehicle using a GPS (Global Positioning System) signal, a differential GPS (DGPS) signal, and an inertial sensor signal, the camera unit, the sensor unit, the driving unit, and the And a controller including two or more processors for controlling the vehicle including a location information unit, wherein the controller is configured to control driving of the vehicle. In the autonomous driving mode in which the autonomous driving level is controlled according to any one of a fully autonomous driving level controlled by the processor and a semi-autonomous driving level controlled by both the driver's operation and the processor. Determining whether at least one function of the sensor unit, the driving unit, the location information unit and the control unit is a fail state other than the normal state, and if the fail state is a vehicle device for performing a reboot, and the method and method therefor Provided is a computer readable recording medium having recorded thereon a program to be executed.|1. A vehicle apparatus for switching a driving mode of an autonomous vehicle, the vehicle apparatus comprising: a camera unit photographing a driver of the vehicle; A sensor unit for dividing the outside of the vehicle into a plurality of areas and including a plurality of sensors for detecting objects of the plurality of divided areas; A driving unit controlling a plurality of systems for controlling longitudinal and lateral movements of the vehicle; A location information unit for deriving location information of the vehicle using a GPS (Global Positioning System) signal, a differential GPS (DGPS) signal, and an inertial sensor signal; And at least two processors controlling the vehicle apparatus including the camera unit, the sensor unit, the driving unit, and the location information unit. And an autonomous driving level according to any one of an autonomous driving level at which the driving of the vehicle is controlled by the processor, and a semi-autonomous driving level at which the driving of the vehicle is shared and controlled by the driver. In the autonomous driving mode to determine whether at least one of the function of the sensor unit, the driver, the location information unit and the processor is in a fail state other than the normal state, if the fail state, perform a reboot, the reboot After that, whether the autonomous driving level is the complete autonomous driving level or the semi-autonomous driving level, and the state of each of the sensor unit, the driving unit, the location information unit, and the processor, driving of the vehicle is controlled by the driver. Limited driving mode controlled by a processor, and the driving of the vehicle is controlled by the driver Manual driving mode of any one of controlling so as to switch to the mode of the mode-switching module is controlled by; including, and then the mode conversion module to reboot in the autonomous mode of the semi-autonomous navigation level, If it is possible to detect all of the plurality of regions by using some of the plurality of sensors of the sensor unit, and switches to the limited driving mode, if it is unable to detect any of the plurality of regions to switch to the manual driving mode, the driving unit If it is not possible to control at least one of the longitudinal and transverse driving of the vehicle through, switch to the manual driving mode, and if it is possible to control both the longitudinal and transverse driving of the vehicle, switch to the restricted driving mode, When the accuracy of the location information is degraded according to the availability of the GPS signal, the DGPS signal, and the inertial sensor signal of the location information unit, the control unit switches to the limited driving mode and, if the position information cannot be calculated, switches to the manual driving mode. If both processors of the control unit are normal, the system enters the limited driving mode, and if either function is disabled, Vehicle device for a driving mode switching of autonomous vehicles, characterized in that the transition to the driving mode.
| 2. The method of claim 1, wherein the mode switching module photographs the driver through the camera unit when the autonomous driving mode of the fully autonomous driving level is switched to the manual driving mode, identifies the state of the photographed driver, and then the driver. If the vehicle can not perform the manual driving mode, the vehicle device for switching the driving mode of the autonomous vehicle, characterized in that for switching to the risk minimization operation (MRM) mode.
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| 4. The method of claim 2, wherein the mode switching module may detect all of the plurality of regions by using some of the plurality of sensors of the sensor unit after the reboot in the autonomous driving mode of the fully autonomous driving level, If only one area cannot be detected, the vehicle switches to the limited driving mode, and if it is unable to detect two or more areas of the front side or the side of the plurality of areas, it switches to the manual driving mode, and the longitudinal direction and If it is not possible to control at least one of the transverse driving, switch to the manual driving mode, and if the accuracy of the position information of the position information deterioration or cannot calculate the position information, switch to the manual driving mode, and If both processors are disabled, switch to manual driving mode, and if only one of the two processors is disabled, enter limited driving mode. Vehicle apparatus for switching the driving mode of the autonomous vehicle, characterized in that for switching.
| 5. The method of claim 2, wherein the mode switching module cannot detect the front region of the vehicle through the sensor unit in the risk minimization operation mode, or cannot detect two or more regions of the side region in the passenger seat direction of the vehicle, If the lateral driving of the vehicle cannot be controlled through the driving unit, the position information cannot be calculated through the position information unit, or both processors are disabled, after decelerating, the vehicle stops at a driving lane after deceleration. The accuracy of the position information calculated by the position information unit, or the two or more areas of the side area in the direction of the driver's seat can not be detected through the sensor unit, the longitudinal operation of the vehicle can not be controlled through the drive unit; Is deteriorated, the operation of the autonomous vehicle, characterized in that for performing the operation to stop in the safety zone or shoulder Vehicle apparatus for a mode change.
| 6. The vehicle apparatus of claim 2, further comprising a communication unit configured to perform V2I communication, which directly communicates with a traffic server through a roadside device, and V2V communication, which directly communicates with a vehicle device of another vehicle. When the operation mode is changed to the minimized mode, a warning signal indicating that the risk minimization operation is performed is transmitted through the communication unit, and the transmitted warning signal is sequentially transmitted to the devices of the plurality of different vehicles in the order of approaching the vehicle. A vehicle apparatus for switching the driving mode of the autonomous vehicle.
| 7. A method for switching a driving mode of a vehicle apparatus of an autonomous vehicle, the method comprising: a fully autonomous driving level controlled by a processor of the vehicle apparatus, and a semi-autonomous driving level controlled by the processor by a driver's operation of the vehicle; Performing autonomous driving according to any one of autonomous driving levels; A sensor unit which divides the outside of the vehicle into a plurality of areas and includes a plurality of sensors for detecting objects in the plurality of divided areas, and controls a plurality of systems for controlling the longitudinal and transverse driving of the vehicle. A driving unit, a position information unit for deriving position information of the vehicle using a GPS (Global Positioning System) signal, a differential GPS (DGPS) signal, and an inertial sensor signal, the sensor unit, the driving unit, and the position information unit. Determining whether at least one function of the control unit including two or more processors for controlling the vehicle is a fail state rather than a normal state; Performing a reboot if the determination result results in the failing state; And after the rebooting, depending on the state of the sensor unit, the driving unit, and the location information unit, whether the autonomous driving level is the full autonomous driving level or the semi-autonomous driving level, and the state of the plurality of items. Switching to one of a limited driving mode in which the driving of the vehicle is controlled by the driver and the processor and a manual driving mode in which the driving of the vehicle is controlled by the driver's operation; In the step of switching from the autonomous driving mode of the semi-autonomous driving level to the one of the limited driving mode and the manual driving mode after the rebooting, If it is possible to detect all of the plurality of regions by using some of the plurality of sensors of the sensor unit, and switches to the limited driving mode, if it is unable to detect any of the plurality of regions to switch to the manual driving mode, the driving unit If it is not possible to control at least one of the longitudinal and transverse driving of the vehicle through, switch to the manual driving mode, and if it is possible to control both the longitudinal and transverse driving of the vehicle, switch to the restricted driving mode, When the accuracy of the location information is degraded according to the availability of the GPS signal, the DGPS signal, and the inertial sensor signal of the location information unit, the control unit switches to the limited driving mode and, if the position information cannot be calculated, switches to the manual driving mode. If both processors of the control unit are normal, the control unit switches to the limited driving mode, and either function of the two processors is disabled. A method for the operation mode switching, characterized in that the switching between the manual driving mode.
| 8. A computer-readable recording medium having recorded thereon a program for performing the method for switching the driving mode according to claim 7. | The apparatus (100) has a camera unit for photographing a driver of a vehicle (10). A sensor unit divides an outer side of the vehicle into multiple areas, and is provided with multiple sensors for detecting objects of the divided areas. A driving unit controls multiple systems for controlling longitudinal and lateral movements of the vehicle. A location information unit derives location information of the vehicle using a global positioning system (GPS) signal, a differential GPS (DGPS) signal and an inertial sensor signal, where a manual driving mode of a control unit is switched to a limited driving mode when the location information of the vehicle is not calculated. INDEPENDENT CLAIMS are also included for the following:a method for switching a driving mode of an autonomous vehiclea computer-readable recording medium for storing a set of instructions for performing a method for switching a driving mode of an autonomous vehicle. Apparatus for switching a driving mode of an autonomous vehicle. The manual driving mode of the control unit is switched to the limited driving mode when the location information of the vehicle is not calculated so as to perform autonomous driving of the vehicle and minimize risk of the driver by diagnosing states of multiple modules of the vehicle. The drawing shows a schematic view of an apparatus for switching a driving mode of an autonomous vehicle. '(Drawing includes non-English language text)' 10Vehicle100Apparatus for switching driving mode of autonomous vehicle200Roadside device300Traffic server |
Please summarize the input | AUTONOMOUS DRIVING GUIDANCE SYSTEM AND OPERATION METHOD THEREOFIn the control server for monitoring the traffic state of a plurality of road sections according to an embodiment of the present application, the control server communicates with a plurality of vehicles and a plurality of traffic lights, the plurality received from the plurality of traffic lights Based on the traffic information for each road section for each of the road sections, the driving route change of the plurality of vehicles is induced.
|1. A control server for monitoring traffic conditions of a plurality of road sections, the control server communicating with a plurality of vehicles and a plurality of traffic lights, and a road section for each of the plurality of road sections received from the plurality of traffic lights A first vehicle communication unit for inducing a change in a driving route of the plurality of vehicles based on the traffic information, and each of the plurality of vehicles is in short-distance communication with the plurality of traffic lights; A time information recording unit storing time information communicated through the first vehicle communication unit;
A second vehicle communication unit that transmits departure and destination information to the control server; And a driving route determining unit for driving the corresponding vehicle according to any one driving route selected by a passenger among at least one driving route received from the control server through the second vehicle communication unit.
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| 3. The method of claim 1, wherein the driving route determining unit calculates a passage time of the at least one driving route, and automatically determines a driving route in which the passing time of the at least one driving route is the shortest time according to whether or not the occupant is selected. Control server decided by.
| 4. According to claim 1, The control server, A first server communication unit for wireless communication with the plurality of vehicles; And first and second traffic lights located within a predetermined distance in the starting and destination information received through the first server communication unit, and selecting a driving path through the plurality of road sections. Control server including a selection unit.
| 5. The control server according to claim 4, wherein the driving route selection unit selects at least one link signal light located within a predetermined distance among a plurality of candidate signal lights located within a predetermined area between the first and second traffic lights.
| 6. The method of claim 4, wherein the driving route selection unit maps the at least one link signal light to a map map stored in advance according to a preset search radius and azimuth range to derive a plurality of candidate routes, and the plurality of candidate routes. The control server selecting the at least one driving route for the departure and destination information in the order of the shortest distance.
| 7. According to claim 1, The control server, A second server communication unit for wireless communication with the plurality of traffic lights;
A traffic state determination unit for determining a traffic state for the plurality of road sections based on the traffic information for each road section; And a storage DB for classifying and storing the traffic information and the traffic conditions for each road section.
| 8. The method of claim 7, wherein the traffic state determining unit sets a confidence interval for each road section according to an average and a standard deviation of the passage time for each road section included in the traffic information for each road section, and the passage time for each road section. The confidence intervals for each road section are compared, and when a passage time of any one of the road sections of the road section is included in the corresponding trust section, a weighted moving average method is used for the passage time of the one road section. A control server that calculates an average driving time and deletes the passing time of any one of the road sections from the storage DB when the passing time of any one of the road sections out of the corresponding confidence section.
| 9. According to claim 1, Each of the plurality of traffic lights, The first infrastructure communication unit for receiving the vehicle information through the V2I communication with the plurality of vehicles, and transmits the current traffic light information;
A second infrastructure communication unit for wireless communication with the control server through a network;
A section identification unit for identifying any one of the plurality of road sections according to the previous traffic light information included in the vehicle information;
A passage time identification unit responsive to the previous traffic light information to identify a driving time for any one of the road sections; And a traffic volume identification unit for identifying traffic volume for any one of the road sections based on vehicle model information included in the vehicle information.
| 10. The method of claim 9, The passage time identification unit, the time interval and the signal waiting time between the first communication time and the second communication time with the current traffic light included in the current traffic light information included in the previous traffic light information Control server for calculating the driving time for any one of the road sections by adding up.
| 11. The method according to claim 9, wherein the traffic amount identification unit accumulates one of the number of traveling vehicles of the preset vehicle type when the vehicle type information corresponds to a preset vehicle model, and according to the accumulated number of traveling vehicles, the one of the roads Acquiring the traffic volume for the vehicle type preset in the section, and obtaining the traffic volume according to the total number of vehicles in any one of the road sections when the vehicle model information does not correspond to the preset vehicle type or the traffic volume for the preset vehicle type is acquired. Control server.
| 12. A plurality of vehicles that autonomously drive from the origin to the destination along a plurality of road sections;
A plurality of traffic lights located on one side and the other side of each of the plurality of road sections, and communicating with the plurality of vehicles to obtain traffic information for each road section; And a control server that induces a change of a driving route of the plurality of vehicles based on the traffic information for each road section, and each of the plurality of vehicles includes a first vehicle communication unit for short-range communication with the plurality of traffic lights. ; A time information recording unit storing time information communicated through the first vehicle communication unit;
A second vehicle communication unit that transmits departure and destination information to the control server; And a driving route determining unit for driving the corresponding vehicle according to any one driving route selected by a passenger among at least one driving route received from the control server through the second vehicle communication unit.
| 13. A method of operating an autonomous driving guidance system, comprising: autonomous driving of a plurality of vehicles from a source to a destination along a plurality of road sections;
Acquiring traffic information for each road section through V2I communication with the plurality of vehicles by a plurality of traffic lights located at one side and the other of each road section among the plurality of road sections;
Monitoring, by a control server, traffic conditions for a plurality of road sections according to traffic information for each road section, which is wirelessly transmitted from the plurality of traffic lights through a network; And inducing, by the control server, a change in a driving route of the plurality of vehicles based on traffic conditions for a plurality of road sections, and the autonomous driving of the plurality of vehicles comprises: Transmitting origin and destination information;
Receiving traffic information for at least one driving route and at least one driving route determined according to the traffic state of the plurality of road sections from the control server;
Calculating a passing time for the at least one driving route;
Providing the passing time and the traffic information for the at least one driving route to a passenger; And automatically determining a driving route in which the passing time is the shortest time among the at least one driving route, according to whether or not the occupant selects the driving method.
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| 14. The method of claim 13, wherein the step of inducing a route change for autonomous driving comprises: receiving departure and destination information from any one of the plurality of vehicles; Selecting at least one link traffic light based on the positions of the first and second traffic lights located within a predetermined distance to the departure and destination information;
Deriving a plurality of candidate paths by mapping the at least one link signal light to a pre-stored map map according to a preset search radius and azimuth range; And selecting the at least one driving path and providing it to the vehicle in the shortest distance among the plurality of candidate paths.
| 16. The method of claim 13, wherein the monitoring of traffic conditions of the plurality of road sections comprises: receiving traffic information for each road section from the plurality of traffic lights;
Comparing whether the number of driving vehicles for a predetermined vehicle type of one of the plurality of road sections corresponds to a first threshold;
Determining a certain road section as a caution state when the number of driving vehicles for a predetermined vehicle type of the road section corresponds to the first threshold;
Comparing whether the total number of vehicles in one of the road sections corresponds to a second threshold when the number of vehicles for a predetermined vehicle type in the one section does not correspond to the first threshold; And if the total number of vehicles in any one of the road sections corresponds to the second threshold, judges the one section of the road in the state of caution, and the total number of vehicles in the one section of the road does not correspond to the second threshold. If not, the operation method of the autonomous driving guidance system comprising the step of determining any one of the road section as a safe state.
| 17. The method of claim 13, wherein the monitoring of traffic conditions of the plurality of road sections comprises: setting a confidence interval for each road section according to an average and a standard deviation of a passing time included in the traffic information for each road section;
Comparing the passage time for each road section and the confidence interval for each road section;
Calculating an average driving time through a weighted moving average method for the passing time of any one of the road sections when the passing time of any one of the road sections is included in the corresponding confidence section; And deleting the passage time of the one road section when the passage time of the one road section out of the corresponding confidence section among the passage times for each road section.
| 14. The method of claim 13, wherein the step of acquiring traffic information for each of the road sections by the plurality of traffic lights comprises: receiving vehicle information through the V2I communication from any one of the plurality of vehicles;
Transmitting current traffic light information to the any one vehicle;
Detecting previous traffic light information from the vehicle information;
Identifying one of the plurality of road sections according to the previous traffic light information and the current traffic light information; And transmitting the traffic information for any one of the road sections to the control server.
| 19. The method of claim 18, wherein the step of acquiring traffic information for each of the road sections by the plurality of traffic lights comprises: identifying a first communication time with a previous traffic light and a second communication time with a current traffic light based on the vehicle information. step;
Determining whether a signal waiting time is included in a time period between the first and second communication times;
If a signal waiting time is included in the time period, summing the time period and the signal waiting time to determine a driving time for the one road section; And when the signal waiting time is not included in the time period, determining the time period as the driving time for the one road section.
| 20. The method of claim 18, wherein the step of obtaining traffic information for each of the road sections by the plurality of traffic lights comprises: extracting vehicle model information from the vehicle information;
Comparing whether the vehicle model information corresponds to a preset vehicle model;
Accumulating one in the number of vehicles of a preset vehicle that has traveled on any one of the road sections when the vehicle model information corresponds to a preset vehicle model;
Acquiring traffic volume for a predetermined vehicle type in any one of the road sections through a predetermined discrimination coefficient according to the accumulated number of traveling vehicles;
When the vehicle model information does not correspond to a preset vehicle model, or when a traffic volume of the preset vehicle model is obtained, the one of the ones may be determined through the predetermined discrimination coefficient according to the total number of vehicles traveling on one of the road sections. Obtaining traffic volume for the entire vehicle of the road section; And transmitting the traffic volume for the preset vehicle and the traffic volume for the entire vehicle to the control server at a predetermined time period. | The system (10) has a control server (300) for monitoring traffic conditions of road sections. A control server is communicated with vehicles (100-1-100-N) and traffic lights (200-1-200-N). A first vehicle communication unit induces change in a driving route of the vehicles based on traffic information, where the vehicles are in short-distance communication with the traffic lights. A time information recording unit stores the time information communicated through the first vehicle communication unit. A second vehicle communication unit transmits departure and destination information to the control server. A driving route determining unit drives a corresponding vehicle according to the driving route that is selected by a passenger and received from the control server through the second vehicle communication unit. An INDEPENDENT CLAIM is included for a method of operating an autonomous driving guidance system. Autonomous driving guidance system for a vehicle. The system induces autonomous driving from a departure point to an destination along an optimized road section for vehicles by reflecting the traffic condition of entire road. The drawing shows a block diagram of an autonomous driving guidance system. (Drawing includes non-English language text). 10Autonomous driving guidance system50Network100-1-100-NVehicles200-1-200-NTraffic lights300Control server |
Please summarize the input | Vehicle secondary accident prevention navigation, vehicle secondary accident prevention system and method using the sameThe vehicle secondary accident prevention system according to an embodiment of the present invention receives and stores driving information from an in-vehicle navigation or an in-vehicle information system, performs accident determination based on vehicle driving information, and determines whether an accident A control server that can generate an accident notification message and transmit it to the navigation of the corresponding vehicle or, if it is not an accident, generate a step-by-step driving warning notification message according to the degree of driving speed and transmit it to the navigation of the vehicle.|1. delete
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| 3. It receives and stores driving information from the in-vehicle navigation or in-vehicle information system, determines whether there is an accident risk according to the degree of accident or driving speed based on the driving information of the vehicle, and generates an accident notification message according to whether the accident is determined and a control server capable of transmitting to the navigation of the corresponding vehicle, or generating a step-by-step driving warning notification message according to the degree of driving speed in case of no accident and transmitting it to the navigation of the vehicle, wherein the control server transmits the driving information to the vehicle a collection unit for receiving data from my navigation or information system through a communication network and storing it in a database;
A communication unit that provides a communication protocol compatible to be connected to a communication network and transmits an accident notification message according to whether the driving information is received or an accident determination or a driving warning notification message according to the degree of driving speed to the navigation device or the mobile terminal;
It is provided from the collecting unit that receives the driving information from the in-vehicle navigation or the in-vehicle information system, and based on the driving information of the vehicle, determines whether or not a vehicle accident occurs or sets the risk reference value for the driving speed step by step, according to the degree of the driving speed an accident judgment unit that determines whether there is an accident risk;
A notification unit that generates an accident notification message according to whether the accident determination unit determines an accident or generates a step-by-step driving warning notification message according to the degree of driving speed, and transmits the generated accident notification message or driving warning notification message to a mobile terminal or navigation system ;
Receives autonomous driving information through ITS traffic information or V2X communication of an autonomous driving server connected through an in-vehicle autonomous driving system or communication network if the vehicle is an autonomous vehicle, and determines whether an accident occurs in connection with the driving information Further comprising a determination support unit that connects with the ITS or in-vehicle autonomous driving system or autonomous driving server, receives traffic information or autonomous driving information, and provides the information to the accident determination unit, wherein the accident determination unit includes the information contained in the driving information. In order to detect a sudden change in driving speed, acceleration information is received, a reference value for acceleration is set, and a vehicle accident is determined depending on whether the reference value is exceeded, or driving information is received between adjacent vehicles as traffic information in connection with the ITS. and comparing the driving speed between the front vehicle and the rear vehicle included in the driving information with the normal average speed of the section to determine whether there is an accident between vehicles or whether there is an accident risk equivalent to an accident, and the control server is As driving information that becomes It is possible to receive autonomous driving information through ITS traffic information, an in-vehicle autonomous driving system or V2X communication of an autonomous driving server connected through a communication network, and determine whether an accident has occurred in connection with the driving information through the accident determination unit. Vehicle secondary accident prevention system, characterized in that.
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| 8. A method for preventing secondary vehicle accidents using the system for preventing secondary vehicle accidents according to claim 3, comprising: periodically collecting vehicle driving information by connecting the control server to a navigation system or an in-vehicle information system through a communication network;
determining, by the control server, whether a vehicle accident has occurred by checking a change in vehicle speed using acceleration information included in the collected vehicle information;
When the control server is determined to be a vehicle accident, generating an accident notification message, and transmitting the accident notification message to a mobile terminal set to receive notification from the control server Second vehicle accident prevention method comprising the step of. | The system has an accident determination unit which determines whether a vehicle accident occurs according to whether the reference value is exceeded. The driving information between adjacent vehicles is received as traffic information in connection with an intelligent transportation system (ITS) (400). Determination is made whether there is an accident between vehicles or the risk of an accident equivalent to an accident by comparing the driving speed between the front and rear vehicles with the normal average speed of the section. A control server (200) receives autonomous driving information as driving information as a reference for accident determination, ITS traffic information, and autonomous driving information through vehicle-to everything (V2X) communication of an autonomous driving server connected through an in-vehicle autonomous driving system or a communication network (500), and determines whether an accident has occurred in connection with the driving information. An INDEPENDENT CLAIM is included for a method for preventing secondary vehicle accidents using system for preventing secondary vehicle accidents. System for preventing secondary vehicle accidents, used for quickly transmitting information about traffic accident to disaster center or driver. The leakage of personal information is prevented by protecting data when collecting personal information based on block chain. The drawing shows a block diagram of the vehicle secondary accident prevention system. (Drawing includes non-English language text) 100Navigation system200Control server300Mobile terminal400ITS500Communication network |
Please summarize the input | Lateral Control Mode Decision Method for Truck PlatooningThe present invention relates to a method for determining a platooning lateral control operation mode for trucks, and more particularly to a control method for lateral control of a group of autonomous vehicles, such as heavy trucks, performing platooning according to the operation mode. According to the present invention, when a following vehicle joins a platooning rank, a preceding vehicle through a Platooning Control Unit (PCU) and a Platooning Lateral Control System (PLCS) mounted on the following vehicle controlling steering of the following vehicle in a following mode by receiving driving information of the vehicle; detecting a plurality of events by monitoring an operation state of the following vehicle in the Platooning Lateral Control System (PLCS); and performing at least one of releasing the following mode, leaving the queue, or transferring control to a driver when the plurality of events occur. . According to the present invention, there is an advantage in that it is possible to present an effective lateral control mode determination method according to the occurrence of an event during platooning.|1. When the following vehicle joins the platooning rank, the driving information of the preceding vehicle is transmitted through the Platooning Control Unit (PCU) and Platooning Lateral Control System (PLCS) mounted on the following vehicle. receiving the reception and controlling steering of the following vehicle in a following mode;
detecting a plurality of events by monitoring an operation state of the following vehicle in the Platooning Lateral Control System (PLCS); and performing at least one of releasing the following mode, leaving the queue, or transferring control to a driver when the plurality of events occur.
| 2. The freight vehicle of claim 1, wherein the controlling of the steering of the following vehicle in the following mode comprises checking a vehicle to vehicle (V2V) communication state of the following vehicle with the preceding vehicle or a state of a lane detection sensor. How to determine the platooning lateral control operating mode.
| 3. The method according to claim 2, wherein when the communication state or the lane detection sensor state is normally operated, generating a route for performing a function of following the preceding vehicle and maintaining a lane. .
| 4. The platooning platooning lateral control system of claim 1, wherein the monitoring of the operating state of the following vehicle further comprises determining whether a Platooning Lateral Control System (PLCS) of the following vehicle is operating normally. How to determine the directional control mode of operation.
| 5. The platooning lateral control system (PLCS) of claim 1 , wherein the plurality of events include a decrease in reliability of lane information of the preceding vehicle or the following vehicle, reception of a lane change request, occurrence of an emergency braking situation, and a Platooning Lateral Control System (PLCS). A method for determining an operation mode of a freight vehicle platooning lateral control, characterized in that at least one of receiving a stop signal from the vehicle and generating a cut-in of another vehicle.
| 6. The method of claim 5 , further comprising: reconfirming the driving information of the preceding vehicle when the reliability of the lane information is reduced or a lane change request is received; and determining whether the reliability is restored or the lane change is completed.
| 7. The method of claim 5, further comprising maintaining the steering angle until the following vehicle stops when the emergency braking situation occurs.
| 8. [Claim 6] The determination of the operation mode of the platooning platooning control operation of trucks according to claim 5, comprising maintaining an existing lane for a predetermined time when a stop signal is received from the platooning lateral control system (PLCS). Way.
| 9. The method of claim 5 , further comprising: maintaining an existing lane for a predetermined time when a cut-in of the other vehicle occurs; and determining whether the other vehicle departs for the predetermined time period.
| 10. The method of claim 9, wherein when the other vehicle departs, the existing steering is maintained as it is, and when the other vehicle does not depart, the vehicle platooning lateral control operation mode is determined, comprising maintaining the existing lane for a predetermined time. Way. | The method involves transmitting the driving information of the preceding vehicle through the Platooning Control Unit (PCU) and Platooning Lateral Control System (PLCS) mounted on the following vehicle, receiving the reception and controlling steering of the following vehicle, and detecting an event by monitoring an operating state of the following vehicle through sensors mounted on the following vehicle. When the event occurs, a step of changing the following, is followed by leaving the queue, or transferring control to a driver. INDEPENDENT CLAIMS are included for the following:a computer readable recording medium andan apparatus for determining a lateral control operating mode of a following vehicle. Method for determining a lateral control operation mode of platooning vehicles. The method presents an effective lateral control mode determination according to the occurrence of an event during platooning. The drawing shows a control process of a Platooning Control Unit (PCU) and a Platooning Lateral Control System (PLCS). |
Please summarize the input | Longitudinal queue associated vehicle system under influence of communication time delay and fuzzy control method thereofThe invention claims a longitudinal queue associated vehicle system under the influence of communication time delay and fuzzy control method thereof, by collecting signal and calculating to obtain the speed of the vehicle, acceleration, position information, associating the error and error change rate between the vehicles, starting from the one-way strong coupling, describing the longitudinal queue control problem with CACC function as the discrete correlation system under the influence of the communication time delay, constructing the correlation system model; then using partial decomposition method and lyapunov function method, obtaining communication time delay ensuring queue associated system stable condition, ignoring the vehicle state information beyond the communication time delay, only using vehicle state information in the communication time delay range; at last, using fuzzy PID control algorithm, combining the vehicle distance with the communication delay upper bound design the longitudinal queue cruise control strategy. The invention can reasonably respond to the acceleration or deceleration behavior of the front vehicle in the proposed control strategy, reach the expected CACC control performance, effectively compensate the influence of the communication delay.|1. A longitudinal queue associated vehicle system under influence of communication time delay, wherein it comprises a workshop communication module, an information collecting module, a cooperative decision module, a motion control module, wherein the workshop communication module, through the global positioning system (GPS) and the vehicle (V2V) communication, receiving and sending the state information of the vehicle; information collecting module, collecting the autonomous vehicle and queue vehicle comprises speed, position and acceleration information, calculating the actual distance between the front vehicle and the autonomous vehicle by the collected information, the relative speed of the front vehicle and the autonomous vehicle, the relative acceleration of the front vehicle and the autonomous vehicle and the error change rate; cooperative decision module, the relative vehicle distance and vehicle distance error of the front vehicle and the autonomous vehicle as input, according to the expected vehicle distance and actual vehicle distance error, combining the workshop kinematics model, ignoring the vehicle state information beyond the communication time delay; using the available vehicle state information in the communication time delay range ensuring the stable queue; a motion control module, comprising a control decision module, a fuzzy controller and a PID controller, for controlling the autonomous vehicle to follow the front vehicle at a desired speed and keep the safety distance; the control decision module determines the acceleration and deceleration behaviour of the autonomous vehicle according to the expected vehicle distance and the vehicle distance error; fuzzy controller according to the expected vehicle distance error and expected vehicle distance error change rate, dynamically outputting three PID parameters; PID controller according to the PID parameter output by the fuzzy controller to control the decision layer to determine the motion control mode, controlling the autonomous vehicle.
| 2. The longitudinal queue associated vehicle system under the influence of communication time delay according to claim 1, wherein the relative speed of the front vehicle and the autonomous vehicle provided by the vehicle state collecting module according to the safety state collecting module, communication time delay and associated system characteristic, the design control target is vehicle distance error and speed error.
| 3. The longitudinal queue associated vehicle system under the influence of communication time delay according to claim 1, wherein the workshop kinematics model is starting from the one-way strong coupling, and the longitudinal queue control problem with CACC function is described as the discrete state space equation under the influence of the communication time delay, constructing the longitudinal queue associated vehicle system under the influence of the communication time delay.
| 4. The longitudinal queue associated vehicle system under the influence of communication time delay according to claim 1, wherein the fuzzy controller is based on the workshop kinematics model. using the membership function fuzzy quantization to obtain the fuzzy input quantity and output the coefficient value of the PID controller by using the deviation of the expected vehicle distance and the actual vehicle distance and the vehicle distance error change rate.
| 5. A fuzzy control method of longitudinal queue associated vehicle system under the influence of communication time delay, applied to the longitudinal association system under the influence of communication time delay according to claim 1, wherein it comprises the following steps: a. collecting the movement state information of the front vehicle and the autonomous vehicle in the queue, using the sensor and wireless communication technology to obtain the vehicle driving, the braking process comprises position, vehicle speed and acceleration information, and calculating to obtain the speed error of other vehicle, acceleration error and error change rate; b, designing the control target, obtaining the position, vehicle speed and acceleration information, the communication time delay, association characteristic fusion and vehicle distance error is designed as the control target; c, obtaining communication time delay upper bound; d, designing the PID controller.
| 6. The fuzzy control method of longitudinal queue associated vehicle system under the influence of communication time delay according to claim 5, wherein in the step c, obtaining the communication time delay upper limit comprises the following sub-steps: c-1, establishing a communication time delay influence the longitudinal queue associated system model, starting from the one-way strong coupling characteristic, the longitudinal queue control problem with CACC function is described as the discrete correlation system under the influence of communication time delay, constructing the communication time delay influence the queue vehicle CACC associated system model; c-2, stability analysis, the longitudinal correlation system model established in step c-1 uses partial decomposition method and lyapunov function method to perform stability analysis and obtain the communication time delay ensuring the stable association queue.
| 7. The fuzzy control method of longitudinal queue associated vehicle system under the influence of communication time delay according to claim 5, wherein the design of the PID controller in the step d is specifically as follows: designing the input quantity of the fuzzy controller: using membership function fuzzy quantization to obtain the vehicle space error and vehicle distance error change rate to obtain two corresponding fuzzy input quantity, the fuzzy language value of vehicle distance error and vehicle distance error change rate is L, M, S and ZO, wherein L represents large, M represents moderate, S represents small, ZO represents zero, fuzzy controller according to fuzzy control rule designed to fuzzy reasoning and obtaining fuzzy output; combining the workshop distance control and communication time delay to control the stability of the whole CACC queue, designing the PID controller u is: u (k) = KP* e (k) + KI* ?e (k) + KD* (e (k + 1) - e (k)), wherein KP is the proportional coefficient, KI is the integral coefficient, KD is differential coefficient.
| 8. The fuzzy control method of longitudinal queue associated vehicle system under the influence of communication time delay according to claim 7, wherein for the input quantity, the Gauss type membership function is adopted, and the triangle membership function is adopted for the output quantity.
| 9. The fuzzy control method of longitudinal queue associated vehicle system under the influence of communication time delay according to claim 7, wherein the fuzzy control rule comprises: when the vehicle distance error is too large, accelerating the response speed of the rear vehicle, meanwhile, it avoids the over-range control effect caused by the start of the CACC array system; when the vehicle distance error is too small, adjusting the proportional coefficient and the integral coefficient, the CACC queue associated system has good steady state performance, at the same time, adjusting the differential coefficient, avoiding the system oscillation at the balance point; when the vehicle distance error is in the middle and so on, it should make the CACC queue associated with the system response, at the same time, ensure the response speed of the CACC queue system. | The system has a workshop communication module for receiving and sending state information of a vehicle through a global positioning system (GPS ) and vehicle (V2V) communication. An information collecting module collects speed, position and acceleration information of an autonomous vehicle. A cooperative decision module determines acceleration and deceleration behavior of the autonomous vehicle according to expected vehicle distance and vehicle distance error. A fuzzy controller dynamically outputs three proportional-integral-derivative (PID) parameters according to the expected car distance error and an expected car error change rate. The PID controller controls a decision layer to determine a motion control mode according to a PID parameter output by the fuzzy controller to control the autonomous car to control. An INDEPENDENT CLAIM is included for a fuzzy control method of longitudinal queue associated vehicle system under the influence of communication time delay. Longitudinal queue associated vehicle system under influence of communication time delay for longitudinal cooperative adaptive cruise control (CACC) under vehicle-vehicle communication. The acceleration or deceleration behavior of the front vehicle in the proposed control strategy is reasonably responded, the expected CACC control performance is reached, and the influence of the communication delay is effectively compensated. The drawing shows a flowchart illustrating the fuzzy control method of the longitudinal queue associated vehicle system. (Drawing includes non-English language text) |
Please summarize the input | System and Method for Automated Evaluation of Driving Ability of Self-Driving CarsA system and method for automatic driving ability evaluation of an autonomous driving system are provided. In the autonomous driving capability automatic evaluation system including a plurality of sensors, an integrated sensor platform, a battery and a switch proposed in the present invention, the integrated sensor platform uses each corresponding small processor for sensor data collected through a plurality of sensors. Autonomous driving processing unit that performs pre- and post-processing, receiving multiple post-processed sensor data from the autonomous driving processing unit, detecting lanes through sensor fusion, measuring vehicle speed, distance from the vehicle in front, whether emergency braking is activated, a sensor unit that detects objects and measures the relative position of objects in real time, and each scenario based on road traffic laws, including lane keeping scenarios and stability evaluations, lane change scenarios and stability evaluations to evaluate the results measured by the sensor unit It includes an evaluation unit that conducts star stability evaluation and a remote unit that transmits data to remotely monitor through an application to a driver who wants to evaluate the progress of the autonomous driving ability automatic evaluation evaluated by the evaluation unit.|1. In the autonomous driving ability automatic evaluation system including a plurality of sensors, an integrated sensor platform, a battery, and a switch, the integrated sensor platform pre-processes and post-processes sensor data collected through a plurality of sensors through respective corresponding small processors. an autonomous driving processing unit that performs; After receiving multiple sensor data post-processed by the autonomous driving processing unit, through sensor fusion, lane detection, vehicle speed measurement, distance to the vehicle in front, emergency braking operation, object detection, and object relative position are displayed in real time. a sensor unit to measure; an evaluation unit that performs stability evaluation for each scenario based on road traffic laws including lane keeping scenarios and stability evaluations, lane change scenarios and stability evaluations to be evaluated for results measured by the sensor unit; and a remote unit for transmitting data to remotely monitor the progress of the automatic evaluation of the autonomous driving ability evaluated by the evaluation unit through an application to a driver who wants to be evaluated, and the sensor unit includes a V2X OBU (Vehicle to Everything On-Board Unit)) The data transmitted from the GNSS (Global Navigation Satellite System) sensor and INS (Inertial Navigation System) sensor are combined using UDP (User Data Protocol) or CAN (Controller Area Network) communication, and the message output from the V2X sensor Among the three (Message Set), BSM (Basic Safety Message), SPAT (Signal Phase and Timing Message), TIM (Traveler Information Message), By combining RSA (Road Side Alert Message), data required for traffic light recognition (SPAT), data required for sensor fusion (BSM), TIM, and RSA are divided based on the distributed processing sensor combination method, and output from the GNSS sensor It receives latitude, longitude, and altitude, configures an environment for outputting own vehicle location and relative vehicle location data based on a distributed processing sensor combination method, and configures an environment for outputting roll, pitch, and yaw output from the INS sensor. (yaw), receives speed data and configures an environment for measuring the position and condition of its own vehicle based on a distributed processing sensor combination method, measuring speed, distance to other vehicle, emergency braking operation, object detection and object Performs sensor combination using an algorithm required to derive relative vehicle position data of, and the autonomous driving processing unit, Distributed processing method is used to perform pre-processing and post-processing through respective small processors that process multiple sensor data in order to generate data necessary for sensor fusion of distributed processing method for sensor data collected through multiple sensors. Autonomous driving capability automatic evaluation system.
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| 3. The autonomous driving capability of claim 1, wherein the evaluation unit receives an evaluation preparation signal from a driver who wants to be evaluated remotely through an application, and then based on a New Car Assessment Program (NCAP) stored in a road traffic law database and road traffic laws and regulations. The evaluation score is calculated using the results measured by the sensor unit according to the evaluation algorithm for each scenario for automatic evaluation, and the evaluation items of the automatic evaluation of autonomous driving ability include the lane keeping scenario and stability evaluation, and the lane change scenario and stability evaluation items. Autonomous driving ability evaluation system including.
| 4. The driver of claim 1, wherein the remote unit remotely controls on/off of the autonomous driving ability automatic evaluation system, receives an evaluation item to be evaluated from the driver through an application in a TCP/IP method, and is evaluated An autonomous driving ability automatic evaluation system that transmits the evaluation preparation signal received remotely through the application from the evaluator to the evaluation unit, and displays the evaluation progress of the current vehicle and the evaluation score calculated through the evaluation unit together with the evaluation start notification to the driver.
| 5. In the automatic evaluation method for autonomous driving capability of an automatic driving capability automatic evaluation system including a plurality of sensors, an integrated sensor platform, a battery, and a switch, the autonomous driving processing unit of the integrated sensor platform receives sensor data collected through a plurality of sensors, respectively. Performing pre-processing and post-processing through a corresponding small processor of; The sensor unit of the integrated sensor platform receives multiple sensor data post-processed by the autonomous driving processing unit and uses sensor fusion to detect lanes, measure vehicle speed, distance from vehicle in front, emergency braking operation, object detection, and object detection. Measuring the relative position of in real time; Performing stability evaluation for each scenario based on road traffic laws including lane keeping scenario and stability evaluation, lane change scenario and stability evaluation to be evaluated by the evaluation unit of the integrated sensor platform for the results measured by the sensor unit; and transmitting data so that the remote unit of the integrated sensor platform remotely monitors the progress of the automatic evaluation of autonomous driving capability evaluated by the evaluation unit through an application to a driver who wants to be evaluated, and the sensor unit of the integrated sensor platform performs autonomous driving. Receives multiple sensor data post-processed by the processing unit and measures lane detection, vehicle speed measurement, distance to the vehicle in front, emergency braking operation, object detection, and object relative position through sensor fusion in real time. In the step, the data transmitted from the V2X OBU (Vehicle to Everything On-Board Unit) sensor, GNSS (Global Navigation Satellite System) sensor, and INS (Inertial Navigation System) sensor is transmitted through UDP (User Data Protocol) or CAN (Controller Area Network) Combined using communication, Data required for traffic light recognition by combining BSM (Basic Safety Message), SPAT (Signal Phase and Timing Message), TIM (Traveler Information Message), and RSA (Road Side Alert Message) among message sets output from V2X sensors (SPAT), data required for sensor fusion (BSM), TIM, and RSA are divided based on the distributed processing sensor combination method, and the latitude, longitude, and altitude output from the GNSS sensor are received and based on the distributed processing sensor combination method to configure an environment for outputting the position data of own vehicle and the position of the other vehicle, receive the roll, pitch, yaw, and speed data output from the INS sensor, and use the distributed processing sensor combination method. Based on this, it configures an environment for measuring the location and status of its own vehicle, and measures speed, distance from the other vehicle, emergency braking operation, The self-driving processing unit of the integrated sensor platform, which performs sensor coupling using an algorithm necessary to detect an object and derive relative vehicle position data of the object, through each corresponding small processor for sensor data collected through a plurality of sensors. The step of performing pre-processing and post-processing includes pre-processing and post-processing through respective small processors that process a plurality of sensor data in order to generate data necessary for sensor fusion of a distributed processing method with respect to sensor data collected through a plurality of sensors. An automatic evaluation method for autonomous driving capability using a distributed processing method that performs post-processing.
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| 7. The method of claim 5, wherein the evaluation unit of the integrated sensor platform performs stability evaluation for each scenario based on road traffic laws, including lane keeping scenarios and stability evaluations, lane change scenarios and stability evaluations to be evaluated with respect to the results measured by the sensor unit. The step to be performed is to receive the evaluation preparation signal remotely through the application from the driver who wants to be evaluated, and then to automatically evaluate the autonomous driving ability based on the NCAP (New Car Assessment Program) and road traffic laws stored in the road traffic law database. The evaluation score is calculated using the results measured by the sensor unit according to the evaluation algorithm for each scenario, and the evaluation items for the automatic evaluation of autonomous driving ability are autonomous driving including lane keeping scenario and stability evaluation, lane change scenario and stability evaluation items. How to automatically evaluate your abilities.
| 8. The autonomous driving ability automatic evaluation system according to claim 5, wherein the remote unit of the integrated sensor platform transmits data to remotely monitor through an application to a driver who wants to evaluate the progress of the autonomous driving ability automatic evaluation evaluated by the evaluation unit Remotely control the on/off of the vehicle, receive the evaluation item to be evaluated from the driver through the application in TCP/IP method, and receive the evaluation preparation signal remotely input through the application from the driver who wants to be evaluated to the evaluation unit When transmitted, an automatic evaluation method for autonomous driving capability that displays the evaluation progress of the current vehicle and the evaluation score calculated by the evaluation unit along with an evaluation start notification to the driver. | The system has an integrated sensor platform (100) for pre-processing and post-processing sensor data collected through multiple sensors through a small processor. An autonomous driving processing unit (110) receives the post-processed sensor data from the system. An evaluation unit (130) performs stability evaluation for each scenario based on road traffic laws. A remote unit (140) transmits data to a driver to receive an evaluation of the progress of automatic evaluation of autonomous driving ability evaluated by evaluation unit. An INDEPENDENT CLAIM is also included for a method for automatic evaluation of autonomous driving capability of self-driving car. The system is useful for automatically evaluating autonomous driving capability of self-driving car. The driver can evaluate the safety and reliability of the autonomous driving system without restriction of location. The development of autonomous driving systems that comply with road traffic regulations and provisional driving permit regulations is promoted. The drawing shows a diagram illustrating the configuration of an integrated sensor platform of an automatic driving ability evaluation system of an autonomous driving system (Drawing includes non-English language text).100Integrated sensor platform110Autonomous driving processing unit130Evaluation unit140Remote unit |
Please summarize the input | A following control method of automatic driving vehicle, system and terminal and storage mediumThe invention claims a following control method of automatic driving vehicle, system and terminal and storage medium. The following control method through establishing the communication connection of the pilot vehicle and the following vehicle, and sending the request to the interactive terminal to obtain the user authorization, so as to generate a pilot signal based on the pilot vehicle information transmitted to the following vehicle, so as to control the following vehicle to follow the driving state of the pilot vehicle according to the pilot signal. The running state control of the pilot vehicle comprises the longitudinal control and the transverse control of the vehicle. The following control method of the automatic driving vehicle is shared by real-time information, the sensing ability of the member vehicle to the environment vehicle is expanded, so that the queue control accuracy and the traffic efficiency are improved. At the same time, the vehicle has a queue driving function, improves the aerodynamic performance, effectively reduces the following vehicle wind resistance area, reduces the vehicle power output. shortening the distance between the vehicle and the vehicle on the basis of ensuring the safety, reducing the speed fluctuation, which can effectively improve the traffic efficiency and reduce the energy consumption.|1. A following control method of automatic driving vehicle, wherein it is used for controlling the pilot vehicle in a preset fleet to guide and follow the vehicle, the following control method comprises the following steps: S1, establishing the communication connection of the pilot vehicle and the following vehicle, the establishing method comprises the following steps: S11, determining that the pilot vehicle is in the V2V communication range; S12, obtaining the pilot command of the pilot and controlling the following vehicle to enter the following mode; S13, judging whether the following vehicle is based on predetermined information to lock the pilot vehicle; is, executing S14; S14, based on the position information of the vehicle near the vehicle identifying the potential vehicle list, and receiving response of V2V information from the potential vehicle list, S15, judging whether the navigation vehicle is authorized and the communication of the following vehicle is successful; if so, executing S16; S16, when the following vehicle is in the normal vehicle tracking distance, judging whether the following vehicle is in the V2V communication range; if so, controlling the pilot vehicle to directly transmit the pilot vehicle information to the following vehicle; otherwise, using the redundant V2V signal to transmit the pilot vehicle information to the following vehicle; S2, sending a request to the interactive end to obtain the user authorization; S3, generating a pilot signal based on the pilot vehicle information transmitted to the following vehicle, so as to control the following vehicle to follow the driving state of the pilot vehicle according to the pilot signal; the driving state control of the pilot vehicle comprises longitudinal control and transverse control of the vehicle; wherein the longitudinal control logic of the pilot vehicle is: real-time collecting speed deviation of the pilot vehicle actual speed and a predetermined speed, and calculating according to the speed deviation to obtain the acceleration pedal control quantity of the pilot vehicle, gear shifting control quantity and brake control quantity, so as to utilize a switching logic to control from the accelerator pedal, selecting one of the shift control and the brake control to compensate the speed deviation; the transverse control logic of the pilot vehicle is: real-time collecting the steering deviation of the expected steering and actual steering of the pilot vehicle, and calculating according to the steering deviation to obtain steering control quantity and gear shifting control quantity, so as to use the switching logic to select one of the gear shifting control and steering control to compensate the steering deviation; wherein the sum of the current steering and steering pre-estimation of the pilot vehicle is used as the steering feedback quantity, the difference between the expected steering and the steering feedback quantity is used as the input steering deviation.
| 2. The following control method of automatic driving vehicle according to claim 1, wherein the running state control of the following vehicle comprises the longitudinal following control of the vehicle, the transverse following control and the signal lamp following control. wherein the following control logic of the signal lamp of the following vehicle is: obtaining the predetermined light state according to the guide signal, calculating the light state deviation of the following vehicle current light state and the predetermined light state, so as to obtain the lamp light signal control information according to the light state deviation and controlling the current light state of the following vehicle.
| 3. The following control method of the automatic driving vehicle according to claim 2, wherein the control object of the signal lamp following control comprises a steering lamp of the vehicle, a brake lamp, an energy recycling lamp and warning light recycling lamp.
| 4. The following control method of automatic driving vehicle according to claim 1, wherein in S13, when the following vehicle is not based on the predetermined information locking the pilot vehicle, executing S21; S21, controlling the following vehicle to continuously receive V2V information from the nearby vehicle.
| 5. The following control method of automatic driving vehicle according to claim 1, wherein in S15, when the unauthorized pilot vehicle and the following vehicle are successful, returning to S14 to re-confirm the V2V communication signal of said potential pilot vehicle list.
| 6. A navigation-following vehicle V2V cooperative guiding system, wherein it uses the following control method of the automatic driving vehicle according to any one of claims 1 to 1 to 5; the V2V Collabored pilot system of the pilot-following vehicle comprises: a pilot vehicle cooperative guide module, which is set in the pilot vehicle, comprising a pilot V2V communication interface, a pilot navigation control module and a pilot information analysis processing module; the navigation V2V communication interface sends the V2V information of the pilot vehicle and receives the V2V information of the following vehicle; the pilot navigation control module is used for controlling the driving state of the pilot vehicle; the navigation information analysis processing module is used for analyzing V2V information received through the pilot V2V communication interface, so as to identify each nearby vehicle and/or identifying nearby vehicle associated with the specific passenger identification data, and a following vehicle system guide module, which is set in the following vehicle, comprising a following guide control module, a following V2V communication interface and following information analysis processing module; the following guide control module is used for controlling the running state of the following vehicle; the following V2V communication interface is used for sending the V2V information of the following vehicle and the V2V information of the receiving pilot vehicle; the following information analysis processing module is used for analyzing V2V information received by the following V2V communication interface, so as to identify the pilot vehicle.
| 7. The V2V cooperative guiding system of the pilot-following vehicle according to claim 6, wherein the pilot vehicle cooperative guiding module further comprises a pilot redundant communication interface; the following vehicle cooperative guide module further comprises a following redundant communication interface; wherein the navigation redundant communication interface and the following redundant communication interface are corresponding to each other for transmitting and receiving V2V information between each other, the V2V information comprises information using DSRC communication type.
| 8. The navigation-following vehicle V2V cooperative guiding system according to claim 7, wherein the navigation redundant communication interface and the following redundant communication interface adopt one or more of the following communication interface types: mobile phone Wi-Fi network, a Wi-Fi network, a ZigBee, a Z-wave communication, a vehicle communication to the infrastructure, the vehicle to the pedestrian communication, the vehicle device communication and the vehicle to the network communication.
| 9. A computer terminal, comprising a memory, A processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program when executing the following control method of the automatic driving vehicle according to any one of claims 1 to 1 to 5.
| 10. A computer readable storage medium, on which a computer program is stored, wherein when the program is executed by a processor, the following control method of the automatic driving vehicle according to any one of claims 1 to 1 to 5 is realized. | The method involves establishing (S1) a communication connection of a pilot vehicle and a following vehicle. The request is sent (S2) to the interactive end to obtain the user authorization. A pilot signal is generated (S3) based on the pilot vehicle information transmitted to the following vehicle, so as to control the following vehicle to follow the driving state of the pilot vehicle according to the pilot signal. The steering deviation of the expected steering and actual steering of the pilot vehicle is calculated in real time, according to the steering deviation to obtain steering control quantity and gear shifting control quantity, so as to use the switching logic to select one of the gear shifting control and steering control to compensate the steering deviation. The difference between the expected steering and the steering feedback quantity is used as the input steering deviation. INDEPENDENT CLAIMS are included for: 1. a navigation-following vehicle V2V cooperative guiding system; 2. a computer terminal; and 3. a computer readable storage medium storing computer program for performing process for controlling automatic driving vehicle. Following-up control method for controlling automatic driving vehicle. The sensing ability of the member vehicle to the environment vehicle is expanded, so that the queue control accuracy and the traffic efficiency are improved. The vehicle has a queue driving function, improves the aerodynamic performance, effectively reduces the following vehicle wind resistance area, reduces the vehicle power output, shortens the distance between the vehicle and the vehicle on the basis of ensuring the safety, and reduces the speed fluctuation, which can effectively improve traffic efficiency and reduce the energy consumption. The drawing shows a flow diagram illustrating the process for controlling automatic driving vehicle. (Drawing includes non-English language text)S1Step for establishing communication connection of pilot vehicle and following vehicle S2Step for sending request to interactive end to obtain user authorization S3Step for generating pilot signal based on pilot vehicle information |
Please summarize the input | PHEV hybrid vehicle group optimization control method of queue management and adaptive cruise controlThe invention claims a PHEV hybrid vehicle group optimization control method of queue management and self-adaptive cruise control, wherein the control method comprises the following steps; step one, clustering the driving data of multiple working conditions by multiple characteristic parameters to determine multiple driving styles; step , determining the vehicle lane-changing safety area according to the obtained vehicle state information and the surrounding environment information; step three, in the self-adaptive cruise control, based on the safety and comfort, restraining the vehicle distance, vehicle speed and acceleration, and reasonably recycling the braking energy when braking; when it is close to the crossroad, the queue is recombined again to make the vehicle group pass through in order; step four, based on the sample data in the training environment of Soft Actor-Critic strengthening learning algorithm, continuously iteratively updating according to the set loss function, finally obtaining the optimal strategy capable of making the vehicle group rearrange the queue according to the driving style of different drivers on the road not more than three lanes; The invention can rationally plan the vehicle queue according to the driving intention of the driver so that the vehicle queue can efficiently drive.|1. A PHEV hybrid vehicle group optimization control method for queue management and adaptive cruise control, wherein the mixed vehicle group is a vehicle group composed of an intelligent network vehicle and a conventional autonomous vehicle of manual driving; step one, clustering the driving data of multiple working conditions by multiple characteristic parameters to determine multiple driving styles; step , determining the vehicle lane-changing safety area according to the obtained vehicle state information and the surrounding environment information; step three, in the self-adaptive cruise control, based on the safety and comfort, restraining the vehicle distance, vehicle speed and acceleration, and reasonably recycling the braking energy when braking; when it is close to the crossroad, the queue is recombined again to make the vehicle group pass through in order; step , based on the sample data in the SoftActor-Critic strengthening learning algorithm training environment, continuously iteratively updating according to the set loss function, finally obtaining the optimal strategy capable of making the vehicle group rearrange the queue according to the driving styles of different drivers on the road not more than three lanes.
| 2. The PHEV hybrid vehicle group optimization control method for queue management and adaptive cruise control according to claim 1, wherein the vehicle at the head in the vehicle queue of the vehicle group is intelligent network vehicle, the queue length is limited to be not more than 8 vehicles; In step one, the specific classification method of different driving styles is to reduce the dimension of the characteristic parameters from the historical data of a large number of manual driving vehicles through the principal component analysis method, and then the classified three driving styles are obtained by K-mean algorithm clustering, and the different driving styles are embodied in the following characteristic parameters: average longitudinal velocity the maximum longitudinal vehicle speed v max, the minimum longitudinal vehicle speed v min, the longitudinal vehicle speed standard deviation e v, longitudinal acceleration average value/> the maximum value of the longitudinal acceleration ax max, the minimum value of the longitudinal acceleration ax min, the standard deviation of the longitudinal acceleration sigma x, the average value of the transverse acceleration/> transverse acceleration maximum value ay max, transverse acceleration minimum value ay min, transverse acceleration standard deviation sigma y, vehicle head time distance THW, collision time parameter TTC, minimum vehicle head distance DHWmin; The standard deviation [e] of the longitudinal vehicle speed is calculated as follows: The vehicle head time interval THW is the time taken from the vehicle head of the main vehicle to the vehicle tail of the front vehicle under the current vehicle speed, the collision time parameter TTC is the time needed by the collision between the main vehicle and the front vehicle under the current state, and the calculation formula is as follows: .upsilon. rel=.upsilon. p- .upsilon. wherein drel is the relative distance between two vehicles, vp is the speed of the main vehicle, vf is the speed of the front vehicle; the distance between the vehicle heads is the distance between the vehicle head of the main vehicle and the vehicle head of the front vehicle in the same lane, the larger the value is, the larger the distance between the two vehicles is, the smaller the possibility of collision accident between the two vehicles is; on the other hand, the accident possibility of the two vehicles is larger; The smaller the distance between the vehicle heads, the more aggressive the driving of the driver is reflected, so the minimum distance between the vehicle heads is selected as the characteristic parameter index of the driving style.
| 3. The PHEV hybrid vehicle group optimization control method for queue management and adaptive cruise control according to claim 1, wherein in step two, the driving road of the vehicle queue is three lanes, the vehicle group recombination process relates to lane changing operation, the network-connected vehicle obtains the surrounding vehicle and environment information through V2V communication and vehicle-mounted sensor, the information comprises vehicle speed, vehicle body length, torque, power, vehicle distance, vehicle position information, intersection signal lamp phase; The method for detecting the lane-changing security area used in the lane-changing operation is as follows: The method is based on vehicle kinematics, in the method, when the vehicle is at a certain position, the vertex coordinates [xp1 (t), yp1 (t)] in the right front end direction are expressed as: wherein: v p (t), theta p (t) are respectively the main vehicle speed and yaw angle of the vehicle; tm is the initial time point of the main vehicle lane changing, tn is the final time point; Similarly, the coordinates of the left front vertex [xp2 (t), yp2 (t)], the left rear vertex [xp3 (t), yp3 (t)] and the right rear vertex [xp4 (t), yp4 (t)] of the main vehicle at time t are: in the formula: a is the length of the vehicle; b is the width of the vehicle; in the vehicle lane changing process, the main vehicle analyzes the reasonable vehicle lane changing safety area according to the condition that the main vehicle does not collide with the surrounding vehicle; assuming that the main vehicle is changed to the left at a certain time point in the future, the collision condition with the front vehicle is that the vehicle speed of the vehicle is greater than that of the front vehicle, the vehicle distance is gradually shortened, and the collision condition between the right front vertex of the main vehicle and the left rear vertex of the front vehicle will occur; The collision point is set as S1, and the collision time point is set as the coordinate of the collision point S1/> is represented by: in the formula: v f (t) is the speed of the front vehicle of the current lane at t moment, D1 is the distance between the main vehicle and the front vehicle of the lane; if there is a collision with the rear vehicle of the lane-changing target lane, the speed of the main vehicle is less than that of the rear vehicle, the distance between the two vehicles is reduced along with the time, at this time, the lane-changing target lane is changed, the left top point of the main vehicle is collided with the rear vehicle of the target lane; setting the collision point as S2, and the collision time point as then according to the vehicle structure size and autonomous vehicle kinematics theory, the coordinate of the collision point S2 is expressed as: in the formula: v r (t) is the speed of the vehicle behind the target lane at time t, D1 is the relative distance between the autonomous vehicle and the vehicle behind the target lane, and l is the width of the lane; according to the collision point S1, S2 point coordinate, determining the vehicle lane changing safety domain to avoid collision; the vehicle is changed to the right side so as to be in the same theory; the intelligent network vehicle judges whether the safety domain satisfies the lane changing condition according to the detected surrounding vehicle information.
| 4. The PHEV hybrid vehicle group optimization control method for queue management and adaptive cruise control according to claim 1, wherein in step three, each intelligent network vehicle is taken as an intelligent body, n network vehicles in the vehicle group are regarded as n intelligent bodies, the number n of the network vehicles in the vehicle group is limited in the range allowed by the calculation force, n network vehicles are controlled by n parallel intelligent bodies to realize interaction; n intelligent bodies share the same neural network and parameter; through the parameter sharing structure of the neural network algorithm, the improvement of the driving state of any network-connected vehicle is beneficial to the vehicle group reward gain; The intelligent network-linked vehicle in the vehicle queue interacts with the vehicle in the adjacent vehicle queue, at the same time, the intelligent network-linked vehicle in the vehicle queue and the manual driving vehicle also maintain interactive cooperation, the interactive cooperation method comprises: Method A, when on the common lane, vehicle queue through self-adaptive cruise with regenerative braking cooperative control, the vehicle between keep reasonable vehicle distance, namely two continuous vehicle must continuously keep a safe longitudinal gap; the deviation from the safe distance, that is, the distance error is as small as possible to reduce the collision risk, and give play to the low oil consumption of the vehicle queue, The advantage of high traffic throughput is that it is compatible with the randomness of the driving of the common vehicle, so the intelligent network vehicle and the common vehicle need larger vehicle distance, when the vehicle is braked, the motor recovers part of the braking energy; method B, when the vehicle is close to the intersection with signal lamp, each vehicle in the vehicle queue is split and recombined to reduce the energy consumption and driving time, so that part of the queue vehicles orderly pass through the intersection before the green light signal is stopped, and the rest vehicles wait before the stop line.
| 5. The PHEV hybrid vehicle group optimization control method for queue management and adaptive cruise control according to claim 4, wherein in step four, the SoftActor-Critic reinforcement learning algorithm is the SAC algorithm, which is the Off-policy model-free non-policy depth reinforcement learning algorithm combining the maximum entropy learning with the Actor-Critic framework; the learning content of the SAC reinforcement learning algorithm comprises a state s, an action a, a reward r and an environment model p; the state comprises oil consumption of vehicle, battery charge state, speed, acceleration, yaw angle, vehicle distance, action as torque, steering angle, reward as fuel consumption, driving time, comfort, self-adaptive cruise cost function; step four, SAC algorithm training and learning the sample data from the environment and continuously updating and optimizing to finally obtain the optimal strategy, so that the intelligent network vehicle in the mixed vehicle group can be distributed in different lanes according to the driving style of the driver, and the mixed vehicle in the same lane forms queues of different lengths; the driving styles of different vehicles are classified into radical type, stable type and prudent type; When the vehicle queue is running on the road and different vehicles are distributed with lanes, the vehicles of the radical style tend to be arranged on the leftmost lane, the robust vehicles tend to be on the middle lane, and the cautious vehicles are on the rightmost lane; the gain degree of the vehicle group is determined; The SAC algorithm adjusts the final distribution result of the vehicle running lane according to the gain degree of the vehicle group.
| 6. The PHEV hybrid vehicle group optimization control method for queue management and adaptive cruise control according to claim 5, wherein the SAC algorithm is composed of one actor neural network and four critic neural networks; the input of the actor neural network is state, the output is action probability distribution parameter P (x); 4 critic neural networks are divided into state value estimation v critic and v critic target network, action-state value estimation Q1 and Q2 critic neural network; the input of the Qcritic neural network is the state and the output is the value of the state; wherein the output of the Vcritic neural network is v (s), representing the estimation of the state value; the output of the Qcritic neural network is q (s, a), which represents the estimation of the action-state to the value; n intelligent bodies share the same neural network and parameter; through the parameter sharing structure of the neural network algorithm, the improvement of the driving state of any network-connected vehicle is beneficial to the vehicle group reward gain; In the algorithm, entropy is defined as: wherein x follows the probability density function P (x) distribution; the introduction of the maximum entropy makes the output of the action more dispersed, which avoids the excessive concentration of the output action, so as to improve the searching ability of the algorithm, the learning ability and stability of the new task; The optimal policy in the SAC algorithm framework is expressed as: Pi represents the strategy adopted by the intelligent body, a is action, s represents state, r represents reward; a is the temperature parameter, determining the relative importance of the reward entropy, so as to ensure the randomness of the optimal strategy; The state space S of the SAC is defined as: Wherein, is the driving style, soc is the battery charge state, v p is the vehicle speed, ap is the vehicle acceleration, tdri is the driving time, theta is the yaw angle, ddes is the distance from the front vehicle; The motion space A is defined as: A = (Tp, [delta] p) Equation 13; wherein Tp is the torque of the vehicle, delta p is the steering wheel corner of the vehicle; The reward function is defined as: R = (w1-mfuelcfuel + w2-Pbattcelec + w3 - (tdri-tref) + w4-Prec + w5-Jmin) formula 14; w1, w2, w3, w4, w5 is the proportion coefficient, mfuel represents the oil consumption of the current intelligent network-connected vehicle, cfuel is the fuel price, Pbatt is the motor power, celec is the price of electricity, tref is the reference driving time, Prec is the brake energy recycling power, Jmin is self-adaptive cruise comprehensive value function; the vehicle driving action is independently executed by each network vehicle, the corresponding reward value is optimized by collecting the control experience of the network vehicle to a centralized playback buffer area; For the specific state st and action at, the soft value function Qsoft (st, at) of the algorithm is expressed as follows: formula 15; wherein, y belongs to [0, 1] is a scale factor; In order to avoid the overestimation when the Q value is maximized and the further overestimation when the target is calculated by using the target network, the SAC algorithm introduces two online networks Q1 and Q2, the parameters are e1 and e2 respectively, and the two target networks v and v target respectively, the parameters are e1 and e2 respectively. and selecting the minimum function value output by the target network as the target value of the target frame; The soft value network parameter is updated by minimizing the loss function, as shown below: The strategy is expressed by Gaussian distribution in the random strategy, that is, the state is mapped into the mean value and variance of the Gaussian distribution by the parameter, and the action is obtained by sampling from the Gaussian distribution; if the state st is used as the input, outputting a Gaussian distribution with mean and standard deviation; then using the re-parameterized technique to obtain the action at, the formula is as follows: in the formula, Epsilon t is the noise signal sampled from the standard normal distribution; is the average value and standard deviation of the Gaussian distribution, wherein u (st) and sigma (st) are the average value and standard deviation of the Gaussian distribution, t is the noise signal sampled from the standard normal distribution; The relationship between the policy function and the soft function is expressed as: updating the policy network parameter by minimizing Kullback-Leibler divergence; the smaller the Kullback-Leibler divergence, the smaller the difference between the rewards corresponding to the output action, the better the convergence effect of the strategy; The update rule of the policy network is expressed as: wherein Z (st) is a distribution function for normalizing the distribution; Finally, the policy network parameter is updated according to the gradient descent method, which is expressed as: the adjustment of the temperature coefficient is important to the training effect of the SAC algorithm; the optimal temperature coefficient is different along with the difference of the strengthening learning task and the training period; the temperature coefficient automatic adjusting mechanism is used; Under this mechanism, the constrained optimization problem is constructed, and the optimal temperature coefficient of each step is obtained by minimizing the objective function, which is expressed as: wherein H0 is a predefined minimum policy entropy threshold.
| 7. The PHEV hybrid vehicle group optimization control method for queue management and adaptive cruise control according to claim 5, wherein step four, through SAC strengthening learning algorithm, training and learning the involved vehicle group queue recombination, self-adaptive cruise and efficient traffic data of crossroad, so as to obtain the optimal control strategy; the SAC considers the state of the network vehicle in the vehicle group and finds the optimal adaptive cruise control strategy, the optimal adaptive cruise control strategy is fed back to the torque and corner of each network vehicle corresponding to the adaptive cruise control, controlling the vehicle running track; in the adaptive cruise control of step three, the needed vehicle distance is influenced by the driving style of the driver, the road commuting efficiency and the vehicle safety, considering the uncertainty of the driving intention of the manual driving vehicle to make the distance between the online vehicle and the manual driving vehicle larger than the vehicle distance between the online vehicle; if the vehicle distance is too narrow, the commuting efficiency will be improved, but the anxiety of the driver may cause the collision accident; On the contrary, the larger vehicle distance is the guarantee of the safety of the vehicle, but the road commuting efficiency will deteriorate, and the side-line vehicle is easy to insert; The constant time vehicle head time interval CTH is used for the vehicle interval algorithm, as shown below: ddes=tau h v h + d0 formula 22; wherein tau h is nominal vehicle head time distance, d0 is safe parking distance; There is the following constraint formula in the following vehicle-following safety: dmin < d < dmax [Delta] d=d-ddes [Delta] vmin < [Delta] vmax [Delta] v=vp_vf wherein d is the actual vehicle distance between the autonomous vehicle and the front vehicle, and dmin and dmax are the minimum and maximum vehicle distances; delta v is the speed difference between the autonomous vehicle and the front vehicle, delta v min and delta v max are the minimum and maximum speed difference; the comfort constraint formula is as follows: FORMULA; delta a = ap-af; af is the acceleration of the front vehicle; The adaptive cruise comprehensive value function is: Jmin=w 6 delta d2 + w 7 delta v 2 + w 8 delta a2 formula 23.
| 8. The PHEV hybrid vehicle group optimization control method for queue management and adaptive cruise control according to claim 5, wherein when the brake force distribution is limited according to the ECE rule, the following brake force distribution strategy is adopted; when the brake strength z is less than z1, the brake force is only provided by the front axle; when the braking strength z1 is less than z2, the braking force of the front and back shafts is distributed along the stated line of the ECE; when the brake strength z2 is less than z3, the brake force of the front axle is not changed, and the brake force of the rear axle is increased; when the brake strength z3 is less than z, the brake of the motor is stopped, and the brake forces of the front and back shafts are distributed along the beta line; in the whole braking process, if the motor braking force is not enough, the hydraulic braking force will compensate the loss of the total braking force; The brake force distribution is represented by the following formula: The boundary of z is calculated as follows: wherein Fbf represents the front shaft braking force, Fbr is the rear shaft braking force, Fb is the total required braking force, L is the total wheelbase, k is the rear wheelbase, hg is the mass centre height, Tbmax is the motor braking moment maximum value, beta is the braking force distribution coefficient, theta is the correction coefficient of the rotating quality, rw is the radius of the wheel, it is the vehicle transmission ratio, n is the transmission efficiency.
| 9. The PHEV hybrid vehicle group optimization control method for queue management and adaptive cruise control according to claim 5, wherein step four, when the vehicle group is close to the intersection with the signal lamp, the optimal strategy adjusts the driving torque of each online vehicle according to the traffic lamp signal timing, corner and braking force, performing queue recombination and queue length planning, so that part of the vehicle forms a queue to pass through the intersection in the green light period, the rest vehicle waits before the stop line, reducing the energy consumption of the whole vehicle group, and realizing better economical efficiency and passing efficiency. | The method involves clustering the driving data of multiple working conditions by multiple characteristic parameters to determine multiple driving styles. The vehicle lane-changing safety area is determined according to the obtained vehicle state information and the surrounding environment information. The vehicle distance, vehicle speed and acceleration is restrained in the self-adaptive cruise control based on the safety and comfort. The braking energy when braking is recycled. The queue is recombined again to make the vehicle group pass through in order when it is close to the crossroad. The optimal strategy capable of making the vehicle group rearrange the queue is obtained according to the driving styles of different drivers on the road not more than three lanes. PHEV hybrid vehicle group optimization control method for queue management and self-adaptive cruise control. Can also be used in hybrid power vehicles and electric vehicles. The method enables rationally planning the vehicle queue according to the driving intention of the driver, so that the queue can efficiently drive the vehicle. The drawing shows a flow diagram of a PHEV hybrid vehicle group optimization control method for queue management and adaptive cruise control. (Drawing includes non-English language text) |
Please summarize the input | DEVICE AND METHOD FOR SELF-AUTOMATED PARKING LOT FOR AUTONOMOUS VEHICLES BASED ON VEHICULAR NETWORKINGThe present disclosure relates to a device and a method for self-automated parking lots for autonomous vehicles based on vehicular networking, advantageous in reducing parking movements and space. It is described a device for self-automated parking lot for autonomous vehicles based on vehicular networking, comprising: a vehicle electronic module for receiving, executing and reporting vehicle movements, a parking lot controller for managing and coordinating a group of vehicles in parking and unparking maneuvers, the vehicle module and controller comprising a vehicular ad hoc networking communication system. It is also described a method comprising moving autonomously in platoon one or more rows of already parked vehicles in order to make available a parking space for a vehicle arriving to the parking space; and moving autonomously in platoon one or more rows of parked vehicles in order to make a parked vehicle able to exit the parking space.|1. A device for self-automated parking lot for autonomous vehicles based on vehicular networking, comprising:
a parking lot controller for managing and coordinating a group of vehicles in parking and unparking maneuvers in said parking lot;
each of said vehicles comprising a vehicle electronic module for receiving, executing and reporting vehicle movements,
wherein said vehicle movements are sent by, and reported to, the parking lot controller,
the parking lot controller comprising a vehicular networking communication system for communicating with the communication system of the vehicle module,
wherein the parking lot controller is configured for:
moving autonomously in platoon one or more rows of already parked vehicles in order to make available a parking space for a vehicle arriving to the parking space; and
moving autonomously in platoon one or more rows of parked vehicles in order to make a parked vehicle able to exit the parking space.
| 2. The device according to claim 1, wherein said vehicular communication system comprises a dedicated short-range communication protocol.
| 3. The device according to claim 1, wherein said vehicular communication system is a mobile communications system.
| 4. The device according to claim 1, wherein said vehicular communicating is a vehicle-to-infrastructure communication system.
| 5. The device according to claim 1, wherein said controller is further configured for:
managing parking infrastructure access based on space availability;
managing vehicle movements upon entering parking infrastructure until the designated parking space is reached;
coordinating vehicle or vehicles movements to allow enter or exit of vehicle or vehicles in the parking area; and
using a communication module for sending data describing said vehicle movements.
| 6. The device according to claim 5, wherein said parking lot controller is configured for also performing as vehicle module, when the parking lot controller functions are assumed by an elected vehicle where this vehicle module is placed.
| 7. The device according to claim 1, wherein said vehicle module is configured for transferring said parking lot controller functions to another vehicle module just before the exit of the parking lot of the controller.
| 8. The device according to claim 1, further comprising a positioning system for positioning the vehicle, a user interface for receiving and displaying user interactions, a connection to the vehicle actuators, computer readable memory and a computer processor.
| 9. The device according to claim 1, wherein said parking lot controller is a local or remote server.
| 10. The device according to the claim 9, further comprising a user interface for receiving and displaying user interactions, computer readable memory and a computer processor.
| 11. A method for operating a self-automated parking lot for autonomous vehicles based on vehicular networking,
said self-automated parking lot comprising a parking lot controller for managing and coordinating the vehicles in parking and unparking maneuvers in said parking lot, and
each vehicle comprising a vehicle electronic module for receiving, executing and reporting vehicle movements, wherein said vehicle movements are received from, and reported to, said parking lot controller by a communications system, said method comprising:
moving autonomously in platoon one or more rows of already parked vehicles in order to make available a parking space for a vehicle arriving to the parking space; and
moving autonomously in platoon one or more rows of parked vehicles in order to make a parked vehicle able to exit the parking space.
| 12. The method according to claim 11, further comprising:
moving autonomously in platoon two rows of vehicles such that vehicles move in carousel between the two rows, transferring vehicles of a first end of the first row of vehicles to a first end of the second row of vehicles, and transferring vehicles of the second end of the second row of vehicles to the second end of the first row of vehicles.
| 13. The method according to claim 11, further comprising:
moving autonomously in platoon one row of vehicles such that an empty parking space is obtained at one end of said row for receiving a vehicle entering the parking lot.
| 14. The method according to claim 11, further comprising:
moving autonomously in platoon two rows of vehicles such that vehicles move in carousel between the two rows, transferring vehicles of a first end of the first row of vehicles to a first end of the second row of vehicles, and transferring vehicles of the second end of the second row of vehicles to the second end of the first row of vehicles,
such that a vehicle exiting the parking lot is moved to one of the ends of one of the vehicle rows.
| 15. The method according to claim 11, further comprising:
on approaching the parking lot, the vehicle module communicating with the parking lot controller to signal the vehicle arrival and receiving a designated parking area;
subsequently, the parking lot controller generating, from a data map of the parking lot vehicles, a number of movements from one or more rows of vehicles to one or more rows of vehicles of the parking lot, then calculating the least costly movement and executing said movement by communicating said movement to the vehicle modules.
| 16. The method according to claim 11, further comprising:
the parking lot controller receiving vehicle position and sensor status data from the vehicle modules, creating a data map of the parking lot vehicles, periodically broadcasting vehicle modules with updates of said data.
| 17. The method according to claim 11, wherein the vehicle rows are linear, circular, elliptical, spiral, or combinations thereof.
| 18. The method according to claim 11, wherein the vehicle rows are grouped in cascading or interlinking parking zones such that only a part of the vehicle rows of one zone are able to exchange vehicles with the vehicle rows of another zone.
| 19. The method according to claim 11, wherein the parking lot controller is carried out by one of the vehicle electronic modules, in particular by electing a vehicle module by the vehicle modules by a set of predefined criteria, further in particular by resolving a conflict of tied vehicle modules by a set of predefined criteria.
| 20. A non-transitory storage media including program instructions for implementing a method for operating a self-automated parking lot for autonomous vehicles based on vehicular ad hoc networking, the program instructions including instructions executable to carry out the method of claim 11. | The device has a parking lot controller to manage/coordinate a group of vehicle (xx0,xx9) in parking/un-parking maneuvers in a parking lot. A vehicle electronic module is provided in the vehicle to receive, execute and report vehicle movements. A vehicular networking communication system (xx1) is provided for communicating with a communication system of the module. The controller is configured for moving autonomously in platoon/rows of already parked vehicles in order to make available a parking space for a vehicle arriving to space and to make a parked vehicle able to exit the space. INDEPENDENT CLAIMS are included for the following:a method for operating a self-automated parking lot for autonomous vehicles based on vehicular networking; anda non-transitory media storing program for operating a self-automated parking lot for autonomous vehicles based on vehicular networking. Device for self-automated parking lot for autonomous vehicles by vehicular networking. The car that is parked in the parking space requires a minimal travel distance of the cars in the parking lot where no optimization based on the estimated exit time is used. The total travelled distance is significantly made less even with such non-optimized strategy. The self-automated parking lots for autonomous vehicles based on vehicular networking are thus beneficial in reducing parking movements and space. The drawing shows a schematic view of a collaborative parking system. x10Computing systemxx0,xx9Vehiclexx1Vehicular networking communication systemxx2Positioning systemxx7Vehicle actuator |
Please summarize the input | DRIVING SYSTEM IN AREA OUTSIDE ENHANCED AUTONOMOUS VALET PARKING LOT, AND APPLICATION METHOD THEREOFA driving system in an area outside an enhanced autonomous valet parking (E-AVP) lot, and an application method thereof, relating to the technical fields of autonomous driving, vehicle-infrastructure cooperation, and intelligent transportation systems. The system comprises a client, an E-AVP cloud, a road side unit, and a 5G-V2X vehicle end; the client communicates with the E-AVP cloud by means of 5G; the E-AVP cloud communicates with the client, the road side unit, and the 5G-V2X vehicle end by means of 5G; the road side unit communicates with the 5G-V2X vehicle end by means of V2X communication; the 5G-V2X vehicle end achieves inter-vehicle interaction by means of a CAN bus. The present application achieves interaction among an E-AVP management system, a user terminal, and a vehicle-mounted terminal, and solves the existing problems that parking and vehicle searching are difficult for users, thereby achieving the objectives of unmanned supervision of remote valet parking and improvement of the parking experience of the users.|1. An enhanced driving system of area outside autonomous parking parking lot, wherein it comprises a client, an E-AVP cloud end, a road test unit and a 5G-V2X vehicle end, wherein: the client end is used for performing identity authentication, sending a request instruction, communicating with the E-AVP cloud end through the 5G and feeding back the vehicle track; the E-AVP cloud is used for performing identity authentication, performing global track planning through the communication between the 5G and the client, the drive test unit and the 5G-V2X vehicle end, and issuing the track slice to the drive test unit according to the corresponding range of each drive test unit; interacting with the AVP system; the road test unit is used for performing identity authentication, communicating with the 5G-V2X vehicle end through V2X communication, and guiding the vehicle according to the track slice sent by the E-AVP cloud end; 5G-V2X vehicle end for identity authentication, through CAN bus between vehicle interaction, obstacle identification and obstacle track generation.
| 2. The driving system of enhanced type autonomous parking parking parking area according to claim 1, wherein, when the client sends the request instruction to the E-AVP cloud end, it needs to carry out the on-chain identity authentication, if the identity authentication is not passed or the request instruction sent by the client is invalid, the E-AVP cloud does not respond and returns the information of refusing response; if the identity authentication is successful, the E-AVP cloud sends a request response to the client, and sends a request instruction to the 5G-V2X vehicle end and the drive test unit; the request instruction sent by the client to the E-AVP cloud comprises parking lot request, parking space request, parking request or car taking request; the request response sent by the E-AVP cloud end received by the client comprises whether the E-AVP cloud end is valid, whether the vehicle is finished to park, whether the off-field parking is finished and the current position information of the vehicle.
| 3. The driving system of enhanced autonomous parking parking area according to claim 1, wherein, 5G-V2X vehicle end collecting position information from the vehicle. the driving information and the track information of other vehicles, the position information of the vehicle and the driving information are sent to the E-AVP cloud end, the E-AVP cloud end generates the global track according to the received information.
| 4. The driving system of enhanced autonomous parking parking parking area according to claim 1 or 3, wherein the E-AVP cloud end according to the request instruction to generate global track, and the global track according to the road test unit covering the global track to slice, and distributing the cut track to each corresponding drive test unit.
| 5. The driving system of enhanced autonomous parking parking area according to claim 1, wherein the E-AVP cloud end after the global track planning according to the range of each road test unit to the global track planning, according to the range slice corresponding to each road test unit, sending each slice to the road test unit; the slice sent by the E-AVP cloud end to the road test unit comprises a starting point and an end point position of the vehicle planning estimation in the range of the road test unit, and a road section ID through which the starting point position to the end point position orderly passes, each road section ID comprises a lane ID when the vehicle runs on the road section, Each lane ID comprises a road centre line of the lane, and the vehicle travels according to the position of the road centre line.
| 6. The driving system of enhanced autonomous parking parking area according to claim 1, wherein, E-AVP cloud end to generate the global track to 5G-V2X vehicle end, 5G-V2X vehicle end through V2X with other vehicle share from the global track; the 5G-V2X vehicle end communicates with the vehicle network through the CAN bus, comprising sending the global track to the vehicle network, realizing the real-time control of the vehicle through the vehicle network.
| 7. An application method of the driving system in the outer area of the enhanced autonomous parking parking lot, wherein it comprises the driving system in the outer area of the enhanced autonomous parking lot, the autonomous parking AVP system and the client terminal according to claim 1, the client terminal comprises identity authentication, request instruction, customer service end control, customer service end and E-AVP cloud end data interaction and vehicle track feedback; A driving method of a driving system in an area outside an enhanced autonomous passenger-and-parking parking lot comprises the following steps: before the client sends the parking request, the E-AVP cloud performs identity authentication on the client through the blockchain technology; after the client passes the identity authentication, the client sends a request instruction to the E-AVP cloud; when the E-AVP cloud end receives the request instruction, the E-AVP cloud end obtains the vehicle information from the client end, and obtains the parking space information and parking lot information from the vehicle network cloud end; the E-AVP cloud performs global path planning according to the obtained information, and slices the planned path as a local path planning, and sends the local path planning to the corresponding drive test unit through 5G; the E-AVP cloud uses the blockchain technology to perform the identity authentication on the drive test unit, after passing the authentication, the E-AVP cloud sends the slice information to the drive test unit; the road test unit guides the vehicle to advance according to the track planned by the local track according to the received local path plan; the road detection unit performs identity authentication to the 5G-V2X controller by using the blockchain technology, after passing the identity authentication, sends the path plan to the 5G-V2X controller, the 5G-V2X controller guides the vehicle to run to the entrance of the parking lot according to the received information.
| 8. The application method of the driving system of the enhanced autonomous parking parking area according to claim 7, wherein, if the E-AVP cloud end through block chain technology to the client end for identity authentication, the client end is not through identity authentication, or if the identity authentication in the request sent by the client after the identity authentication is invalid, the E-AVP cloud does not accept the request instruction of the client. | The system has a road detecting unit for performing identity authentication, where a track slice is sent to the road test unit according to the corresponding range of the road test unit. A vehicle end is connected with a vehicle end Fifth-generation (5G)-V2X. The vehicle end guides a vehicle according to a track slice sent by an E-AVP cloud. A client is utilized for performing identity authentication and sending request instruction. The vehicle end performs the identity authentication to realize vehicle interaction through a Controller Area Network (CAN) bus, obstacle recognition and obstacle avoidance track generation. An INDEPENDENT CLAIM is included for an enhanced autonomous passenger parking car park external area driving system application method. Enhanced autonomous passenger parking car park external area driving system. The system realizes interaction of the vehicle terminal by the E-AVP management system and the user terminal, so as to solve the problem that the existing user parking is difficult to search to realize the unmanned monitoring of remote parking, thus improving the user parking experience. The drawing shows a schemctic block diagram of an enhanced autonomous passenger parking car park external area driving system (Drawing includes non-English language text). |
Please summarize the input | Methods and software for managing vehicle priority in a self-organizing traffic control systemMethods and software for managing vehicle priority proximate to a potential travel-priority conflict zone, such as a roadway intersection, where travel conflicts, such as crossing traffic, can arise. Coordination involves forming an ad-hoc network in a region containing the conflict zone using, for example, vehicle-to-vehicle communications and developing a dynamic traffic control plan based on information about vehicles approaching the conflict zone. Instructions based on the dynamic traffic control plan are communicated to devices aboard vehicles in the ad-hoc network, which display one or more virtual traffic signals to the operators of the vehicles and/or control the vehicles (for example, in autonomous vehicles) in accordance with the dynamic traffic control plan, which may account for a priority level associated with one or more of the vehicles.What is claimed is:
| 1. A method of managing vehicle priority proximate to a potential travel-priority conflict zone, the method being executed in a dynamic traffic control system and comprising:
communicating with a first component of the dynamic traffic control system located on-board a vehicle proximate to the potential travel-priority conflict zone so as to establish a dynamic traffic control plan for avoiding a travel-priority conflict in the potential travel-priority conflict zone;
coordinating with the first component of the dynamic traffic control system via said communicating to elect a dynamic traffic controller as a temporary coordinator vehicle responsible for temporarily coordinating the dynamic traffic control plan;
receiving a priority-request message from a priority vehicle;
determining a travel direction of the priority vehicle;
comparing the travel direction of the priority vehicle to a travel direction of a non-priority vehicle proximate to the potential travel-priority conflict zone;
transmitting a priority-granted message to the priority vehicle when the travel direction of the priority vehicle and the travel direction of the non-priority vehicle proximate to the potential travel-priority conflict zone differ; and
providing traffic control instructions to an operator of the priority vehicle via a visual or audio indication produced in the priority vehicle as a function of the priority-granted message.
| 2. A method of managing vehicle priority proximate to a potential travel-priority conflict zone, the method being executed in a dynamic traffic control system and comprising:
communicating with a first component of the dynamic traffic control system located on-board a vehicle proximate to the potential travel-priority conflict zone so as to establish a dynamic traffic control plan for avoiding a travel-priority conflict in the potential travel-priority conflict zone;
coordinating with the first component of the dynamic traffic control system via said communicating to elect a first dynamic traffic controller as a first temporary coordinator vehicle responsible for temporarily coordinating the dynamic traffic control plan;
receiving a priority-request message from a priority vehicle;
determining a travel direction of the priority vehicle;
comparing the travel direction of the priority vehicle to a travel direction of a non-priority vehicle proximate to the potential travel-priority conflict zone; and
when the travel direction of the priority vehicle and the travel direction of the non-priority vehicle proximate to the potential travel-priority conflict zone are the same, coordinating with the first component of the dynamic traffic control system via said communicating to hand over responsibility for coordinating the dynamic traffic control plan to a new temporary coordinator vehicle by electing a second dynamic traffic controller as a second temporary coordinator vehicle responsible for temporarily coordinating the dynamic traffic control plan.
| 3. A method according to claim 1 or 2, wherein said receiving a priority-request message includes receiving a priority-request message from an emergency vehicle.
| 4. A method according to claim 1 or 2, further comprising receiving a priority-clear message from the priority vehicle.
| 5. A method according to claim 1 or 2, wherein at least a portion of said communicating is performed via vehicle-to-vehicle communication.
| 6. A method according to claim 1 or 2, further comprising revoking priority for the priority vehicle if no transmissions are received from the priority vehicle for a predetermined period of time. | The managing method involves coordinating with the primary component of the dynamic traffic control system through communication to elect dynamic traffic controller as a temporary coordinator vehicle responsible for temporarily coordinating the dynamic traffic control plan. A priority request message is received from the priority vehicle before the priority granted message is transmitted to the priority vehicle. An INDEPENDENT CLAIM is also included for a machine readable storage medium. Managing method for vehicle priority proximate to potential travel priority conflict zone. Uses include but are not limited to bus, trail, trolley, streetcar. The dynamic traffic control system may weight the travel directions and lanes containing mass transit vehicles in a manner that allows each of those travel directions and lanes to clear more quickly than they would if a non-priority vehicle were present in place of each mass transit vehicle. The drawing shows a flow diagram of the managing method for vehicle priority in self-organizing traffic control system from the perspective of a priority vehicle. |
Please summarize the input | Methods and software for managing vehicle priority in a self-organizing traffic control systemMethods and software for managing vehicle priority proximate to a potential travel-priority conflict zone, such as a roadway intersection, where travel conflicts, such as crossing traffic, can arise. Coordination involves forming an ad-hoc network in a region containing the conflict zone using, for example, vehicle-to-vehicle communications and developing a dynamic traffic control plan based on information about vehicles approaching the conflict zone. Instructions based on the dynamic traffic control plan are communicated to devices aboard vehicles in the ad-hoc network, which display one or more virtual traffic signals to the operators of the vehicles and/or control the vehicles (for example, in autonomous vehicles) in accordance with the dynamic traffic control plan, which may account for a priority level associated with one or more of the vehicles.What is claimed is:
| 1. A non-transitory machine-readable storage medium containing machine-executable instructions for performing a method of managing vehicle priority proximate to a potential travel-priority conflict zone, the method being executed in a dynamic traffic control system, said machine-executable instructions comprising:
a first set of machine-executable instructions for communicating with a first component of the dynamic traffic control system located on-board a vehicle proximate to the potential travel-priority conflict zone so as to establish a dynamic traffic control plan for avoiding a travel-priority conflict in the potential travel-priority conflict zone;
a second set of machine-executable instructions for coordinating with the first component of the dynamic traffic control system via said communicating to elect a dynamic traffic controller as a temporary coordinator vehicle responsible for temporarily coordinating the dynamic traffic control plan;
a third set of machine-executable instructions for receiving a priority-request message from a priority vehicle;
a fourth set of machine-executable instructions for determining a travel direction of the priority vehicle;
a fifth set of machine-executable instructions for comparing the travel direction of the priority vehicle to a travel direction of a non-priority vehicle proximate to the potential travel-priority conflict zone;
a sixth set of machine-executable instructions for transmitting a priority-granted message to the priority vehicle when the travel direction of the priority vehicle and the travel direction of the non-priority vehicle proximate to the potential travel-priority conflict zone differ; and
a seventh set of machine-executable instructions for providing traffic control instructions to an operator of the priority vehicle via a visual or audio indication produced in the priority vehicle as a function of the priority-granted message.
| 2. A non-transitory machine-readable storage medium according to claim 1, wherein said third set of machine-executable instructions includes machine-executable instructions for receiving a priority-request message from an emergency vehicle.
| 3. A non-transitory machine-readable storage medium according to claim 1, further comprising machine-executable instructions for receiving a priority-clear message from the priority vehicle.
| 4. A non-transitory machine-readable storage medium according to claim 1, further comprising machine-executable instructions for implementing vehicle-to-vehicle communication.
| 5. A non-transitory machine-readable storage medium according to claim 1, further comprising machine-executable instructions for revoking priority for the priority vehicle if no transmissions are received from the priority vehicle for a predetermined period of time.
| 6. A non-transitory machine-readable storage medium containing machine-executable instructions for performing a method of managing vehicle priority proximate to a potential travel-priority conflict zone, the method being executed in a dynamic traffic control system, said machine-executable instructions comprising:
a first set of machine-executable instructions for communicating with a first component of the dynamic traffic control system located on-board a vehicle proximate to the potential travel-priority conflict zone so as to establish a dynamic traffic control plan for avoiding a travel-priority conflict in the potential travel-priority conflict zone;
a second set of machine-executable instructions for coordinating with the first component of the dynamic traffic control system via said communicating to elect a dynamic traffic controller as a temporary coordinator vehicle responsible for temporarily coordinating the dynamic traffic control plan;
a third set of machine-executable instructions for receiving a priority-request message from a priority vehicle;
a fourth set of machine-executable instructions for determining a travel direction of the priority vehicle;
a fifth set of machine-executable instructions for comparing the travel direction of the priority vehicle to a travel direction of a non-priority vehicle proximate to the potential travel-priority conflict zone; and
a sixth set of machine-executable instructions for, when the travel direction of the priority vehicle and the travel direction of the non-priority vehicle proximate to the potential travel- priority conflict zone are the same, coordinating with the first component of the dynamic traffic control system via said communicating to hand over responsibility for coordinating the dynamic traffic control plan to a new temporary coordinator vehicle by electing a second dynamic traffic controller as a second temporary coordinator vehicle responsible for temporarily coordinating the dynamic traffic control plan.
| 7. A non-transitory machine-readable storage medium according to claim 6, wherein said third set of machine-executable instructions includes machine-executable instructions for receiving a priority-request message from an emergency vehicle.
| 8. A non-transitory machine-readable storage medium according to claim 6, further comprising machine-executable instructions for receiving a priority-clear message from the priority vehicle.
| 9. A non-transitory machine-readable storage medium according to claim 6, further comprising machine-executable instructions for implementing vehicle-to-vehicle communication.
| 10. A non-transitory machine-readable storage medium according to claim 6, further comprising machine-executable instructions for revoking priority for the priority vehicle if no transmissions are received from the priority vehicle for a predetermined period of time. | The storage medium comprises a component that provides with a set of machine-executable instructions for communicating in a dynamic traffic control system. The control system is located with on-board a vehicle proximate to the potential travel-priority conflict zone. A dynamic traffic control plan is established for avoiding a travel-priority conflict in the potential travel priority conflict zone. Another set of machine-executable instructions for coordinating with the component of the dynamic traffic control system through a dynamic traffic controller. Storage medium for storing instructions for a method for managing vehicle priority proximate to a potential travel-priority conflict zone in a dynamic traffic control system. The dynamic traffic control plan is established for avoiding a travel-priority conflict in the potential travel priority conflict zone, and thus enables to easily manage vehicle priority proximate to a potential travel-priority conflict zone in a dynamic traffic control system. The drawing shows a block diagram of a computing system. 824Storage device832Input device836Display848Remote device852Display adapter |
Please summarize the input | Self-driving rule learning method based on deep strengthening learningThe invention claims an autonomous driving rule learning method based on deep strengthening learning, under the vehicle networking environment, there are two types of vehicles in the road network, autonomous driving vehicle and network vehicle. the autonomous driving vehicle obtains the driving state of the online vehicle in the road network in real time through the vehicle-to-vehicle (V2V) communication technology of the vehicle-to-vehicle (vehicle-to-vehicle, V2V) communication system of the vehicle-to-vehicle (vehicle-to-vehicle, V2V) communication system of the vehicle-to-vehicle (vehicle-to-vehicle, V2V) communication system of the vehicle-to-vehicle (vehicle-to-vehicle, V2V) communication system of the vehicle-to-vehicle (vehicle-to-vehicle, V2V) communication system learning the autonomous driving rule, adjusting the vehicle queue driving distance, so as to maximize the average speed of the road network and improve the passing efficiency of the road network. The invention lays the foundation for further improving the autonomous decision-making ability of the vehicle by deep reinforcement learning.|1. An autonomous driving rule learning method based on deep strengthening learning, wherein The specific implementation steps of the method are as follows: step 1: obtaining self-driving vehicle information; In the driving process, the information to be acquired by the autonomous driving vehicle comprises: the position x and the speed v of the online vehicle in the road network; the driving state of the current autonomous driving vehicle comprises the speed, acceleration and position of the autonomous driving vehicle; the self-driving vehicle adopts the driving action according to the driving state of the network vehicle; the driving state of the network vehicle is used as the input of the driving strategy model; step 2: self-driving vehicle driving rule; The defined driving behaviour of the autonomous driving vehicle is the acceleration α of the vehicle, the speed of the autonomous driving vehicle at t, t + 1 time is velocityt, velocityt + 1, the equation of the autonomous driving vehicle updating motion state is: step 3: a reward and punishment mechanism of the driving rule of the autonomous driving vehicle; setting the acceleration threshold of the autonomous driving vehicle as accel_threshold, calculating the average value alpha avg of the stored autonomous driving vehicle driving behaviour alpha, comparing the alpha avg with the accel_threshold, If aavg > accel_threshold, then there is, raccel = r + 8 * (accel_threshold-aavg), aavg > accel_threshold, wherein, r represents a reward value obtained before the occurrence of a vehicle collision behaviour, and [delta] is a hyperparameter; there is a negative reward value rcollide=-500 when there is a vehicle collision; the ui (t) and hi (t) are respectively the speed and time distance of the vehicle i at the time step length t; The form of the reward equation is shown as follows: wherein v des expected speed; hmax is time interval threshold value, alpha is gain; step 4: a self-driving vehicle driving strategy model; the autonomous driving vehicle driving strategy model selects multi-layer perceptron MLP; the driving strategy model of autonomous driving vehicle is composed of four layers of network, comprising an input layer, a hidden layer and an output layer; the number of the hidden layer is 3, the number of the output layer is 1; step 5: learning the driving rule of the self-driving vehicle; the learning of the driving rule of the autonomous driving vehicle can obtain the position and speed of the online vehicle in the road network in each time step length, and the probability value of the driving behaviour is output by the driving strategy model of the autonomous driving vehicle; storing the position and speed of the online vehicle in the road network of each turn, the driving action and reward value adopted by the autonomous driving vehicle and the speed and position of the online vehicle in the next time step; after collecting the network vehicle driving state data, sampling MiniBatch from the data for training; wherein the self-driving vehicle driving strategy model adjustment is realized by PG algorithm; In the PG algorithm, J (theta) is used to represent the target function, representing the expected return of the whole round; The expected return of the track is expanded to obtain J (0) = # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # [pi] [theta] ([tau]) represents a probability of a selection behaviour, and r ([tau]) represents a reward value obtained in the round; The objective of the PG algorithm is to maximize the expected return value, and the maximized process is realized by gradient calculation to obtain the final form of solving the gradient. taking the probability distribution paold of the driving action aold of the autonomous driving vehicle as the expected output probability distribution; combining the driving state s of the online vehicle into a matrix and inputting to the neural network, outputting the probability distribution panew of the driving action after Softmax as the actual output probability distribution; Calculating the proximity of two probability distributions judging whether the calculated gradient is trustworthy according to the size of the reward value; the cross entropy loss function is reward value A discount processing is performed prior to reverse propagation, indicating that the current reward value is more important than the future reward value, Rdiscount=rl + yr2 + y2r3 + ... wherein y represents a discount factor, and the final form of the cross entropy loss function is as follows: Next, parameter update is performed. wherein, the learning-rate represents the learning rate, theta represents the driving strategy model of the autonomous driving vehicle before updating, The invention claims a driving strategy model of updated autonomous driving vehicle.
| 2. The autonomous driving rule learning method based on deep strengthening learning according to claim 1, wherein The network structure of step 4 is as follows: an input layer: The input layer has two neurons, firstly according to the input element xi of the input layer, and a bias/> solving the input element f of the hidden layer; in the formula: the p layer is the element number of the input layer; q is the number of hidden layer elements; i represents an input layer neuron; the neural network input is the position and speed [vN, xN] of the online vehicle in the road network sensed by the autonomous driving vehicle, N represents the number of the online vehicle in the road network; hidden layer: Input element of hidden layer in the activation function, calculating the output element zj of the hidden layer, the activation function selects tanh function; The output element zj function expression of the hidden layer is output layer: The output element zj, weight of the hidden layer and a bias/> In its activation function, the input element f of the output layer is solved. in the formula: j is the element number of the output layer, n is the hidden layer number; the output layer is the driving action adopted by the self-driving vehicle; The input element of the output layer the output element yk of the output layer is solved in the activation function thereof, and the activation function uses Softmax function. | The method involves providing autonomous driving vehicle in vehicle queue vehicle-to-vehicle communication during the driving process. The position and speed of the connected vehicle in the road network are obtained. The autonomous driving vehicle is needed to adopt driving behaviors according to the driving status of the connected vehicle. The defined driving behavior of the autonomous driving vehicle is the acceleration of the vehicle. The speed of the autonomous driving vehicle updates the movement state. The basic goal of autonomous vehicle driving is to dissipate stop-and-stop waves in the road network. The acceleration threshold of autonomous driving vehicles is set to accel threshold. The driving strategy model of autonomous driving vehicle selects multi-layer perceptron (MLP). The driving rules of autonomous driving vehicle are learned. The probability value of driving behavior is outputted through the driving strategy model of autonomous driving vehicle. Autonomous driving rule learning based on deep reinforcement learning. The utilization of deep reinforcement learning improves the autonomous decision-making ability of vehicles. The driver of the non-standard operation and error operation influence of the running safety of the automobile is reduced, which improves the driving safety of a vehicle. The drawing shows a flowchart illustrates the implementation method for autonomous driving rule learning based on deep reinforcement learning. (Drawing includes non-English language text) |
Please summarize the input | Multi-agent cruise control method, device, electronic device and storage mediumThe invention claims a multi-intelligent cruise control method, device, electronic device and storage medium, wherein the intelligent cruise control of each automatic driving vehicle is realized by inputting the vehicle signal of the current time of the sub-vehicle team collected by each automatic driving vehicle to the intelligent optimization control model; wherein the intelligent optimization control model is obtained by performing centralized neural network parameter training on the partially observable Markov game model based on the vehicle queue real-time collection state sample built by multiple automatic driving vehicles. The invention continuously and continuously interacts with the environment, continuously and intelligently learns and adjusts the optimized control strategy of the networked cruise control, so as to adapt to the actual complex and variable network dynamic scene, The invention solves the problem that the current cruise control method based on network control has unpredictability of complex traffic environment and unreliability of network.|1. A multi-agent cruise control method, wherein the method comprises: determining the current time vehicle signal of the sub-vehicle team collected by each automatic driving vehicle; respectively inputting the present state signal of each automatic driving vehicle to the corresponding intelligent optimizing control model to realize the intelligent cruise control of multiple automatic driving vehicles; wherein the intelligent optimization control model is obtained by performing centralized neural network parameter training to the partially observable Markov game model based on the vehicle queue real-time collection state sample built by the automatic driving vehicle; The construction process of the partially observable Markov game model comprises the following steps: dividing the vehicle queue composed of all vehicles into several sub-queues according to the position of the automatic driving vehicle, taking a certain automatic driving vehicle as reference, the nearest automatic driving vehicle in front of the vehicle is the head vehicle, the nearest automatic driving vehicle in back of the vehicle is the tail vehicle, all the vehicles between the head and the tail form the sub-queue system of the automatic driving vehicle; obtaining the sub-queue state information built by each automatic driving vehicle, and establishing the dynamic equation of the queue system according to the sub-queue state information; according to the dynamic equation of the sub-queue system, taking the minimum state error and input as the target function to construct the quadratic optimization control equation; constructing a partially observable Markov game model of network control according to the dynamic equation of the sub-queue system and the quadratic optimization control equation; the step of obtaining the sub-queue state information established by each automatic driving vehicle, and establishing a dynamic equation of the sub-queue system according to the sub-queue state information, comprises the following steps: obtaining vehicle distance, vehicle speed and acceleration information of each vehicle in the sub-queue through vehicle-to-vehicle communication; according to the vehicle distance, vehicle speed and acceleration information of each vehicle in the vehicle sub-queue, establishing the dynamic equation of each vehicle in the sub-queue; setting the real-time vehicle speed of the head vehicle as the expected vehicle speed, obtaining the expected vehicle distance of each vehicle corresponding to the expected vehicle speed based on the preset range strategy, and establishing the state error equation of each vehicle according to the expected vehicle speed of the head vehicle, the expected vehicle distance of each vehicle and the current vehicle speed and vehicle distance of each vehicle; combining the state error equation of each vehicle in the sub-queue, and based on the state equation of each vehicle in the sub-queue of continuous time, after discretization processing, obtaining the dynamic equation of the sub-queue system; The preset range policy includes: The definition is as follows: wherein vd (l) represents a desired speed based on vehicle distance l, lmin represents a preset minimum vehicle distance, lmax represents a preset maximum vehicle distance, vmax represents a preset maximum vehicle speed; the discretization process to obtain the dynamic equation of the sub-queue system is as follows: Wherein, k is the sampling interval sequence number, and is a state variable and an acceleration control strategy respectively representing the ith sub-queue at the kth moment, Bi = [01 x 2n, 0, 1, 01 x 2m] T, p i (t) = [fi (- n), ..., fi (-1), 0, 0, fi1, ..., fim] T, fij = [0, δ ij (t)], j belongs to [- n, -1] U [1, m], wherein, T is the sampling interval, τ k is the network induction time delay at the kth time, the current automatic driving vehicle label is 0, from the vehicle to the head vehicle direction in turn mark 1, 2, ..., m, m + 1, from the vehicle to the tail vehicle direction in turn mark -1, -2, ...,-(n-1), - n, ij represents the jth vehicle in the ith sub-queue, xi ij and n ij represent artificial driver parameter, the partial derivative at the desired vehicle distance, [delta] ij (t) is an additional acceleration interference term due to the time-varying characteristic of the desired vehicle distance; according to the dynamic equation of the sub-queue system, taking the minimum state error and input as the target function to construct the quadratic optimization control equation as follows: Wherein, N is the sampling interval number, and D0i and Vi are coefficient matrixes; the intelligent optimization control model performs neural network parameter training on the partially observable Markov game model based on the vehicle sub-queue real-time collection state sample established by each automatic driving vehicle, comprising: each intelligent agent constructs a depth deterministic strategy gradient algorithm comprising a current actor network, a current critic network, a target actor network and a target critic network to update the partially observable Markov game model parameter; The observation of all the intelligent bodies itself constitutes the environment state S = (o1, ..., oC), wherein C is the number of the intelligent bodies, namely the total number of the automatic driving vehicles, in each time slot k, the observation of each intelligent body i according to the input the current actor network will output the corresponding action strategy f; executing strategy/> The actions of all the intelligent bodies constitute a global action A = (a1, ..., aC), according to the environment state, all the intelligent agent actions, and according to the state transfer matrix to obtain the environment state S ' at the next time, each intelligent agent reward function to obtain the corresponding reward rik, wherein the reward value is composed of a local reward rip and a global reward rg, all the intelligent agent reward value R = (r1, ..., rC), storing (Sk, Ak, Rk, S ' k) as a sample in an empirical playback buffer to obtain a state sample; wherein, is the parameter of the actor network, in order to effectively explore and add random noise in the continuous action space; Each current critic network obtains global information and updates its parameters by centralized training minimization of the following mean square error loss function Wherein, U is the sample number of the small batch sampling, t is the small batch sampling sequence number. is the current Q value, through the global/> and The information is input to the current critic network of each agent, and the information is input to the current critic network of each agent. is the target Q value, expressed as: In the formula, rit is the corresponding reward function value of the agent i, For the next Q value generated by the target critic network of the agent i, a 'j = u' j (0, j) is the target actor network according to the input self-observation/. the generated next action strategy, gamma is discount factor; the current actor network of the intelligent agent i updates its parameter through the following strategy gradient function Wherein, is a gradient operator; Each target actor network and target critic network respectively update their parameters by the following way: and Wherein, Epsilon is a fixed constant, and Epsilon is more than 0 and less than 1.
| 2. The multi-agent cruise control device realized based on the multi-agent cruise control method according to claim 1, wherein it comprises an observing signal unit and an intelligent control unit; the signal collecting unit is used for obtaining the current vehicle speed and vehicle distance information of the vehicle in the sub-queue; the intelligent control unit is used for inputting the current vehicle signal of the sub-queue vehicle to the intelligent optimization control model to realize the intelligent cruise control of the automatic driving vehicle; the intelligent optimization control model is obtained by training the Markov game model based on the mixed vehicle queue real-time collection state sample pair part observable by the multiple automatic driving vehicles and the manual driving vehicle.
| 3. An electronic device realized based on the multi-agent cruise control method according to claim 1, comprising a memory, a processor and a computer program stored on the memory and operated on the processor, wherein the processor realizes the multi-agent cruise control method when executing the program.
| 4. A non-transient state computer readable storage medium realized based on the multi-agent cruise control method according to claim 1, wherein the non-transient state computer readable storage medium is stored with a computer program; the computer program is executed by the processor to realize the multi-agent cruise control method. | The method involves obtaining vehicle distance, vehicle speed and acceleration information of a vehicle in a sub-queue through vehicle-to-vehicle communication. Dynamic equation of the vehicle is established according to the vehicle distance. A state error equation of each vehicle is established based on expected vehicle speed of a head vehicle. The expected vehicle distance of the vehicle is obtained corresponding to the expected vehicle speed based on a preset range strategy. An intelligent optimization control model is obtained by performing a centralized neural network parameter training to a partially observable Markov game model based on real-time collection state sample of a vehicle queue in a real time. An INDEPENDENT CLAIM is included for a device for performing multi-agent cruise control for automatic driving vehicles. Method for performing multi-agent cruise control for automatic driving vehicles. The method enables continuously and intelligently learning and adjusting an optimized control strategy of networked cruise control, so as to adapt actual complex and variable network dynamic scene, thus reducing unpredictability of complex traffic environment and unreliability of network. The drawing shows a flow diagram illustrating a method for performing multi-agent cruise control for automatic driving vehicles. (Drawing includes non-English language text). |
Please summarize the input | A linked automatic driving automobile auxiliary sensing street lamp system based on intelligent network of the V2IThe invention claims an intelligent network-based V2I-linked automatic driving automobile auxiliary sensing street lamp system, comprising a street lamp and data centre is set in the road, street lamp is equipped with a camera image sensor. the millimetre wave radar sensor and a laser radar sensor, each sensor is connected with the data centre so that the data transmission to the data center of each collection, data centre after receiving the data based on the computer vision technique and data fusion technology to perform data extraction and fusion process so as to form the road of real-time data. data centre with the 5G network service provider combine so as to corresponding to the real-time data of the road through the 5G network distribution intelligent network connected to each vehicle-mounted terminal to realize the V2I signal according to the requirement of automatic driving automobile. the system through road can detect all kinds of information covered by the traffic network, obtaining instant data information of its path is convenient to the vehicle terminal, unlimitedly extend the sensing range, avoid the potential safety hazard and the traffic jam.|1. An intelligent network-based V2I-linked automatic driving automobile auxiliary sensing street lamp system, wherein it comprises a street lamp and data centre is set in the road, the street lamp is provided with a camera image sensor, millimeter wave radar sensor and a laser radar sensor; the camera image sensor collects the image data in the coverage area, the object speed data collected by the millimetre wave radar sensor under the street lamp, the laser radar sensor collecting the road point cloud data, each sensor is connected with the data centre so that the data transmission to the data center of each acquisition. real-time data after the data center receives the data based on the computer vision technique and data fusion technology to perform data extraction and fusion process so as to form the road, the data centre and the 5G network service provider combined so as to fix the corresponding real-time data of the road network by 5G the network distributed to each vehicle-mounted terminal to the intelligent network for realizing V2I according to the requirement of automatic driving automobile.
| 2. The intelligent network according to claim 1 automatic driving automobile auxiliary sensing street lamp system, wherein the data centre comprises a lower data centre and the high-grade data centre. the lower data centre connected with each sensor by receiving the collected data corresponding to each sensor based on the computer vision technique and data fusion technology to perform data extraction and fusion processing to form the road data area is then transmitted to the high-grade data centre, the high-grade data centre fusion so as to form the road of real-time data to data of larger range than lower data centre, the high-grade data centre and 5G network service provider combined so as to realize the intelligent network of the V2I communication signal.
| 3. The intelligent network according to claim 2 automatic driving automobile auxiliary sensing street lamp system, wherein the road data in the region comprises the road video data, point cloud data, and a category, size, outline, moving speed and direction of travel of each of the traffic participants.
| 4. said intelligent network connected automatic driving automobile auxiliary sensing street lamp system according to any one of claims 1 to 3, wherein said street lamp having a unique device ID, data of each sensor transmits the collected after frame addition time, position and the device ID are transmitted to the data center.
| 5. said intelligent network connected automatic driving automobile auxiliary sensing street lamp system according to any one of claims 1 to 3, wherein the camera image sensor is a high speed camera image sensor view, the laser radar sensor is a solid-state laser radar sensor.
| 6. The intelligent network according to claim 2 3 automatic driving automobile auxiliary sensing street lamp system, wherein the road network of several street lamp adopts multiple-to-one connected lower data centre, the lower data center, each lower data centre multi-to-one connecting high-grade data centre, the high-grade data centre fusion each area lower data center uploading of data, forming the real-time data of the whole road network.
| 7. The intelligent network according to claim 6 automatic driving automobile auxiliary sensing street lamp system, wherein the street lamp and the corresponding lower data centre is a high-speed network, through different levels of optical fibre connected with each street lamp and low-grade data centre to networking, the high-grade data centre and the 5G network service provider the combined building wireless network.
| 8. The intelligent network according to claim 4 automatic driving automobile auxiliary sensing street lamp system, wherein, the high-grade data centre receives the automatic driving vehicle sent from the vehicle of the planning of the route and the current position after the automatic driving automobile near real-time data of street lamp distribution to the vehicle terminal through the 5G network. | The system has a street lamp provided with a camera image sensor, a millimeter wave radar sensor and a laser radar sensor. The camera image sensor collects image data in a coverage area. The laser radar sensor collects road point cloud data. A sensor is connected with a data center. The data center receives data based on a computer vision technology and data fusion technology to perform data extraction and fusion process to form a road. The data center cooperates with a Fifth Generation (5G) network service provider to distribute real-time data corresponding to a road network to an automobile vehicle terminal through Fifth Generation (5G) network according to requirement of an automatic driving automobile to realize intelligent network communication of V2I. V2I intelligent network based linked automatic driving automobile auxiliary sensing street lamp system. The system detects multi-information through the road covered by a traffic network for obtaining instant data information to the automobile terminal in convenient manner, and avoids potential safety hazard and traffic jam. The drawing shows a schematic view of a V2I intelligent network based linked automatic driving automobile auxiliary sensing street lamp system. '(Drawing includes non-English language text)' |
Please summarize the input | The medium access method for the V2V communication.In the medium access method for the V2 V (Vehicle-to-Vehicle) communication, the base station obtains multiple vehicle each driving path information and the base station receives the V2V connect request signal from the connection request vehicle among the multiple vehicles and the base station selects the connection request vehicle and the vehicle in which the driving path most lengthways coincides with the future between multiple vehicles and the connection request vehicle and the vehicle based on multiple vehicle each driving path information as the connection object car and the base station assigns the resource block to the pair of the connection object car and connection request vehicle and the V2V communication is performed using the connection request vehicle and the resource block in which the connection object car is allocated.|1. A medium access method for the V2V communication, wherein: It comprises the step that the base station obtains multiple vehicle each driving path information; the step that the base station receives the V2 V (Vehicle-to-Vehicle) connect request signal from the connection request vehicle among the multiple vehicles; the step that the base station selects the connection request vehicle and the vehicle in which the driving path most lengthways coincides with the future between multiple vehicles and the connection request vehicle and the vehicle based on multiple vehicle each driving path information as the connection object car; and the step, it includes the step that the base station assigns the resource block to the pair of the connection object car and connection request vehicle, and the connection request vehicle and the step that the connection object car performs the V2V communication using the above-mentioned allocated resource block; and that the base station assigns the resource block to the pair of the connection object car and connection request vehicle. Is the step of grasping on the carrier frequency of the resource block allocated in the adjacent vehicle pair adjacent to the pair of the connection object car and connection request vehicle; the step of determining the size of the separation frequency based on the moving direction of the moving direction of the pair of the connection request vehicle and connection object car and adjacent vehicle pair; and the step of assigning the carrier frequency and the resource block having the separated carrier frequency over the separation frequency of the resource block allocated in the adjacent vehicle pair to the pair of the connection object car and connection request vehicle.
| 2. The medium access method for the V2V communication of claim 1, wherein: the step that the base station obtains multiple vehicle each driving path information comprises the step that the base station receives the driving path information of the corresponding to vehicle from the navigation device for being included respectively on multiple vehicles.
| 3. The medium access method for the V2V communication of claim 1, wherein: the step that the base station obtains multiple vehicle each driving path information comprises the step that the base station receives multiple vehicle each driving path information from the navigation server guiding the route of reaching even the destination location to multiple vehicles.
| 4. The medium access method for the V2V communication of claim 1, wherein: the step that the base station obtains multiple vehicle each driving path information comprises the step that the base station receives multiple vehicle each driving path information from the autonomous driving server which controls multiple vehicles so that multiple vehicles move to the autonomous driving mode.
| 5. The medium access method for the V2V communication of claim 1, wherein: The step that the base station selects the connection request vehicle and the vehicle in which the driving path most lengthways coincides with the future between multiple vehicles and the connection request vehicle and the vehicle based on multiple vehicle each driving path information as the connection object car comprise the step that the base station determines vehicles having within the connection request vehicle and V2V communication possible distance among multiple vehicles with candidate vehicles; and the step that the base station selects the connection request vehicle and the vehicle in which the driving path most lengthways coincides with the future between candidate vehicles and the connection request vehicle and the vehicle based on the candidate vehicle each driving path information as the connection object car.
| 6. The medium access method for the V2V communication of claim 5, wherein: The step that the base station selects the connection request vehicle and the vehicle in which the driving path most lengthways coincides with the future between candidate vehicles and the connection request vehicle and the vehicle based on the candidate vehicle each driving path information as the connection object car comprise the step that the base station decides the bifurcations existing in the driving path of the connection request vehicle based on the driving path information of the connection request vehicle as standard bifurcations; and the step that the base station selects the candidate vehicle which it very much most includes standard bifurcations based on the candidate vehicle each driving path information of candidate vehicles on the driving path from the current position as the connection object car forward.
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| 9. The medium access method for the V2V communication of claim 1, wherein: The step of determining the size of the separation frequency based on the moving direction of the moving direction of the pair of the connection request vehicle and connection object car and adjacent vehicle pair comprise the step the moving direction of the moving direction of the pair of the connection request vehicle and connection object car and adjacent vehicle pair are identical; and of determining the separation frequency with the first frequency; and the step the moving direction of the moving direction of the pair of the connection request vehicle and connection object car and adjacent vehicle pair are opposite from each other; and of determining the separation frequency with the secondary frequency which is greater than the first frequency.
| 10. The medium access method for the V2V communication of claim 1, wherein: the medium access method further includes choosing and connecting as the candidate pair the vehicle in which the base station treats based on multiple vehicle each driving path information respectively on multiple vehicles and the driving path most lengthways coincides with the future while it has within the V2V communication possible distance.
| 11. The medium access method for the V2V communication of claim 10, wherein: the step that the base station selects the connection request vehicle and the vehicle in which the driving path most lengthways coincides with the future between multiple vehicles and the connection request vehicle and the vehicle based on multiple vehicle each driving path information as the connection object car comprise the step that the base station determines the vehicle connected among multiple vehicles to the connection request vehicle and candidate pair with the connection object car.
| The method involves receiving vehicle-to-vehicle (V2V) connect request signal from a connection request vehicle i.e. car by a base station. The connection request vehicle is selected (S300) by the base station. A driving path is coincided between the connection request vehicle and a connection object vehicle. A resource block is assigned to the connection object vehicle and the connection request vehicle by the base station. Carrier frequency of the resource block is grasped. The carrier frequency of the resource block is assigned with separated carrier frequency to pair the connection object vehicle and the connection request vehicle. Method for accessing V2V communication medium by utilizing a base station. The method enables assigning the resource block to the connection object vehicle and the connection request vehicle by the base station so as to increase network lifetime. The method enables reducing communication overhead defects and inter-carrier interference. The drawing shows a flowchart illustrating a method for accessing V2V communication medium by utilizing base station. '(Drawing includes non-English language text)' S300Step for selecting connection request vehicle by base station |
Please summarize the input | APPARATUS AND METHOD FOR DETECTING VIOLATION OF TRAFFIC SIGNAL USING SENSOR OF AUTONOMOUS VEHICLE AND V2I COMMUNICATIONDisclosed is an apparatus and method for regulating signal-violating objects using a sensor of an autonomous vehicle and V2I communication, and a method for regulating signal-violating objects using a sensor and V2I communication of an autonomous vehicle according to an embodiment of the present invention, (a) autonomous driving Based on the surrounding information collected through the target vehicle, which is the vehicle, determining whether the target vehicle satisfies a predefined reference state as a state in which a signal violation object can be controlled through the surrounding information, (b) the If the target vehicle satisfies the reference condition, detecting the signal-violating object based on the surrounding information; and (c) displaying a report image captured to include the signal-violating object based on the detection result to the self-driving vehicle. It may include transmitting through V2I communication to a predetermined infrastructure interlocked with.|1. In the method for regulating signal-violating objects using autonomous vehicle sensors and V2I communication, (a) based on surrounding information collected through a target vehicle, which is an autonomous vehicle, the target vehicle detects a signal-violating object through the surrounding information. Determining whether or not a predefined reference state is satisfied as a state in which enforcement is possible;
(b) when the target vehicle meets the reference state, detecting the signal violation object based on the surrounding information; And (c) transmitting, based on the detection result, a tip video captured to include the signal violation object to a predetermined infrastructure linked to the autonomous vehicle through V2I communication, step (a) is to determine whether the target vehicle meets the reference state based on the location of the target vehicle, the condition of the road on which the target vehicle is located, and the arrangement of surrounding objects with respect to the target vehicle, wherein (a) The step includes: (a1) determining whether signal information on the road where the target vehicle is located is a stop signal;
(a2) based on location information on the road of the target vehicle, determining whether the target vehicle is located within a preset reference distance from the stop line of the road considering the size of a single vehicle; and (a3) determining the presence or absence of surrounding objects around the target vehicle, wherein in the step (b), when it is determined that the state of the target vehicle meets the reference state, the signal violation object An enforcement method that activates a detection operation and, when it is determined that the state of the target vehicle does not meet the reference state, deactivates the detection operation of the signal violation object.
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| 3. The enforcement method according to claim 1, wherein step (a1) derives equalization information of a traffic light structure disposed in front of the target vehicle from an image acquired through a vision sensor mounted on the target vehicle.
| 4. The method of claim 1, wherein the step (a1) involves receiving lighting information of a traffic light structure on a road where the target vehicle is located through the V2I communication.
| 5. The method of claim 1, wherein the step (a2) determines whether the target vehicle is located within the reference distance based on precision road map (Local Dynamic Map, LDM) information provided in the target vehicle and GPS information of the target vehicle. A crackdown method to determine whether or not.
| 6. The method of claim 1, wherein the surrounding information includes object movement information including at least one of a relative speed, a relative distance, and a relative acceleration of an object traveling around the target vehicle, wherein step (b) comprises: To detect the signal violation object of the object based on the object movement information, enforcement method.
| 7. The enforcement method of claim 6, wherein the object movement information is obtained based on at least one of a lidar sensor and a vision sensor mounted on the target vehicle.
| 8. The method of claim 1, wherein the information video is a time period set in advance based on a time point at which the signal violation object is detected in step (b) from an original video obtained time-sequentially through a vision sensor mounted on the target vehicle. Which is a partial image extracted to correspond to, the enforcement method.
| 9. The enforcement method according to claim 1, characterized in that the reporting image is a partial image extracted from an original image obtained time-sequentially through a vision sensor mounted on the target vehicle so that the rear license plate of the traffic violation object is identified. Way.
| 10. The enforcement method of claim 1, wherein step (c) transmits vehicle number information of the traffic violation object identified from the report image together with the report image.
| 11. An apparatus for controlling signal-violating objects using a sensor of an autonomous vehicle and V2I communication, wherein the target vehicle is capable of cracking down on a signal-violating object through the surrounding information based on surrounding information collected through a target vehicle, which is an autonomous vehicle. a state analyzer that determines whether or not a state meets a predefined reference state;
a detection unit that detects the signal violation object based on the surrounding information when the target vehicle meets the reference state; And a communication unit that transmits a report image captured to include the signal violation object based on the detection result to a predetermined infrastructure linked to the autonomous vehicle through V2I communication, and the state analysis unit includes the target vehicle. Based on the location of the target vehicle, the road condition on which the target vehicle is located, and the arrangement of surrounding objects with respect to the target vehicle, it is determined whether the target vehicle meets the reference state, wherein the state analysis unit determines the location of the target vehicle. It is determined whether the signal information on the road is a stop signal, and based on the location information on the road of the target vehicle, whether the target vehicle is located within a preset reference distance from the stop line of the road considering the size of a single vehicle. determines whether or not there are surrounding objects around the target vehicle, and the detection unit, if it is determined that the state of the target vehicle meets the reference state, activates a detection operation of the signal violation object, An enforcement device that deactivates the detection operation of the signal violation object when it is determined that the state of the target vehicle does not meet the reference state.
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| 13. The method of claim 11, wherein the state analysis unit derives equalization information of a traffic light structure placed in front of the target vehicle from an image acquired through a vision sensor mounted on the target vehicle or the target vehicle through the V2I communication. Receives lighting information of the traffic light structure of the located road, and the condition analysis unit determines the target vehicle based on the precision road map (Local Dynamic Map, LDM) information provided in the target vehicle and the GPS information of the target vehicle. A enforcement device that determines whether the device is located within a distance.
| 12. The method of claim 11, wherein the surrounding information includes object movement information including at least one of a relative speed, a relative distance, and a relative acceleration of an object traveling in the vicinity of the target vehicle, and wherein the sensor comprises the object movement information. On the basis of which to detect the signal violation object of the object, the enforcement device.
| 15. According to claim 11, wherein the communication unit, to transmit the license plate information of the signal violation object identified from the report image together with the report image, the enforcement device. | The method involves detecting a signal-violating object through surrounding information collected through a target vehicle (1), based on surrounding information collected through a target vehicle, which is an autonomous vehicle. A signal offending object is detected based on the surrounding information when the target vehicle meets a reference state. A report video captured to include the signal violation object is transmitted to a predetermined infrastructure (200) linked with a self-driving vehicle based on a detection result. A determination is made whether signal information of a road is a stop signal. The target vehicle is located within a preset reference distance from a stop line of the road. An INDEPENDENT CLAIM is included for an apparatus for controlling signal-violating objects using a sensor of an autonomous vehicle and V2I communication. Method for regulating signal-violating objects using autonomous vehicle sensors and V2I communication. The self-driving vehicle detects a traffic violation vehicle at an intersection and transmits a report image of the traffic violation object so as to control signal violation objects using sensors and V2I communication. The method enables actively cracking down traffic violation vehicles in different locations using communication with the autonomous vehicle without installing a separate information collection device around the road. The drawing shows a schematic configuration diagram of the enforcement system using the autonomous vehicle including the signal violation object enforcement device using the sensor of the autonomous vehicle and V2I communication. 1Target vehicle10Enforcement system12Vision sensor100Signal violation object enforcement device200Infrastructure |
Please summarize the input | DYNAMIC AUTONOMOUS DRIVING ASSISTANT SYSTEM USING V2I COMMUNICATIONA dynamic autonomous driving support system using V2I communication is disclosed, and the dynamic autonomous driving support method using V2I communication according to an embodiment of the present application includes a vehicle operation design domain (ODD) and a driving path Receiving data and sensor data collected by a sensor installed in the vehicle, collecting driving environment data related to the driving route, designing the driving based on the driving data, the sensor data, and the driving environment data It may include dynamically changing the area and transmitting information on the changed driving design area to the vehicle.|1. A method for supporting dynamic autonomous driving using V2I communication, the method comprising: receiving driving data including an operation design domain (ODD) and a driving route of a vehicle and sensor data collected by a sensor installed in the vehicle;
collecting driving environment data associated with the driving route;
dynamically changing the driving design area based on the driving data, the sensor data, and the driving environment data; and transmitting information on the changed driving design area to the vehicle, wherein the dynamically changing the driving design area includes dividing the driving path into link units; and generating determination information on whether or not autonomous driving is permitted for each section of the driving route based on the driving data, the sensor data, and the driving environment data in consideration of the divided links, wherein the determination information is generated. In the step of performing the first determination information for determining that the vehicle is capable of autonomous driving based on the sensor data, for a link in which it is determined that the vehicle is capable of autonomous driving only with the sensor data without the driving environment data. and for a link in which the vehicle cannot perform autonomous driving only with the sensor data, but it is determined that autonomous driving is possible by converging the sensor data and the driving environment data, the vehicle determines that the sensor data and the driving environment data are capable of autonomous driving. Second determination information for determining that autonomous driving is possible based on the link is generated, and even if the vehicle fuses the sensor data and the driving environment data, autonomous driving is determined to be impossible. Generating third decision information for determining that the autonomous driving of the vehicle is impossible, wherein the first to third decision information are individually generated for each unit section including the link or at least one link. In the step of collecting the driving environment data, the driving environment data associated with the driving path of the vehicle is selected from among the driving environment data obtained from the infrastructure, and the driving environment data is selected from a plurality of infrastructures for one link. When redundant data is received, data corresponding to any one of the plurality of infrastructures is allocated as the driving environment data based on the reliability information of each of the plurality of infrastructures, and the reliability information is if the infrastructure is a photographing device type., It is characterized in that the higher the photographing device installed to photograph the road corresponding to the link in a relatively wide area, the interface visualizing the first to third determination information, It is visualized through a display unit provided in a dynamic self-driving device provided in the vehicle or a user terminal interworking with the dynamic self-driving device, and based on an autonomous driving selection input for each link received based on the interface, for each link. Whether or not autonomous driving is determined, and if an autonomous driving setting input preset to perform autonomous driving for a section in which a specific type of determination information is generated among the first to third determination information is received in advance, wherein an autonomous driving function is activated even if the autonomous driving selection input is not received for at least one of the link and the unit section in which the determination information of the specific type is generated.
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| 5. The method of claim 1, further comprising, after the transmitting step, transmitting the driving environment data collected for the link where the second determination information is generated to the vehicle..
| 6. The method of claim 1, wherein the generating of the determination information comprises: predicting road surface condition information and shadow information for a link through which the vehicle will pass based on the driving environment data; and generating the determination information for a corresponding link based on the predicted road surface condition information and shadow information.
| 7. The method of claim 6, wherein the driving environment data includes time-series data on the road surface condition of the link, and the predicting includes an LSTM (The dynamic autonomous driving support method of predicting the road surface condition information based on a Long Short-Term Memory)-based model.
| 8. The method of claim 6, wherein the driving environment data includes image data on the road surface of the link, and the predicting includes predicting the presence or absence of a shadow on the road surface and a change in the length of the shadow when the image data is input. A dynamic autonomous driving support method of predicting the shadow information based on a learned You Only Look Once (YOLO)-based model.
| 9. In a dynamic autonomous driving method using V2I communication, driving data including an operation design domain (ODD) and a driving route of a vehicle and sensor data collected by a sensor installed in the vehicle are used as a dynamic autonomous driving support device transmitting;
receiving, from the dynamic autonomous driving support device, determination information on whether or not autonomous driving is allowed for each section of the driving route, which is generated based on the driving data, the sensor data, and driving environment data collected in association with the driving route; and determining whether or not to perform autonomous driving for each section of the vehicle based on the determination information, wherein the apparatus for supporting dynamic autonomous driving divides the driving path in units of links and considers the divided links. Based on the driving data, the sensor data, and the driving environment data, determination information on autonomous driving per section of the driving route is generated, and the dynamic autonomous driving support apparatus determines whether the vehicle is detected by the sensor without the driving environment data. For a link that is determined to be capable of autonomous driving only with data, first determination information for determining that the vehicle is in a state capable of autonomous driving based on the sensor data is generated, and the vehicle cannot perform autonomous driving only with the sensor data. One, for a link that is determined to be capable of autonomous driving by fusing the sensor data and the driving environment data, Second determination information for determining that the vehicle is in a state in which autonomous driving is possible based on the sensor data and the driving environment data is generated, and even if the vehicle fuses the sensor data and the driving environment data, autonomous driving is not possible. Third decision information for determining that the vehicle is in a state in which autonomous driving is impossible is generated for a link determined to be the link, wherein the first to third decision information include the link or at least one link. Characterized in that the dynamic self-driving support device selects driving environment data associated with the driving route of the vehicle from among the driving environment data obtained from the infrastructure, and multiple links for one link are individually generated. When the driving environment data is redundantly received from the infrastructure of, allocating data corresponding to any one of the plurality of infrastructures as the driving environment data based on reliability information of each of the plurality of infrastructures; If the infrastructure is a type of photographing device, the reliability information may be given higher to a photographing device installed to photograph a road corresponding to the link in a relatively large area, and after the receiving step, provided in the vehicle displaying an interface for visualizing the first to third determination information through a display unit provided in a dynamic self-driving device or a user terminal interworking with the dynamic self-driving device; and receiving an autonomous driving selection input and an autonomous driving setting input for each link based on the interface, wherein the step of determining whether or not to perform autonomous driving includes the link based on the autonomous driving selection input for each link. It is determined whether or not autonomous driving is performed, and if an autonomous driving setting input preset to perform autonomous driving is received in advance for a section in which a specific type of determination information is generated among the first to third determination information, The autonomous driving function is activated even if the autonomous driving selection input is not received for at least one of the link and the unit section in which the specific type of determination information is generated.
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| 10. The method of claim 9, wherein the determination information is individually generated for each link included in the driving route, and the determining step comprises autonomously for each link based on the determination information received for each link. A dynamic self-driving method that determines whether to perform driving.
| 12. The method of claim 11, further comprising: transmitting a driving environment data request signal for the link in which the second determination information is generated to the dynamic autonomous driving support device;
receiving the driving environment data from the dynamic autonomous driving support device; and performing autonomous driving on a corresponding link based on the sensor data and the driving environment data.
| 13. A dynamic autonomous driving support device using V2I communication, comprising: a communication unit configured to receive driving data including an operation design domain (ODD) and a driving route of a vehicle and sensor data collected by a sensor installed in the vehicle;
a collection unit that collects driving environment data associated with the driving route; and a determination unit that dynamically changes the driving design area based on the driving data, the sensor data, and the driving environment data, wherein the determination unit divides the driving route by link unit, and the divided link to generate determination information on autonomous driving per section of the driving route based on the driving data, the sensor data, and the driving environment data in consideration of For a link that is determined to be capable of autonomous driving only, first determination information for determining that the vehicle is in a state capable of autonomous driving based on the sensor data is generated, and whether or not the vehicle is capable of autonomous driving only with the sensor data, For a link determined to be capable of autonomous driving by fusing the sensor data and the driving environment data, Second determination information for determining that the vehicle is in a state in which autonomous driving is possible based on the sensor data and the driving environment data is generated, and even if the vehicle fuses the sensor data and the driving environment data, autonomous driving is not possible. Third decision information for determining that the vehicle is in a state in which autonomous driving is impossible is generated for a link determined to be the link, wherein the first to third decision information include the link or at least one link. characterized in that it is individually generated for each unit section, wherein the communication unit transmits information on the changed driving design area to the vehicle, and the collection unit includes a driving route of the vehicle among the driving environment data obtained from infrastructure Select driving environment data associated with, but if the driving environment data is received redundantly from a plurality of infrastructures for one link, Based on the reliability information of each of the plurality of infrastructures, data corresponding to any one of the plurality of infrastructures is allocated as the driving environment data, and the reliability information corresponds to the link if the infrastructure is a photographing device type. An interface visualizing the first to third determination information is provided in the dynamic self-driving device provided in the vehicle. Whether or not autonomous driving for each link is determined based on an autonomous driving selection input for each link visualized through a display unit or a user terminal interworking with the dynamic autonomous driving device and received based on the interface, When an autonomous driving setting input is received in advance to perform autonomous driving for a section in which a specific type of determination information is generated among the first to third determination information, wherein an autonomous driving function is activated even if the autonomous driving selection input is not received for at least one of the link and the unit section where the determination information of the specific type is generated.
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| 15. The method of claim 13, wherein the determination unit generates the determination information for each divided link, and the communication unit transmits the driving environment data collected for the link where the second determination information is generated to the vehicle. Phosphorus, dynamic autonomous driving support device.
| 16. The method of claim 15, wherein the determination unit predicts road surface condition information and shadow information for a link through which the vehicle will pass based on the driving environment data, and predicts road surface condition information and shadow information on the link based on the predicted road surface condition information and shadow information. For generating the determination information for, a dynamic autonomous driving support device. | The method involves receiving driving data including an operation design domain (ODD) and a driving route of a vehicle (1) and sensor data collected by a sensor installed in the vehicle. The driving environment data associated with the driving route is collected. The driving design area is changed dynamically based on the driving data, the sensor data, and the driving environment data. The information on the changed driving design area is transmitted to the vehicle. The determination information on whether autonomous driving is possible for each section of the driving route is generated based on the driving data, the sensor data and the driving environment data. An INDEPENDENT CLAIM is included for a dynamic autonomous driving support device. Method for supporting dynamic autonomous driving using vehicle-to-infrastructure communication. The method enables supporting autonomous driving by actively determining the ODD for an autonomous vehicle according to road conditions. The method enables providing a dynamic autonomous driving support system using vehicle to infrastructure (V2I) communication so as to support safe and highly reliable autonomous driving. The drawing shows a schematic view of a dynamic autonomous driving support system using V2I communication. (Drawing includes non-English language text) 1Vehicle10Dynamic autonomous driving support system20Network100Dynamic autonomous driving support device300Infrastructure |
Please summarize the input | The vehicle charging and communication systemVehicle-to-vehicle charging and communication systems are provided. The vehicle-to-vehicle charging and communication system includes a first electric vehicle including a first battery unit and a first communication terminal unit, a second electric vehicle including a second battery unit and a second communication terminal unit, and the first and second electric vehicles through a mesh network. 2 A server unit connected to the communication terminal unit, and a block in which the first electric vehicle corresponds to a first node, the second electric vehicle corresponds to a second node, and connects the first and second nodes in a block chain structure A chain network is included, and the battery unit and the communication terminal are interlocked to provide the power required amount of the battery unit to the server unit through the communication terminal unit, and the second electric vehicle approves the request for power charging of the first electric vehicle , The first and second electric vehicles transmit and receive location information through the mesh network, and compensation is paid to the second electric vehicle through the blockchain network.
|1. A first electric vehicle including a first battery unit and a first communication terminal unit; A second electric vehicle including a second battery unit and a second communication terminal unit; A server unit connected to the first and second communication terminals through a mesh network; And a blockchain network in which the first electric vehicle corresponds to a first node, the second electric vehicle corresponds to a second node, and connects the first and second nodes in a block chain structure. Including, wherein the battery unit and the communication terminal are interlocked to provide the power required amount of the battery unit to the server unit through the communication terminal unit, and when the second electric vehicle approves the power charging request of the first electric vehicle, the The first and second electric vehicles transmit and receive location information to and from each other through the mesh network, and compensation is paid to the second electric vehicle through the blockchain network, and power to a third electric vehicle within the communication range of the first electric vehicle When a charging request is provided and the third electric vehicle fails to provide power charging for the first electric vehicle, the electric power charging request is transmitted to the second electric vehicle within the communication range of the third electric vehicle. And communication systems.
| 2. The vehicle-to-vehicle charging and communication system of claim 1, wherein the first electric vehicle and the second electric vehicle move to a position where wireless charging is performed through autonomous driving control.
| 3. The vehicle-to-vehicle charging and communication system of claim 1, wherein the first electric vehicle and the second electric vehicle exchange electric power through a mutual magnetic resonance method.
| 4. The vehicle-to-vehicle charging and communication system of claim 1, wherein a wireless power receiver is installed at a front end of the first electric vehicle, and a wireless power transmitter is installed at a rear end of the second electric vehicle.
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| 13. delete | The system has a first electric vehicle (10) including a first battery unit and a first communication terminal unit in a power equipment management system. A second electric vehicle (20) includes a second battery unit and a second communication terminal unit. A server unit (30) is connected to the first and second communication terminal units through a mesh network. A block chain network (100) is provided in which the first electric vehicle corresponds to first node, the second electric vehicle corresponds to second node, and connects the first and second nodes in a block chain structure. The battery unit and the communication terminal unit are interlocked so that the required power of battery unit is provided to server unit through communication terminal unit. The first and second electric vehicles transmit and receive location information with each other through the mesh network, when the second electric vehicle approves the request for power charging of first electric vehicle. Inter-vehicle charging and communication system. The vehicle having sufficient battery level can be searched for by sharing information between electric vehicles, and the vehicle requiring battery charging can receive power from the vehicle. The convenience of charging the electric vehicle is increased since the power can be exchanged while the vehicle is running. The surplus power of the electric vehicle can be easily bought and sold, and the reward can be easily paid using the blockchain technology for the electric vehicle that provided the electric power. The drawing shows a block diagram illustrating an inter-vehicle charging and communication system. (Drawing includes non-English language text) 10First electric vehicle20Second electric vehicle30Server unit100Block chain network |
Please summarize the input | Systems and Methods for Training Machine-Learned Models with Deviating Intermediate RepresentationsSystems and methods for vehicle-to-vehicle communications are provided. An adverse system can obtain sensor data representative of an environment proximate to a targeted system. The adverse system can generate an intermediate representation of the environment and a representation deviation for the intermediate representation. The representation deviation can be designed to disrupt a machine-learned model associated with the target system. The adverse system can communicate the intermediate representation modified by the representation deviation to the target system. The target system can train the machine-learned model associated with the target system to detect the modified intermediate representation. Detected modified intermediate representations can be discarded before disrupting the machine-learned model.What is claimed is:
| 1. A computer-implemented method, the method comprising:
obtaining, by a computing system comprising one or more computing devices, sensor data representative of a secondary environment proximate to an autonomous vehicle;
generating, by the computing system, an intermediate representation for the autonomous vehicle based, at least in part, on the sensor data, wherein the intermediate representation is descriptive of at least a portion of the secondary environment;
determining, by the computing system, an intermediate representation deviation for the intermediate representation based, at least in part, on the intermediate representation and a machine-learned model associated with the autonomous vehicle;
generating, by the computing system, data indicative of a modified intermediate representation based, at least in part, on the intermediate representation and the intermediate representation deviation; and
communicating, by the computing system, the data indicative of the modified intermediate representation to a vehicle computing system associated with the autonomous vehicle.
| 2. The computer-implemented method of claim 1, wherein the machine-learned model associated with the autonomous vehicle comprises a machine-learned model utilized by the vehicle computing system to detect one or more objects within a surrounding environment of the autonomous vehicle.
| 3. The computer-implemented method of claim 2, wherein the machine-learned model is configured to output one or more bounding box proposals indicative of one or more objects within the surrounding environment of the autonomous vehicle based, at least in part, on one or more intermediate representations.
| 4. The computer-implemented method of claim 3, wherein determining the intermediate representation deviation for the intermediate representation based, at least in part, on the intermediate representation and the machine-learned model associated with the autonomous vehicle comprises:
obtaining, by the computing system via a second machine-learned model, one or more ground truth bounding box proposals based, at least in part, on the intermediate representation, wherein the second machine-learned model is the same as the machine-learned model;
obtaining, by the computing system via the second machine-learned model, one or more deviating bounding box proposals based, at least in part, on the modified intermediate representation; and
modifying, by the computing system, the intermediate representation deviation for the intermediate representation based, at least in part, on a comparison between the one or more ground truth bound box proposals and the one or more deviating bounding box proposals.
| 5. The computer-implemented method of claim 4, wherein modifying the intermediate representation deviation for the intermediate representation based, at least in part, on the comparison between the one or more ground truth bound box proposals and the one or more deviating bounding box proposals comprise:
determining, by the computing system, an adversarial loss for the intermediate representation deviation based, at least in part, on the one or more ground truth bound box proposals and the one or more deviating bounding box proposals; and
modifying, by the computing system, the intermediate representation deviation based, at least in part, on adversarial loss, wherein the intermediate representation deviation is modified to minimize the adversarial loss over the one or more deviating bounding box proposals.
| 6. The computer-implemented method of claim 5, wherein each respective ground truth bounding box proposal of the one or more ground truth bounding box proposals comprises a respective ground truth class score indicative of respective ground truth object classification and one or more respective ground truth bounding box parameters indicative of a respective ground truth spatial location and one or more respective ground truth dimensions of the respective ground truth object classification, and
wherein each respective deviating bounding box proposal of the one or more respective deviating bounding box proposals comprises a respective deviating class score indicative of a respective deviating object classification and one or more respective deviating bounding box parameters indicative of a respective deviating spatial location and one or more respective deviating dimensions of the respective deviating object classification.
| 7. The computer-implemented method of claim 6, wherein the adversarial loss is determined based, at least in part, on a difference between a ground truth class score corresponding to at least one ground truth bounding box proposal and a deviating class score corresponding to a deviating bounding box proposal corresponding to the at least one ground truth bounding box proposal.
| 8. The computer-implemented method of claim 6, wherein the adversarial loss is determined based, at least in part, on a difference between one or more ground truth bounding box parameters corresponding to at least one ground truth bounding box proposal and one or more deviating bounding box parameters corresponding to a deviating bounding box proposal corresponding to the at least one ground truth bounding box proposal.
| 9. The computer-implemented method of claim 4, wherein the computing system is onboard a transmitting autonomous vehicle physically located proximate to the autonomous vehicle, and wherein the intermediate representation deviation is associated with a first time.
| 10. The computer-implemented method of claim 9, further comprising:
obtaining, by the computing system, movement data indicative of a motion of the transmitting autonomous vehicle from the first time to a second time;
obtaining, by the computing system, second sensor data representative of the secondary environment proximate to the autonomous vehicle at the second time;
generating, by the computing system, a second intermediate representation for the autonomous vehicle based, at least in part, on the second sensor data; and
determining, by the computing system, a second intermediate representation deviation for the second intermediate representation based, at least in part, on the intermediate representation deviation associated with the first time and the movement data.
| 11. A computing system comprising:
one or more processors; and
one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the system to perform operations, the operations comprising:
obtaining a plurality of intermediate representations associated with an autonomous vehicle, wherein each intermediate representation is descriptive of at least a portion of a secondary environment proximate to the autonomous vehicle at a plurality of times;
generating a surrogate machine-learned model based, at least in part, on the plurality of intermediate representations;
obtaining a target intermediate representation from the plurality of intermediate representations;
determining an intermediate representation deviation for the target intermediate representation based, at least in part, on the target intermediate representation and the surrogate machine-learned model;
generating data indicative of a modified intermediate representation based, at least in part, on the target intermediate representation and the intermediate representation deviation; and
communicating the data indicative of the modified intermediate representation to a vehicle computing system associated with the autonomous vehicle.
| 12. The computing system of claim 11, wherein each intermediate representation of the plurality of intermediate representations is generated by a first portion of a machine-learned model associated with the autonomous vehicle, and wherein a first portion of the surrogate machine-learned model is trained to output a surrogate intermediate representation substantially similar to the plurality of intermediate representations.
| 13. The computing system of claim 11, wherein generating the surrogate machine-learned model based, at least in part, on the plurality of intermediate representations comprises:
obtaining sensor data representative of surrogate environment proximate to the autonomous vehicle; and
generating the surrogate machine-learned model based, at least in part, on the plurality of intermediate representations and the sensor data.
| 14. The computing system of claim 13, wherein generating the surrogate machine-learned model based, at least in part, on the plurality of intermediate representations further comprises:
generating, via a first portion of the surrogate machine-learned model, a surrogate intermediate representation based, at least in part, on the sensor data;
generating, via a machine-learned discriminator model, a discriminator loss based, at least in part, on the surrogate intermediate representation and at least one of the plurality of intermediate representations; and
training the surrogate machine-learned model to minimize the discriminator loss.
| 15. The computing system of claim 14, wherein the discriminator loss is indicative of a difference between the surrogate intermediate representation and the at least one intermediate representation.
| 16. The computing system of claim 11, wherein a second portion of the surrogate machine-learned model is configured to output one or more bounding box proposals indicative of one or more objects within the secondary environment proximate to the autonomous vehicle based, at least in part, on the target intermediate representation.
| 17. The computer-implemented method of claim 16, wherein determining the intermediate representation deviation for the target intermediate representation based, at least in part, on the target intermediate representation and the surrogate machine-learned model comprises:
obtaining, via the second portion of the surrogate machine-learned model, one or more ground truth bounding box proposals based, at least in part, on the target intermediate representation;
obtaining, via the second portion of the surrogate machine-learned model, one or more deviating bounding box proposals based, at least in part, on the modified intermediate representation; and
modifying the intermediate representation deviation for the target intermediate representation based, at least in part, on a comparison between the one or more ground truth bound box proposals and the one or more deviating bounding box proposals.
| 18. An autonomous vehicle comprising:
one or more sensors;
one or more processors; and
one or more tangible, non-transitory, computer readable media that collectively store instructions that when executed by the one or more processors cause the one or more processors to perform operations, the operations comprising:
obtaining, via the one or more sensors, sensor data representative of a surrounding environment of the autonomous vehicle;
generating, via a first portion of a machine-learned model, an intermediate representation based, at least in part, on the sensor data, wherein the intermediate representation is descriptive of at least a portion of the surrounding environment of the autonomous vehicle;
determining an intermediate representation deviation for the intermediate representation based, at least in part, on the intermediate representation and the machine-learned model;
generating, data indicative of a modified intermediate representation based, at least in part, on the intermediate representation and the intermediate representation deviation; and
communicating the data indicative of the modified intermediate representation to one or more devices associated with a target autonomous vehicle.
| 19. The autonomous vehicle of claim 18, wherein the target autonomous vehicle is configured to utilize a second portion of the machine-learned model to detect one or more objects within a surrounding environment of the target autonomous vehicle.
| 20. The autonomous vehicle of claim 18, wherein the machine-learned model is trained to detect the modified intermediate representation. | The method (800) involves obtaining (802) sensor data representative of a secondary environment proximate to an autonomous vehicle by a computing system, where the computing system is provided with computing devices. An intermediate representation for the autonomous vehicle is generated (804) based on the sensor data by the computing system, where the intermediate representation is descriptive of a portion of the secondary environment. Data indicative of the modified intermediate representation is generated (808) based on the intermediate representation and intermediate representation deviation by the computer system. The data indicative of the modified intermediate representation is communicated (810) to a vehicle computing system associated with the autonomous vehicle by the computing system. An INDEPENDENT CLAIM is included for a system for training a machine-learned model for performing operations of an autonomous vehicle. Method for training a machine-learned model for performing operations of an autonomous vehicle (claimed) i.e. car (from drawings). The method enables improving safety of passengers of an autonomous vehicle, safety of surroundings of the autonomous vehicle and experience of a rider and/or operator of the vehicle, and reducing traffic congestion in communities as well as providing alternate forms of transportation that provide environmental benefits. The drawing shows a flow diagram illustrating a method for training a machine-learned model for performing operations of an autonomous vehicle. 800Method for training machine-learned model for performing operations of autonomous vehicle 802Obtaining sensor data representative of secondary environment proximate to autonomous vehicle by computing system 804Generating intermediate representation for autonomous vehicle based on sensor data by computing system 808Generating data indicative of modified intermediate representation 810Communicating data indicative of modified intermediate representation to vehicle computing system |
Please summarize the input | Telecommunications Network For VehiclesSystems and methods for facilitating communication with autonomous vehicles are provided. In one example embodiment, the autonomous vehicle telecommunications network system includes a first point-of-presence (POP) interface configured to obtain a first communication associated with a first autonomous vehicle and to route the first communication associated with the first autonomous vehicle. The autonomous vehicle telecommunications network system includes a first security system configured to authenticate the first autonomous vehicle. The first POP interface is separate from the first security system. The autonomous vehicle telecommunications network system includes a first data center system configured to receive the first communication associated with the first autonomous vehicle that is authenticated and to provide data to the first autonomous vehicle. The first POP interface is separate from the first data center system. The autonomous vehicle telecommunications network system can include a similarly configured second POP interface, second security system, and second data center system.|1. A vehicle telecommunications network system comprising:
a first point-of-presence interface configured to obtain a first communication associated with a first vehicle and to route the first communication, wherein the first point-of-presence interface is remote from the first vehicle;
a first security system configured to authenticate the first vehicle, wherein the first security system is remote from the first vehicle;
a first data center system that is remote from the first vehicle, the first data center system configured to receive the first communication associated with the first vehicle that is authenticated and to provide data to the first vehicle;
a second point-of-presence interface configured to obtain a second communication associated with a second vehicle and to route the second communication, wherein the second point-of-presence interface is remote from the second vehicle and the first vehicle;
a second security system configured to authenticate the second vehicle, wherein the second security system is remote from the second vehicle and the first vehicle; and
a second data center system that is remote from the first vehicle and the second vehicle, the second data center system configured to receive the second communication associated with the second vehicle that is authenticated and to provide data to the second vehicle, wherein the first point-of-presence interface, the first security system, and the first data center are remote from the second vehicle.
| 2. The vehicle telecommunications network system of claim 1, wherein the first point-of-presence interface is separate from the first security system and the first data center system, and wherein the second point-of-presence interface is separate from the second security system and the second data center system.
| 3. The vehicle telecommunications network system of claim 1, wherein the first point-of-presence interface, the first security system, and the first data center system are associated with a first geographic region, and
wherein the second point-of-presence interface, the second security system, and the second data center system are associated with a second geographic region that is different from the first geographic region.
| 4. The vehicle telecommunications network system of claim 3, wherein the first vehicle is located within the first geographic region and wherein the second vehicle is located within the second geographic region.
| 5. The vehicle telecommunications network system of claim 1, wherein the first point-of-presence interface is further configured to obtain the second communication associated with the second vehicle when the second point-of-presence interface is unavailable.
| 6. The vehicle telecommunications network system of claim 1, wherein the second point-of-presence interface is further configured to obtain the first communication associated with the first vehicle when the first point-of-presence interface is unavailable.
| 7. The vehicle telecommunications network system of claim 1, further comprising:
a first vehicle assistance system configured to facilitate a provision of assistance to at least one of the first vehicle or a first user of the first vehicle; and
a second vehicle assistance system configured to facilitate a provision of assistance to at least one of the second vehicle or a second user of the second vehicle.
| 8. A vehicle telecommunications network system comprising:
a first point-of-presence interface configured to obtain a first communication associated with a first vehicle and to route the first communication, wherein the first point-of-presence interface is remote from the first vehicle;
a first security system configured to authenticate the first vehicle, wherein the first point-of-presence interface is separate from the first security system, wherein the first security system is remote from the first vehicle; and
a first data center system that is remote from the first vehicle, the first data center system configured to receive the first communication associated with the first vehicle that is authenticated and to provide data to the first vehicle, wherein the first point-of-presence interface is separate from the first data center system.
| 9. The vehicle telecommunications network system of claim 8, further comprising:
a second point-of-presence interface configured to obtain a second communication associated with a second vehicle and to route the second communication associated with the second vehicle, wherein the second point-of-presence interface is remote from the second vehicle and the first vehicle;
a second security system configured to authenticate the second vehicle, wherein the second point-of-presence interface is separate from the second security system, wherein the second security system is remote from the second vehicle and the first vehicle; and
a second data center system that is remote from the first vehicle and the second vehicle, the second data center system configured to receive the second communication associated with the second autonomous vehicle that is authenticated and to provide data to the second autonomous vehicle, wherein the second point-of-presence interface is separate from the second data center system, wherein the first point-of-presence interface, the first security system, and the first data center are remote from the second vehicle.
| 10. The vehicle telecommunications network system of claim 9, wherein the first point-of-presence interface, the first security system, and the first data center system are associated with a first geographic region, and
wherein the second point-of-presence interface, the second security system, and the second data center system are associated with a second geographic region that is different from the first geographic region.
| 11. The vehicle telecommunications network system of claim 10, wherein the first vehicle is located within the first geographic region, and wherein the second vehicle is located within the second geographic region.
| 12. The vehicle telecommunications network system of claim 9, wherein the first point-of-presence interface is further configured to obtain the second communication associated with the second vehicle when the second point-of-presence interface is unavailable, and wherein the second point-of-presence interface is further configured to obtain the first communication associated with the first vehicle when the first point-of-presence interface is unavailable.
| 13. The vehicle telecommunications network system of claim 8, wherein the first point-of-presence interface is further configured to route the first communication associated with the first vehicle to vehicle assistance system.
| 14. The vehicle telecommunications network system of claim 8, wherein the first point-of-presence interface is configured to allow the first vehicle to access a public internet network.
| 15. The vehicle telecommunications network system of claim 8, wherein the first point-of-presence interface is physically separate from the first security system and the first data center system.
| 16. The vehicle telecommunications network system of claim 8, wherein the first point-of-presence interface is logically separate from the first security system and the first data center system.
| 17. The vehicle telecommunications network system of claim 8, wherein the vehicle telecommunications network system does not utilize internet protocol security.
| 18. A computer-implemented method for facilitating communication with vehicles comprising:
obtaining, by a point-of-presence interface, a communication associated with vehicle, wherein the point-of presence interface is remote from the vehicle;
determining, by the point-of presence interface, a recipient computing system that is remote from the vehicle based at least in part on the communication from the vehicle, wherein the recipient computing system is separate from the point-of presence interface;
facilitating, by the point-of-presence interface, an authentication of the vehicle by a security system, wherein the point-of-presence interface is separate from the security system;
providing, by the point-of-presence interface, the communication associated with the vehicle that is authenticated to the recipient computing system.
| 19. The computer-implemented method of claim 18, further comprising:
obtaining, by the point-of-presence interface from the recipient computing system, data in response to the communication; and
providing, by the point-of-presence interface, the data to the vehicle.
| 20. The computer-implemented method of claim 18, wherein the point-of-presence interface is a first point-of-presence interface associated with a first geographic region, wherein the vehicle is located in a second geographic region that is different from the first geographic region, wherein a second point-of-presence interface associated with the second geographic region is unavailable, and wherein obtaining the communication associated with the vehicle comprises:
obtaining, by the point-of-presence interface, a communication from the vehicle located in the second geographic region based at least in part on the second point-of-presence interface being unavailable. | The vehicle telecommunication network system (100) has a first point-of-presence interface and a second point-of-presence interface that obtains first and second communication associated with first and second vehicles and routes the first and second communication. A first security system and a second security system authenticates the first and second vehicles. The second security system is remote from the second vehicle and the first vehicle. A first data center system is remote from the first vehicle. A second data center system that is remote from the first vehicle and the second vehicle. The first and second data center systems receive the first and second communication associated with the first and second vehicles that are authenticated and to provide data to the first and second vehicles. The first point-of-presence interface, the first security system and the first data center are remote from the second vehicle. An INDEPENDENT CLAIM is included for a computer-implemented method for facilitating communication with vehicles. Vehicle telecommunication network system for communicating data to and from vehicle e.g. autonomous vehicle. The autonomous vehicle telecommunication network system provides an improved infrastructure to facilitate communication between an autonomous vehicle and computing system that are remote from the vehicle. The system provides redundant regional telecommunication systems that decrease transmission latency, and offer better reliability and scalability. The drawing shows a schematic view of an autonomous vehicle system. 100Vehicle telecommunication network system105Vehicle computing system110Vehicle115Operation computing system120User |
Please summarize the input | vehicle management systemThe invention claims a system, method and vehicle for the vehicle to stop the service. In one example embodiment, a method includes: data one or more of airborne unmanned vehicle on the autonomous computing device obtains one or more parameters indicating tool associated with the autonomous unmanned vehicle. the autonomous unmanned vehicle providing the vehicle service to one or more users to the vehicle service is configured. the method comprises: by the computing device based at least in part on traffic tool associated with the autonomous driving of the one or more parameters determining there is a fault associated with the autonomous driving vehicle. the method comprising: one or more motion the presence is determined by the computing device based at least in part on the failure of the execution by the autonomous driving of the vehicle. the method comprises the following steps: performing the action by the computing device at least partially in one or more such that the autonomous driving vehicle stopping service based on the failure.|1. A computer-implemented method for enabling a vehicle to stop the service implementation, which comprises: data one or more of airborne unmanned vehicle on the autonomous computing device obtains one or more parameters indicating tool associated with the autonomous unmanned vehicle, wherein the autonomous driving vehicle providing the vehicle service to one or more users to the vehicle service is configured, by the one or more computing devices based at least in part on traffic tool associated with the autonomous driving of the one or more parameters determining there is a fault associated with the autonomous driving vehicle; the existence determination by the one or more computing devices based at least in part on the failure of the unmanned vehicle by the autonomous execution of one or more actions, and by the one or more computing devices at least partially executing the action in one or more such that the autonomous driving vehicle stopping service based on the failure.
| 2. The method implemented by the computer according to claim 1, wherein the autonomous driving vehicle and indicating the autonomous driving vehicle can be used or not available for state provides the vehicle service is associated. wherein at least one of the actions comprises the state adjusting tool associated with the autonomous unmanned vehicle, and wherein the one or more computing devices executing the action in one or more such that the vehicle stops service comprising: by the one or more computing devices to adjust and the autonomous unmanned vehicle associated with the state to indicate that the autonomous driving vehicle is not available for providing the vehicle service.
| 3. The method implemented by the computer according to claim 2, wherein the one or more computing devices adjusting traffic tool associated with the autonomous driving of the state to indicate that the autonomous driving vehicle is not available for providing the traffic with the service comprises: indicating the autonomous driving vehicle is not available for data provides the vehicle service provided to away from the autonomous driving vehicle to one or more remote computing devices by the one or more computing devices.
| 4. The method implemented by the computer according to claim 3, wherein the one or more computing devices to perform one or more of the action based at least in part on the fault to make the autonomous unmanned vehicle stops service comprises removing the autonomous unmanned vehicle from the service queue associated with the vehicle by the one or more computing devices.
| 5. The method implemented by the computer according to claim 2, wherein when the state associated with the vehicle indicates that the autonomous driving vehicle can be used to provide the vehicle service, the autonomous unmanned vehicle does not accept a request for the service of the vehicle.
| 6. The method implemented by the computer according to claim 1, wherein the one or more computing devices determining the one or more actions comprising: by the one or more computing devices based at least in part on one or more characteristics of the fault to determine the severity of the fault; by the one or more computing devices at least in part determines the operating state of the autonomous unmanned vehicle based on the severity of the fault, wherein said operating state indicates the autonomous driving vehicle is in condition to provide the service of the vehicle; the operation state and by the one or more computing devices based at least in part on the autonomous unmanned vehicle determining the one or more actions.
| 7. The method executed by computer according to claim 6, wherein said operating state indicates the autonomous unmanned vehicle is in condition to the self-drive vehicle of one or more current users providing the service of the vehicle, and wherein the autonomous driving vehicle is configured to stop the service on the vehicle before finishing current provided by the user to the one or more of the vehicle service.
| 8. The method implemented by the computer according to claim 6, wherein said operating state indicates the vehicle is not in a condition to provide the service of the vehicle.
| 9. The method implemented by the computer according to claim 8, wherein the autonomous driving vehicle is configured to stop the one or more current user providing the vehicle service.
| 10. The method implemented by the computer according to claim 1, wherein at least one of the actions includes a travel to and reaches the maintenance position, and further comprising: by the one or more computing devices the one or more control command signal provided to the autonomous driving one or more system vehicle is airborne to cause the autonomous unmanned vehicle travel to and reaches the maintenance position wherein the method.
| 11. The method implemented by the computer according to claim 10, wherein the one or more computing devices based at least in part on traffic tool associated with the autonomous driving of the one or more parameters determined from the presence of the autonomous unmanned vehicle associated with said fault comprises at least one of threshold value to the parameter by the one or more computing device is associated with the autonomous driving of the vehicle.
| 12. The method implemented by the computer according to claim 11, further comprising: at least one geographic position by the one or more computing devices to obtain data indicative of the maintenance position, wherein the data indicative of the maintenance position indicative of the maintenance position; travel route of the geographical position by the one or more computing devices based at least in part on the determined the maintenance position to the maintenance position, data of one or more travel factors by the one or more computing devices obtaining an indication associated with the travel route; and by the one or more computing devices at least partially determining the threshold based on the travel route and the one or more travel factors necessary degree, wherein said threshold value indicates that the autonomous driving vehicle to traverse the travel route and reaches the maintenance position needed by said geographic location of said at least one parameter.
| 13. A computing system for the vehicle to stop the service, the system comprising: one or more memory devices on one or more processor for autonomous driving vehicle is airborne, and the autonomous unmanned vehicle airborne, the one or more memory devices storing instructions, the instructions when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising: data obtaining an indication with the autonomous unmanned vehicle associated with one or more parameters; wherein the autonomous driving vehicle providing vehicle in one or more user configured service to the vehicle service. and wherein the autonomous unmanned vehicle and indicating that the autonomous driving vehicle can be used or not available for state provides the vehicle service is associated, based at least in part on the presence and the autonomous driving fault vehicle associated with comparing the determined threshold value and one or more of the autonomous unmanned vehicle associated with said one or more parameters; the existence determination based at least in part on the failure of the unmanned vehicle by the autonomous execution of one or more actions, wherein the action in one or more comprises adjusting the state associated with the autonomous unmanned vehicle, and based at least in part on the fault adjustment with the autonomous driving vehicle associated with the state to indicate that the autonomous driving vehicle is not available to service the traffic tool.
| 14. The computing system according to claim 13, wherein the adjusting and the autonomous driving vehicle associated with the state to indicate that the autonomous driving vehicle is not available for providing the vehicle service comprises: indicating the autonomous driving vehicle will data service queue from service associated with said traffic tool for providing removable away from the autonomous driving vehicle to one or more remote computing devices.
| 15. The computing system according to claim 13, wherein at least one of the actions comprises travel to and reaches the maintenance position, and further comprising: the one or more control command signal provided to the autonomous driving one or more system vehicle is airborne to cause the autonomous unmanned vehicle travel to and reaches the maintenance position wherein the operation.
| 16. The computing system according to claim 15, wherein at least one of the parameters indicates that the autonomous driving amount of available data storage device on the vehicle onboard.
| 17. The computing system according to claim 16, wherein at least one threshold indicating the threshold value amount of available data storage device, and wherein the threshold value for data storage device is based at least in part on the autonomous driving vehicle travel to and onto the data storage device an amount required for the maintenance position.
| 18. An autonomous unmanned vehicle, comprising: one or more system of the autonomous unmanned vehicle is airborne, one or more processors of the autonomous unmanned vehicle is airborne, and one or a plurality of memory devices of the autonomous unmanned vehicle is airborne. the one or more memory devices storing instructions, the instructions when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising: data obtaining an indication with the autonomous unmanned vehicle associated with one or more parameters; one or more of the system wherein at least a portion of the data on airborne by the autonomous driving vehicle in the claims, wherein the autonomous driving vehicle included in a plurality of vehicles associated with a service provider. and wherein the autonomous driving vehicle to vehicle provides the service provider to one or more users of the service is configured, based at least in part on the autonomous driving vehicle associated with the one or more parameters determining the presence and the associated autonomous driving the fault vehicle; and at least partially performing one or more actions to make the vehicle stop service based on the failure, so that the autonomous driving vehicle is not available for providing the vehicle service.
| 19. The computing device of autonomous driving vehicle according to claim 18, wherein associated with said service provider will not in the autonomous driving vehicle when stopping service to the autonomous driving vehicle provides one or more request for the service of the vehicle.
| 20. The autonomous driving vehicle according to claim 18, wherein the system of the autonomous unmanned vehicle is airborne in the one or more comprises one or more image capture device, the image capture data device is configured to obtain the operation to be used for the autonomous driving vehicle in the autonomous mode, and wherein the fault and storing the image data are associated.
| 21. The method implemented by computer of the movement of a stopped vehicle, comprising: data one or more of airborne unmanned vehicle on the autonomous computing device obtains one or more parameters indicating tool associated with the autonomous unmanned vehicle, wherein the autonomous driving vehicle providing vehicle services to one or more users to the vehicle service is configured, by the one or more computing devices based at least in part on traffic tool associated with the autonomous driving of the one or more parameters determining there is a fault associated with the autonomous driving vehicle; the existence determination by the one or more computing devices based at least in part on the failure of the unmanned vehicle by the autonomous execution of one or more actions, wherein at least one of the actions comprises stopping the moving of the autonomous unmanned vehicle; and by the one or more computing devices the one or more control command signal provided to the autonomous driving one or more airborne in the system on the vehicle to facilitate stopping the autonomous driving vehicle of the motion in response to the presence of the fault.
| 22. The method implemented by the computer according to claim 21, wherein, in order to facilitate the movement of stopping the vehicle, the airborne system in one or more of the at least partially based on determined following at least one of the stopping position of the autonomous unmanned vehicle, the deceleration rate and the deceleration time delay, and wherein in order to facilitate stopping the autonomous driving the motion of the vehicle, with the unmanned vehicle of the fault self-associated and one or more travelling condition the autonomous driving the one or more system vehicle is airborne to cause the autonomous driving vehicle deceleration.
| 23. The method implemented by the computer according to claim 22, wherein the stopping position is located in the current running of the autonomous unmanned vehicle lane.
| 24. The method implemented by the computer according to claim 22, wherein the stop position outside of the autonomous driving vehicle of the current driving lane.
| 25. The method implemented by the computer according to claim 21, wherein the one or more computing device determines that one or more actions of one or more system is airborne by the autonomous driving vehicle comprises the execution of: by the one or more computing devices at least partially determining the severity of the fault based on one or more characteristics of the fault, and by the one or more computing devices at least partially determining the action of one or more based on the severity of the fault.
| 26. The method implemented by the computer according to claim 25, wherein the deceleration rate of the autonomous unmanned vehicle is based at least in part on the severity associated with the fault.
| 27. The method implemented by the computer according to claim 21, wherein at least one of the actions comprises passing the vehicle stops service, further comprising: by the one or more computing devices executing the action in one or more such that the autonomous driving vehicle stop service so that the autonomous driving vehicle is not available for providing the vehicle service wherein the method.
| 28. The method implemented by the computer according to claim 21, wherein the one or more action comprises one or more of the fault notification in the user, and further comprising: providing data of the presence indicative of the failure via one or more display device by the one or more computing devices for display wherein the method.
| 29. The method implemented by the computer according to claim 21, wherein the action of one or more request containing different vehicle provides the vehicle to the one or more user service, and further comprising: indicating the data request so that the different vehicle provides the vehicle to the one or more user service provided to one or more remote computing devices associated with a service provider by the one or more computing devices wherein the method.
| 30. data The method implemented by the computer according to claim 21, wherein the user of said one or more parameters comprises an indication associated with the fault of the input.
| 31. A computing system for stopping the movement of the traffic tool, comprising: an autonomous driving one or more processor is onboard of the vehicle, and one or more memory devices of the autonomous unmanned vehicle is airborne. the one or more memory devices storing instructions, the instructions when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising: data obtaining an indication with the autonomous unmanned vehicle associated with one or more parameters; wherein the autonomous driving vehicle providing the vehicle service to one or more users to the vehicle service is configured; based at least in part on traffic tool associated with the autonomous driving of the one or more parameters determining there is a fault associated with the autonomous driving of the vehicle, determined at least in part by the autonomous unmanned vehicle to perform one or more actions, wherein at least one of the actions comprises stopping the moving of the autonomous unmanned vehicle, based on said failure and the one or more control command signal provides one or more of the system on the autonomous driving of the vehicle onboard to facilitate stopping the autonomous driving vehicle of the motion in response to the presence of the fault.
| 32. The computing system according to claim 31, wherein at least partially determining the one or more actions executed by the autonomous driving vehicle comprises determining a severity associated with the fault, and at least partially based on information associated with the fault of the severity determining the one or more actions based on the fault.
| 33. The computing system according to claim 31, wherein the operations further comprise: the indication of the traffic tool to request of data maintenance is provided away from the autonomous driving vehicle to one or more remote computing devices.
| 34. data The computing system according to claim 31, wherein the one or more parameters comprises an indication associated with the fault of the input, and wherein said operation further comprises: the indication provided to far away from the autonomous driving vehicle of one or more remote computing device pair confirmation data of the request of the presence of the fault.
| 35. The system according to claim 31, wherein the operations further comprise: via one or more display devices provide data indicating one or more characteristics of the fault for display.
| 36. An autonomous unmanned vehicle, comprising: one or more system of the autonomous unmanned vehicle is airborne, one or more processors of the autonomous unmanned vehicle is airborne, and one or a plurality of memory devices of the autonomous unmanned vehicle is airborne. the one or more memory devices storing instructions, the instructions when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising: data obtaining an indication with the autonomous unmanned vehicle associated with one or more parameters; one or more of said data indicates that said one or more parameters wherein at least a portion of the airborne on via the autonomous unmanned vehicle for providing in said system, and wherein the autonomous driving vehicle providing the vehicle service to one or more users to the vehicle service is configured, based at least in part on traffic tool associated with the autonomous driving of the one or more parameters determining the presence and the associated autonomous driving the fault vehicle, determined at least in part by the autonomous unmanned vehicle to perform one or more actions based on the fault; and the one or more control command signal provides one or more of the system on the autonomous airborne unmanned vehicle to perform one or more of the actions is to facilitate stopping movement of the autonomous driving vehicle in response to the presence of the fault.
| 37. The autonomous driving vehicle according to claim 36, wherein the one or more configured in the airborne system based at least in part on the fault determining a stop position of the vehicle.
| 38. The autonomous driving vehicle according to claim 36, wherein the parameter in the one or more of user input indicating the request input by the user indicates to stop the vehicle.
| 39. The autonomous driving vehicle according to claim 38, which further comprises one or a plurality of display apparatus, and further comprising: via the one or more display devices provide indication data of the request of stopping the vehicle for display wherein the operation.
| 40. The autonomous driving vehicle according to claim 39, further comprising one or more of an audio output device, and further comprising: the indication request so that a human operator with at least one of the autonomous unmanned vehicle of the current user communication of data via the display device and the audio output device is supplied to from the autonomous driving vehicle to one or more remote computing device wherein the operation. | The method involves obtaining the data (604,608) that indicates parameters associated with the autonomous vehicle. An existence of a fault associated with the autonomous vehicle is determined. The service request is rejected that is associated with the vehicle service. The level of severity of the fault is determined by the computing devices based on the characteristics of the fault. An operational state of the autonomous vehicle is determined based on the level of severity of the fault. The autonomous vehicle is controlled to travel to and arrive at a maintenance location. INDEPENDENT CLAIMS are included for the following:a computing system for taking a vehicle out-of-service; andan autonomous vehicle has processors and a memory device for storing instructions to obtain data of parameters. Method for taking a vehicle, particularly autonomous vehicle (Claimed) out-of-service. The potential latency issues are avoided that arises from remote computing device to process vehicle fault diagnosis request. The computationally efficient approach is provided to address vehicle faults, while saving computational resources. The computational response time is reduced for addressing the determined faults. The drawing shows a schematic view of a user interface. 600User interface602Display device604,608Data606User interface element |
Please summarize the input | Systems and Methods for Mitigating Vehicle Pose Error Across an Aggregated Feature MapSystems and methods for improved vehicle-to-vehicle communications are provided. A system can obtain sensor data depicting its surrounding environment and input the sensor data (or processed sensor data) to a machine-learned model to perceive its surrounding environment based on its location within the environment. The machine-learned model can generate an intermediate environmental representation that encodes features within the surrounding environment. The system can receive a number of different intermediate environmental representations and corresponding locations from various other systems, aggregate the representations based on the corresponding locations, and perceive its surrounding environment based on the aggregated representations. The system can determine relative poses between the each of the systems and an absolute pose for each system based on the representations. Each representation can be aggregated based on the relative or absolute poses of each system and weighted according to an estimated accuracy of the location corresponding to the representation.What is claimed is:
| 1. A computer-implemented method, the method comprising:
obtaining, by a computing system comprising one or more computing devices onboard an autonomous vehicle, sensor data associated with an environment of a first autonomous vehicle;
obtaining, by the computing system, estimated location data indicative of a first estimated pose of the first autonomous vehicle;
determining, by the computing system, a first intermediate environmental representation of at least a first portion of the environment of the first autonomous vehicle based, at least in part, on the sensor data;
obtaining, by the computing system, a first message from a second autonomous vehicle, wherein the first message comprises a second intermediate environmental representation of at least a second portion of the environment of the first autonomous vehicle and second estimated location data indicative of a second estimated pose of the second autonomous vehicle;
determining, by the computing system, a first relative pose between the first autonomous vehicle and the second autonomous vehicle based, at least in part, on the intermediate environmental representation and the second intermediate environmental representation;
generating, by the computing system, an updated intermediate environmental representation based, at least in part, on the first intermediate environmental representation, the second intermediate environmental representation, and the first relative pose; and
generating, by the computing system, an autonomy output for the first autonomous vehicle based, at least in part, on the updated intermediate environmental representation.
| 2. The computer-implemented method of claim 1, wherein the first intermediate environmental representation is a first feature map encoded with a first plurality of encoded features representative of the first portion of the environment; and
wherein the second intermediate environmental representation is a second feature map encoded with a second plurality of encoded features representative of the second portion of the environment.
| 3. The computer-implemented method of claim 2, wherein determining the relative pose between the first autonomous vehicle and the second autonomous vehicle based, at least in part, on the first intermediate environmental representation and the second intermediate environmental representation, comprises:
generating, by the computing system, an intermediate environmental representation pair by concatenating the first plurality of encoded features and second plurality of encoded features along a features dimension;
inputting, by the computing system, the intermediate environmental representation pair to a machine-learned relative pose regression model configured to output the relative pose based, at least in part, on the intermediate environmental representation pair.
| 4. The computer-implemented method of claim 1, wherein method further comprises:
obtaining, by the computing system, a second message from a third autonomous vehicle, wherein the second message comprises a third intermediate environmental representation of at least a third portion of the environment of the first autonomous vehicle and third estimated location data indicative of a third estimated pose of the third autonomous vehicle;
determining, by the computing system, a second relative pose between the first autonomous vehicle and the third autonomous vehicle based, at least in part, on the first intermediate environmental representation and the third intermediate environmental representation; and
generating, by the computing system, the updated intermediate environmental representation based, at least in part, on the third intermediate environmental representation and the second relative pose between the first autonomous vehicle and the third autonomous vehicle.
| 5. The computer-implemented method of claim 4, wherein the first relative pose is indicative of a first displacement between the first autonomous vehicle and the second autonomous vehicle, wherein the second relative pose is indicative of a second displacement between the first autonomous vehicle and the third autonomous vehicle, and wherein the method further comprises:
determining, by the computing system, a third relative pose between the second autonomous vehicle and the third autonomous vehicle based, at least in part, on the second intermediate environmental representation and the third intermediate environmental representation, the third relative pose indicative of a third displacement between the second autonomous vehicle and the third autonomous vehicle; and
generating, by the computing system, the updated intermediate environmental representation based, at least in part, on the third relative pose.
| 6. The computer-implemented method of claim 5, wherein the first estimated pose is indicative of one or more first estimated spatial coordinates and a first estimated orientation for the first autonomous vehicle, the second estimated pose is indicative of one or more second estimated spatial coordinates and a second estimated orientation for the second autonomous vehicle, and the third estimated pose is indicative of one or more third estimated spatial coordinates and a third estimated orientation for the third autonomous vehicle.
| 7. The computer-implemented method of claim 5, further comprising:
determining, by the computing system, a first absolute pose for the first autonomous vehicle based, at least in part, on the first relative pose, the second relative pose, and the third relative pose;
determining, by the computing system, a second absolute pose for the second autonomous vehicle based, at least in part, on the first relative pose, the second relative pose, and the third relative pose;
determining, by the computing system, a third absolute pose for the third autonomous vehicle based, at least in part, on the first relative pose, the second relative pose, and the third relative pose; and
generating, by the computing system, the updated intermediate environmental representation based, at least in part, on the first absolute pose, the second absolute pose, and the third absolute pose.
| 8. The computer-implemented method of claim 7, wherein the first absolute pose is indicative of one or more first updated spatial coordinates and a first updated orientation for the first autonomous vehicle, the second absolute pose is indicative of one or more second updated spatial coordinates and a second updated orientation for the second autonomous vehicle, and the third absolute pose is indicative of one or more third updated spatial coordinates and a third updated orientation for the third autonomous vehicle.
| 9. The computer-implemented method of claim 8, wherein generating the updated intermediate environmental representation comprises:
generating, by the computing system using a machine-learned aggregation model, a second transformed intermediate environmental representation by transforming the second intermediate environmental representation based, at least in part, on the one or more second updated spatial coordinates and a second updated orientation for the second autonomous vehicle;
generating, by the computing system using the machine-learned aggregation model, a third transformed intermediate environmental representation by transforming the third intermediate environmental representation based, at least in part, on the one or more third updated spatial coordinates and the third updated orientation for the second autonomous vehicle; and
generating, by the computing system using the machine-learned aggregation model, the updated intermediate environmental representation based, at least in part, on the first intermediate environmental representation, the second transformed intermediate environmental representation, and the third transformed intermediate environmental representation.
| 10. The computer-implemented method of claim 9, wherein generating the updated intermediate environmental representation further comprises:
assigning, by the computing system using the machine-learned aggregation model, a second weight to the second transformed intermediate environmental representation;
assigning, by the computing system using the machine-learned aggregation model, a third weight to the third transformed intermediate environmental representation; and
generating, by the computing system using the machine-learned aggregation model, the updated intermediate environmental representation based, at least in part, on the second weight and the third weight.
| 11. A computing system comprising:
one or more processors; and
one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the system to perform operations, the operations comprising:
obtaining sensor data associated with an environment of a first autonomous vehicle;
obtaining estimated location data indicative of a first estimated pose of the first autonomous vehicle;
determining, via a first portion of a machine-learned detection and prediction model, a first intermediate environmental representation of at least a first portion of the environment of the first autonomous vehicle based, at least in part, on the sensor data;
obtaining a first message from a second autonomous vehicle, wherein the first message comprises a second intermediate environmental representation of at least a second portion of the environment of the first autonomous vehicle and second estimated location data indicative of a second estimated pose of the second autonomous vehicle;
determining, via a machine-learned regression model, a first relative pose between the first autonomous vehicle and the second autonomous vehicle based, at least in part, on the intermediate environmental representation and the second intermediate environmental representation; and
generating, via a second portion of the machine-learned detection and prediction model, an autonomy output for the first autonomous vehicle based, at least in part, on the first intermediate environmental representation, the second intermediate environmental representation, and the first relative pose.
| 12. The computing system of claim 11, wherein the operations further comprise:
generating, via the second portion of the machine-learned detection and prediction model, an updated intermediate environmental representation based, at least in part, on the first intermediate environmental representation, the second intermediate environmental representation, and the first relative pose; and
generating, via the second portion of the machine-learned detection and prediction model, the autonomy output for the first autonomous vehicle based, at least in part, on the updated intermediate environmental representation.
| 13. The computing system of claim 12, wherein generating the updated intermediate environmental representation comprises:
obtaining a second message from a third autonomous vehicle, wherein the second message comprises a third intermediate environmental representation of at least a third portion of the environment of the first autonomous vehicle and third estimated location data indicative of a third estimated pose of the third autonomous vehicle;
determining, via the machine-learned regression model, a second relative pose between the first autonomous vehicle and the third autonomous vehicle based, at least in part, on the first intermediate environmental representation and the third intermediate environmental representation; and
generating, via the second portion of the machine-learned detection and prediction model, the updated intermediate environmental representation based, at least in part, on the third intermediate environmental representation and the second relative pose.
| 14. The computing system of claim 13, wherein the second portion of the machine-learned detection and prediction model comprises a machine-learned attention model configured to weigh a plurality of intermediate environmental representations.
| 15. The computing system of claim 14, wherein generating the updated intermediate environmental representation comprises:
determining, via the machine-learned attention model, a first weight for the second intermediate environmental representation;
determining, via the machine-learned attention model, a second weight for the third intermediate environmental representation; and
generating, via the second portion of the machine-learned detection and prediction model, the updated intermediate environmental representation based, at least in part, on the first weight and the second weight.
| 16. The computing system of claim 15, wherein the first weight is indicative of a first predicted accuracy of the second estimated pose, and wherein the second weight is indicative of a second predicted accuracy of the third estimated pose.
| 17. The computing system of claim 11, wherein the machine-learned detection and prediction model and the machine-learned regression model are trained end-to-end via backpropagation.
| 18. An autonomous vehicle comprising:
one or more sensors;
one or more processors; and
one or more tangible, non-transitory, computer readable media that collectively store instructions that when executed by the one or more processors cause the one or more processors to perform operations, the operations comprising:
obtaining, via the one or more sensors, sensor data associated with a surrounding environment of the autonomous vehicle;
determining a first intermediate environmental representation of at least a first portion of the surrounding environment of the autonomous vehicle based, at least in part, on the sensor data;
obtaining a plurality of messages from a plurality of respective autonomous vehicles, wherein each respective message of the plurality of messages comprises a respective intermediate environmental representation of at least another portion of the surrounding environment associated with a respective autonomous vehicle of the plurality of respective autonomous vehicles;
determining a plurality of relative poses based, at least in part, on the first intermediate environmental representation and the respective intermediate environmental representation, the plurality of relative poses comprising a respective relative pose between the autonomous vehicle and each of the plurality of respective autonomous vehicles;
determining a plurality of absolute poses based, at least in part, on the plurality of relative poses, the plurality of absolute poses comprising a respective absolute pose for the autonomous vehicle and each of the plurality of respective autonomous vehicles;
generating an updated intermediate environmental representation based, at least in part, on the first intermediate environmental representation, the respective intermediate environmental representation, and at least one of the plurality of absolute poses; and
generating an autonomy output for the autonomous vehicle based, at least in part, on the updated intermediate environmental representation.
| 19. The autonomous vehicle of claim 18, wherein the sensor data comprises three-dimensional data representative of the surrounding environment of the autonomous vehicle.
| 20. The autonomous vehicle of claim 18, wherein the autonomy output comprises one or more bounding boxes indicative of one or more objects within the surrounding environment of the autonomous vehicle. | The method involves obtaining (702) sensor data associated with an environment of an autonomous vehicle. The estimated location data indicative of an estimated pose of the autonomous vehicle is obtained (704). An intermediate environmental representation of a portion of the environment of the vehicle is determined (706) based on the sensor data. A message is obtained (708) from another autonomous vehicle, where the message comprises another intermediate representation. A relative pose between the autonomous vehicles is determined (712). An updated intermediate representation is generated (716). An autonomy output is generated (718) for the first autonomous vehicle based on updated representation by a computing system. The intermediate representations are feature maps encoded with encoded features representative of the portions of environment. INDEPENDENT CLAIMS are included for: (1) a computing system comprises multiple processors; (2) an autonomous vehicle comprises multiple sensors. Method for mitigating vehicle pose errors across an aggregated feature map used for performing autonomous vehicle operations by a computing system. Uses include but are not limited to a laptop computer, a tablet computer, an ultrabook, a smartphone, a Personal digital assistant (PDA), and a wearable device. The autonomous vehicle technology improves the ability of an autonomous vehicle to effectively provide vehicle services to others and support the various members of the community in which the autonomous vehicle is operating, including persons with reduced mobility and/or persons that are underserved by other transportation options. The drawing shows a flow chart of the method. 702Obtaining sensor data associated with an environment of a first autonomous vehicle 704Obtaining estimated location data indicative of an estimated pose of the autonomous vehicle 706Determining intermediate environmental representation of a portion of the environment of the vehicle 708Obtaining message from another autonomous vehicle 712Determining relative pose between the autonomous vehicles 716Generating updated intermediate representation 718Generating autonomy output for first autonomous vehicle |
Please summarize the input | Systems and methods for vehicle-to-vehicle communications for improved autonomous vehicle operationsSystems and methods for vehicle-to-vehicle communications are provided. An example computer-implemented method includes obtaining from a first autonomous vehicle, by a computing system onboard a second autonomous vehicle, a first compressed intermediate environmental representation. The first compressed intermediate environmental representation is indicative of at least a portion of an environment of the second autonomous vehicle and is based at least in part on sensor data acquired by the first autonomous vehicle at a first time. The method includes generating, by the computing system, a first decompressed intermediate environmental representation by decompressing the first compressed intermediate environmental representation. The method includes determining, by the computing system, a first time-corrected intermediate environmental representation based at least in part on the first decompressed intermediate environmental representation. The first time-corrected intermediate environmental representation corresponds to a second time associated with the second autonomous vehicle.What is claimed is:
| 1. A computer-implemented method for vehicle-to-vehicle communications, the method comprising:
obtaining from a first autonomous vehicle, by a computing system comprising one or more computing devices onboard a second autonomous vehicle, a first compressed intermediate environmental representation,
wherein the first compressed intermediate environmental representation is indicative of at least a portion of an environment of the second autonomous vehicle and is based at least in part on sensor data acquired by the first autonomous vehicle at a first time;
generating, by the computing system, a first decompressed intermediate environmental representation by decompressing the first compressed intermediate environmental representation; and
determining, by the computing system, a first time-corrected intermediate environmental representation based at least in part on the first decompressed intermediate environmental representation, wherein the first time-corrected intermediate environmental representation is indicative of a time difference between the first time and a second time at which the second autonomous vehicle obtains sensor data of the environment to account for the time difference.
| 2. The computer-implemented method of claim 1, wherein determining the first time-corrected intermediate environmental representation comprises:
generating, by the computing system, the first time-corrected intermediate environmental representation based at least in part on a machine-learned time correction model,
wherein the machine-learned time correction model is configured to adjust the first decompressed intermediate environmental representation to account for the time difference between the first time and the second time.
| 3. The computer-implemented method of claim 2, wherein the machine-learned time correction model comprises a neural network.
| 4. The computer-implemented method of claim 1, wherein the first time is associated with a sensor timestamp of the first autonomous vehicle.
| 5. The computer-implemented method of claim 1, wherein the second time is indicative of a time at which the second autonomous vehicle obtains the sensor data through one or more sensors of the second autonomous vehicle.
| 6. The computer-implemented method of claim 1, further comprising:
determining, by the computing system, an updated intermediate environmental representation based at least in part on the first time-corrected intermediate environmental representation.
| 7. The computer-implemented method of claim 6, further comprising:
determining, by the computing system, an autonomy output based at least in part on the updated intermediate environmental representation.
| 8. The computer-implemented method of claim 7, further comprising:
generating, by the computing system, a motion plan for the second autonomous vehicle based at least in part on the autonomy output.
| 9. The computer-implemented method of claim 8, further comprising:
initiating, by the computing system, a motion control of the second autonomous vehicle based at least in part on the motion plan.
| 10. A computing system comprising:
a machine-learned time correction model configured to compensate for time differences between a plurality of times;
one or more processors; and
one or more tangible, non-transitory, computer readable media that collectively store instructions that when executed by the one or more processors cause the computing system to perform operations, the operations comprising:
obtaining a first compressed intermediate environmental representation from a first autonomous vehicle, wherein the first compressed intermediate environmental representation is based at least in part on sensor data acquired by the first autonomous vehicle at a first time;
generating a first decompressed intermediate environmental representation by decompressing the first compressed intermediate environmental representation;
determining, using the machine-learned time correction model, a first time-corrected intermediate environmental representation indicative of a time difference between the first time and a second time based at least in part on the first decompressed intermediate environmental representation, wherein the first time-corrected intermediate environmental representation is adjusted based at least in part on the time difference associated with a second autonomous vehicle; and
generating an updated intermediate environmental representation based at least in part on the first time-corrected intermediate environmental representation.
| 11. The computing system of claim 10, wherein the first time is associated with a sensor timestamp of the first autonomous vehicle.
| 12. The computing system of claim 10, wherein the first time and the second time are based on global positioning system data.
| 13. The computing system of claim 10, wherein generating the updated intermediate environmental representation based at least in part on the first time-corrected intermediate environmental representation comprises:
aggregating, using a machine-learned aggregation model, the first time-corrected intermediate environmental representation and a second intermediate environmental representation generated by the second autonomous vehicle.
| 14. The computing system of claim 13, wherein the machine-learned aggregation model is a graph neural network comprising a plurality of nodes.
| 15. The computing system of claim 14, wherein the machine-learned aggregation model is configured to initialize a node state of at least one node based at least in part on the time difference between the first time and the second time.
| 16. The computing system of claim 10, wherein the operations further comprise:
generating a motion plan based at least in part on the updated intermediate environmental representation; and
initiating a motion control of the second autonomous vehicle based at least in part on the motion plan.
| 17. An autonomous vehicle comprising:
one or more processors; and
one or more tangible, non-transitory, computer readable media that collectively store instructions that when executed by the one or more processors cause the one or more processors to perform operations, the operations comprising:
obtaining a first compressed intermediate environmental representation from another autonomous vehicle, wherein the first compressed intermediate environmental representation is based at least in part on sensor data acquired by the other autonomous vehicle at a first time;
generating a first decompressed intermediate environmental representation by decompressing the first compressed intermediate environmental representation;
determining a first time-corrected intermediate environmental representation based at least in part on the first decompressed intermediate environmental representation and one or more machine-learned models, wherein the first time-corrected intermediate environmental representation is indicative of a time difference between the first time and a second time and, wherein the first time-corrected intermediate environmental representation is adjusted based at least in part on the time difference associated with the autonomous vehicle; and
performing one or more autonomy operations of the autonomous vehicle based at least in part on the first time-corrected intermediate environmental representation.
| 18. The autonomous vehicle of claim 17, wherein performing the one or more autonomy operations of the autonomous vehicle based at least in part on the first time-corrected intermediate environmental representation comprises:
generating an autonomy output based at least in part on the first time-corrected intermediate environmental representation.
| 19. The autonomous vehicle of claim 18, wherein generating the autonomy output based at least in part on the first time-corrected intermediate environmental representation comprises:
generating an updated intermediate environmental representation based at least in part on the first time-corrected intermediate environmental representation; and
generating the autonomy output based at least in part on the updated intermediate environmental representation.
| 20. The autonomous vehicle of claim 18, wherein the autonomy output is indicative of perception data and prediction data associated with the autonomous vehicle. | The computer-based method involves obtaining from a first autonomous vehicle and a first compressed intermediate environmental representation by a computing system comprising one or more computing devices onboard a second autonomous vehicle. The first compressed intermediate environmental representation is indicative of a portion of an environment of the second autonomous vehicle and is based on sensor data acquired by the first autonomous vehicle at a first time. A first time-corrected intermediate environmental representation is determined based on the first decompressed intermediate environmental representation by the computing device. The first time-corrected intermediate environmental representation corresponds to a second time associated with the second autonomous vehicle. INDEPENDENT CLAIMS are included for the following: 1. a computing system for vehicle-to-vehicle communication 2. an autonomous vehicle for vehicle-to-vechicle communication Computer- based method for vehicle-to-vehicle communication. The autonomous vehicle technology helps to improve the safety of passengers of an autonomous vehicle, improves the safety of the surroundings of the autonomous vehicle, improves the experience of the rider and operator of the autonomous vehicle. The compressed intermediate environmental representation reduces bandwidth requirements without sacrificing performance. The joint perception or prediction system is configured to perform the functions of the perception system and the prediction system in a coordinated manner for improved speed, efficiency and on-board computational resource cost. |
Please summarize the input | Systems and methods for vehicle-to-vehicle communications for improved autonomous vehicle operationsSystems and methods for vehicle-to-vehicle communications are provided. An example computer-implemented method includes obtaining, by a computing system onboard a first autonomous vehicle, sensor data associated with an environment of the first autonomous vehicle. The method includes determining, by the computing system, an intermediate environmental representation of at least a portion of the environment of the first autonomous vehicle based at least in part on the sensor data. The method includes generating, by the computing system, a compressed intermediate environmental representation by compressing the intermediate environmental representation of at least the portion of the environment of the first autonomous vehicle. The method includes communicating, by the computing system, the compressed intermediate environmental representation to a second autonomous vehicle.What is claimed is:
| 1. A computer-implemented method for vehicle-to-vehicle communications, the method comprising:
obtaining, by a first autonomous vehicle, sensor data associated with an environment of the first autonomous vehicle;
determining an intermediate environmental representation of at least a portion of the environment of the first autonomous vehicle based at least in part on the sensor data;
generating a compressed intermediate environmental representation by compressing the intermediate environmental representation of at least the portion of the environment of the first autonomous vehicle;
selecting a second autonomous vehicle to which to communicate the compressed intermediate environmental representation from among a plurality of autonomous vehicles based at least in part on an ability of the second autonomous vehicle to decompress the compressed intermediate environmental representation, wherein selecting the second autonomous vehicle comprises:
communicating with the second autonomous vehicle as the second autonomous vehicle enters a communication range of the first autonomous vehicle;
exchanging, with the second autonomous vehicle, data indicating that the second autonomous vehicle is able to decompress the compressed intermediate environmental representation; and
selecting the second autonomous vehicle based on the data indicating that the second autonomous vehicle is able to decompress the compressed intermediate environmental representation; and
communicating the compressed intermediate environmental representation to the second autonomous vehicle.
| 2. The computer-implemented method of claim 1, wherein the sensor data comprises three-dimensional point cloud data, and wherein determining the intermediate environmental representation comprises:
generating voxelized sensor data by voxelizing the three-dimensional point cloud data;
inputting the voxelized sensor data into a machine-learned model, the machine-learned model configured to apply one or more convolutional layers to the voxelized sensor data; and
obtaining the intermediate environmental representation as an output of the machine-learned model.
| 3. The computer-implemented method of claim 1, wherein the intermediate environmental representation comprises a feature map describing at least the portion of the environment of the first autonomous vehicle.
| 4. The computer-implemented method of claim 1, further comprising:
selecting the second autonomous vehicle to which to communicate the compressed intermediate environmental representation from among a plurality of autonomous vehicles.
| 5. The computer-implemented method of claim 4, wherein selecting the second autonomous vehicle to which to communicate the compressed intermediate environmental representation from among the plurality of autonomous vehicles comprises:
selecting, by the computing system, the second autonomous vehicle based at least in part on the communication range of the first autonomous vehicle.
| 6. The computer-implemented method of claim 1, wherein the sensor data comprises LIDAR point cloud data.
| 7. The computer-implemented method of claim 1, wherein the sensor data comprises a first type of sensor data and a second type of sensor data, wherein the first type of sensor data is associated with a first sensor modality, and the second type of sensor data is associated with a second sensor modality.
| 8. The computer-implemented method of claim 1, wherein the sensor data comprises a first set of sensor data acquired by the first autonomous vehicle and a second set of sensor data acquired by another autonomous vehicle.
| 9. A computing system comprising:
one or more processors; and
one or more tangible, non-transitory, computer readable media that collectively store instructions that when executed by the one or more processors cause the computing system to perform operations, the operations comprising:
obtaining sensor data associated with an environment of a first autonomous vehicle;
determining an intermediate environmental representation of at least a portion of the environment of the first autonomous vehicle based at least in part on the sensor data and a machine-learned model;
generating a compressed intermediate environmental representation by compressing the intermediated environmental representation of at least the portion of the environment of the first autonomous vehicle;
selecting a second autonomous vehicle to which to communicate the compressed intermediate environmental representation from among a plurality of autonomous vehicles based at least in part on an ability of the second autonomous vehicle to decompress the compressed intermediate environmental representation, wherein selecting the second autonomous vehicle comprises:
communicating with the second autonomous vehicle as the second autonomous vehicle enters a communication range of the first autonomous vehicle;
exchanging, with the second autonomous vehicle, data indicating that the second autonomous vehicle is able to decompress the compressed intermediate environmental representation; and
selecting the second autonomous vehicle based on the data indicating that the second autonomous vehicle is able to decompress the compressed intermediate environmental representation; and
communicating the compressed intermediate environmental representation to the second autonomous vehicle.
| 10. The computing system of claim 9, wherein the sensor data comprises three-dimensional LIDAR point cloud data.
| 11. The computing system of claim 9, wherein determining the intermediate environmental representation comprises:
generating voxelized sensor data based at least in part on the sensor data; and
generating the intermediate environmental representation based at least in part on the voxelized sensor data and the machine-learned model.
| 12. The computing system of claim 9, wherein the second autonomous vehicle is configured to decompress the compressed intermediate environmental representation and utilize the intermediate environmental representation for one or more autonomous operations of the second autonomous vehicle.
| 13. The computing system of claim 9, wherein the operations further comprise:
obtaining, from another autonomous vehicle, a second intermediate environmental representation of at least the portion of the environment of the first autonomous vehicle.
| 14. An autonomous vehicle comprising:
one or more sensors;
one or more processors; and
one or more tangible, non-transitory, computer readable media that collectively store instructions that when executed by the one or more processors cause the one or more processors to perform operations, the operations comprising:
obtaining, via the one or more sensors, sensor data associated with an environment of the autonomous vehicle;
determining a first intermediate environmental representation of at least a portion of the environment of the autonomous vehicle based at least in part on the sensor data;
generating a first compressed intermediate environmental representation by compressing the first intermediate environmental representation of at least the portion of the environment of the autonomous vehicle;
determining a recipient to which to communicate the first compressed intermediate environmental representation from among a plurality of potential recipients based at least in part on an ability of the recipient to decompress the compressed intermediate environmental representation, wherein determining the recipient comprises:
exchanging, with the recipient, data indicating that the recipient is able to decompress the compressed intermediate environmental representation; and
selecting the recipient based on the data indicating that the recipient is able to decompress the compressed intermediate environmental representation;
communicating the first compressed intermediate environmental representation to the recipient.
| 15. The autonomous vehicle of claim 14, wherein determining the first intermediate environmental representation based at least in part on the sensor data comprises:
generating voxelized sensor data by voxelizing three-dimensional point cloud data of the sensor data;
inputting the voxelized sensor data into a machine-learned model; and
receiving the first intermediate environmental representation as an output of the machine-learned model.
| 16. The autonomous vehicle of claim 15, wherein the machine-learned model is configured to apply one or more convolutional layers to the voxelized sensor data.
| 17. The autonomous vehicle of claim 14, wherein the operations further comprise:
obtaining a second compressed intermediate environmental representation from another autonomous vehicle;
generating a decompressed intermediate environmental representation by decompressing the second compressed intermediate environmental representation;
determining, using one or more machine-learned models, an updated intermediate environmental representation based at least in part on the decompressed intermediate environmental representation and the first intermediate environmental representation generated by the first autonomous vehicle; and
generating an autonomy output for the autonomous vehicle based at least in part on the updated intermediate environmental representation.
| 18. The autonomous vehicle of claim 17, wherein the operations further comprise compensating for a time delay between the first intermediate environmental representation and the decompressed intermediate environmental representation. | The method (700) involves obtaining (702) the sensor data associated with an environment of the first autonomous vehicle by a computing system for the multiple computing devices onboard a first autonomous vehicle. An intermediate environmental representation of a portion for the environment in the first autonomous vehicle is determined (704) based on the sensor data by the computing system. A compressed intermediate environmental representation is generated (706) by compressing the intermediate environmental representation of at least the portion for the environment of the first autonomous vehicle by the computing system. The compressed intermediate environmental representation is communicated (710) to a second autonomous vehicle. INDEPENDENT CLAIMS are also included for the following:(a) a computing system for performing an autonomous vehicle operations in vehicle-to-vehicle communication;(b) an autonomous vehicle for vehicle-to-vehicle communications. Method for performing an autonomous vehicle operations in vehicle-to-vehicle communications. Method ensures the more accurate estimates of the object's position, size, and shape, as well as the predicted future trajectory of the object and improve the ability of the autonomous vehicle to safely plan its motion though its environment. The drawing shows a flow chart of a method for performing an autonomous vehicle operations in vehicle-to-vehicle communications.700Method 702Obtaining the sensor data associated with an environment of the first autonomous vehicle by a computing system for the multiple computing devices onboard a first autonomous vehicle 704Determining an intermediate environmental representation of a portion for the environment in the first autonomous vehicle based on the sensor data by the computing system 706Generating the compressed intermediate environmental representation by compressing the intermediate environmental representation of at least the portion for the environment of the first autonomous vehicle by the computing system 710Communicating the compressed intermediate environmental representation to a second autonomous vehicle |
Please summarize the input | Systems and methods for vehicle-to-vehicle communications for improved autonomous vehicle operationsSystems and methods for vehicle-to-vehicle communications are provided. An example computer-implemented method includes obtaining from a first autonomous vehicle, by a second autonomous vehicle, a first compressed intermediate environmental representation. The first compressed intermediate environmental representation is indicative of at least a portion of an environment of the second autonomous vehicle. The method includes generating a first decompressed intermediate environmental representation by decompressing the first compressed intermediate environmental representation. The method includes determining, using one or more machine-learned models, an updated intermediate environmental representation based at least in part on the first decompressed intermediate environmental representation and a second intermediate environmental representation generated by the second autonomous vehicle. The method includes generating an autonomy output for the second autonomous vehicle based at least in part on the updated intermediate environmental representation.What is claimed is:
| 1. A computer-implemented method for vehicle-to-vehicle communications, the method comprising:
obtaining from a first autonomous vehicle, by a computing system comprising one or more computing devices onboard a second autonomous vehicle, a first compressed intermediate environmental representation output by one or more intermediate layers of a machine-learned perception model of the first autonomous vehicle, wherein the first compressed intermediate environmental representation is indicative of at least a portion of an environment of the second autonomous vehicle;
generating, by the computing system, a first decompressed intermediate environmental representation by decompressing the first compressed intermediate environmental representation;
determining, by the computing system using one or more machine-learned models onboard the second autonomous vehicle, an updated intermediate environmental representation based at least in part on the first decompressed intermediate environmental representation and a second intermediate environmental representation output by one or more intermediate layers of a machine-learned perception model of the second autonomous vehicle,
wherein the one or more machine-learned models used to determine the updated intermediate environmental representation comprise a machine-learned aggregation model configured to aggregate a plurality of intermediate environmental representations,
wherein the machine-learned aggregation model comprises a graph neural network, and wherein the graph neural network comprises a plurality of nodes, each respective node of the graph neural network corresponding to a respective autonomous vehicle of a plurality of autonomous vehicles within the environment of the second autonomous vehicle, wherein the plurality of autonomous vehicles comprises the first autonomous vehicle,
wherein at least one node of the machine-learned aggregation model is configured to be updated based on a change to the plurality of autonomous vehicles that are within the environment of the second autonomous vehicle; and
generating, by the computing system, an autonomy output for the second autonomous vehicle based at least in part on the updated intermediate environmental representation.
| 2. The computer-implemented method of claim 1, wherein the machine-learned aggregation model is configured based on a number of transmitter vehicles within the environment of the second autonomous vehicle.
| 3. The computer-implemented method of claim 1, wherein each respective autonomous vehicle of the plurality of autonomous vehicles is associated with a respective set of spatial coordinates, and wherein the machine-learned aggregation model is configured to transform the first decompressed intermediate environmental representation based at least in part on a set of spatial coordinates associated with the first autonomous vehicle.
| 4. The computer-implemented method of claim 1, wherein determining, onboard the second autonomous vehicle, the updated intermediate environmental representation comprises:
determining, by a time delay correction model, a first time-corrected intermediate environmental representation, wherein the first-time corrected intermediate environmental representation comprises a first time associated with a sensor timestamp of the first autonomous vehicle;
obtaining, by the time delay correction model, a second time indicative of a time at which the second autonomous vehicle intends to perceive the environment;
generating, an updated time-corrected intermediate environmental representation,
wherein the updated time-corrected intermediate environmental representation accounts for a time delay associated with the first time-corrected intermediate environmental representation obtained from the first autonomous vehicle and the second time at which the second autonomous vehicle intends to perceive the environment; and
determining, by the computing system, the updated intermediate environmental representation based at least in part on the updated time-corrected intermediate environmental representation.
| 5. The computer-implemented method of claim 4, wherein determining the updated intermediate environmental representation further comprises:
determining, by the computing system, the updated intermediate environmental representation based at least in part on the first time-corrected intermediate environmental representation, the second intermediate environmental representation generated by the second autonomous vehicle, and the machine-learned aggregation model.
| 6. The computer-implemented method of claim 5, wherein the machine-learned aggregation model is configured to aggregate the first time-corrected intermediate environmental representation and the second intermediate environmental representation and provide the updated intermediate environmental representation as an output of the machine-learned aggregation model.
| 7. The computer-implemented method of claim 1, wherein generating the autonomy output for the second autonomous vehicle based at least in part on the updated intermediate environmental representation comprises:
inputting, by the computing system, the updated intermediate environmental representation into the machine-learned perception model of the second autonomous vehicle; and
obtaining, by the computing system, the autonomy output as an output of the machine-learned perception model of the second autonomous vehicle.
| 8. The computer-implemented method of claim 1, wherein the autonomy output is indicative of a bounding shape associated with an object within the environment of the second autonomous vehicle and one or more predicted future locations of the object.
| 9. The computer-implemented method of claim 1, wherein the object is occluded from a field of view of one or more sensors of the second autonomous vehicle.
| 10. The computer-implemented method of claim 1, further comprising:
generating, by the computing system, a motion plan for the second autonomous vehicle based at least in part on the autonomy output; and
initiating, by the computing system, a motion control of the second autonomous vehicle based at least in part on the motion plan.
| 11. An autonomous vehicle computing system comprising:
a machine-learned aggregation model configured to aggregate a plurality of intermediate environmental representations from a first autonomous vehicle and a second autonomous vehicle, the autonomous vehicle computing system being onboard the second autonomous vehicle;
a machine-learned perception model configured to generate autonomy outputs;
one or more processors; and
one or more tangible, non-transitory, computer readable media that store instructions that are executable by the one or more processors to cause the computing system to perform operations, the operations comprising:
obtaining a first compressed intermediate environmental representation output by one or more intermediate layers of a machine-learned perception model of the first autonomous vehicle, wherein the first compressed intermediate environmental representation is indicative of at least a portion of an environment of the second autonomous vehicle;
generating a first decompressed intermediate environmental representation by decompressing the first compressed intermediate environmental representation;
obtaining a second intermediate environmental representation output by one or more intermediate layers of a machine-learned perception model of the second autonomous vehicle;
determining, using the machine-learned aggregation model, an updated intermediate environmental representation based at least in part on the first decompressed intermediate environmental representation and the second intermediate environmental representation,
wherein the machine-learned aggregation model comprises a graph neural network, the graph neural network comprising a plurality of nodes each corresponding to a respective autonomous vehicle of a plurality of autonomous vehicle within the environment of the second autonomous vehicle, wherein the plurality of autonomous vehicles comprises the first autonomous vehicle,
wherein at least one node of the machine-learned aggregation model is configured to be updated based on a change to the plurality of autonomous vehicles that are within the environment of the second autonomous vehicle; and
generating an autonomy output for the second autonomous vehicle based at least in part on the updated intermediate environmental representation.
| 12. The autonomous vehicle computing system of claim 11, wherein determining the updated intermediate environmental representation based at least in part on the first decompressed intermediate environmental representation and the second intermediate environmental representation comprises:
inputting the first decompressed intermediate environmental representation and the second intermediate environmental representation into the machine-learned aggregation model,
wherein the machine-learned aggregation model is configured to aggregate the first decompressed intermediate environmental representation and the second intermediate environmental representation to generate the updated intermediate environmental representation; and
obtaining the updated intermediate environmental representation as an output of the machine-learned aggregation model.
| 13. The autonomous vehicle computing system of claim 11, wherein the machine-learned aggregation model is configured to initialize a node state of at least one node of the graph neural network and to update the node state of the at least one node based at least in part on a spatial transformation.
| 14. The autonomous vehicle computing system of claim 11, further comprising a time delay correction model configured to adjust the first decompressed intermediate environmental representation to account for a time delay.
| 15. The autonomous vehicle computing system of claim 14, wherein the first decompressed intermediate environmental representation is adjusted to account for the time delay using the time delay correction model.
| 16. The autonomous vehicle computing system of claim 11, wherein obtaining the second intermediate environmental representation generated by the second autonomous vehicle comprises: obtaining sensor data via one or more sensors of the second autonomous vehicle; and determining the second intermediate environmental representation based at least in part on the sensor data obtained via the one or more sensors of the second autonomous vehicle.
| 17. An autonomous vehicle comprising:
one or more processors; and
one or more tangible, non-transitory, computer readable media that store instructions that are executable by the one or more processors to cause the one or more processors to perform operations, the operations comprising:
obtaining a first compressed intermediate environmental representation output by one or more intermediate layers of a machine-learned perception model of another autonomous vehicle, wherein the first compressed intermediate environmental representation is indicative of at least a portion of an environment of the autonomous vehicle;
generating a first decompressed intermediate environmental representation by decompressing the first compressed intermediate environmental representation;
generating a second intermediate environmental representation output by one or more intermediate layers of a machine-learned perception model of the autonomous vehicle;
determining, using a machine-learned aggregation model onboard the autonomous vehicle, an updated intermediate environmental representation based at least in part on the first decompressed intermediate environmental representation and the second intermediate environmental representation,
wherein the machine-learned aggregation model comprises a graph neural network, and wherein the graph neural network comprises a plurality of nodes, each respective node of the graph neural network corresponding to a respective autonomous vehicle of a plurality of autonomous vehicles within the environment of the autonomous vehicle, wherein the plurality of autonomous vehicles comprises the other autonomous vehicle,
wherein at least one node of the machine-learned aggregation model is configured to be updated based on a change to the plurality of autonomous vehicles that are within the environment of the autonomous vehicle; and
generating an autonomy output for the autonomous vehicle based at least in part on the updated intermediate environmental representation, wherein the autonomy output is indicative of an object within the environment of the autonomous vehicle and one or more predicted future locations of the object; and
generating a motion plan for the autonomous vehicle based at least in part on the autonomy output.
| 18. The autonomous vehicle of claim 17, wherein the object is occluded from a field of view of one or more sensors of the autonomous vehicle, and wherein the motion plan comprises a trajectory for the autonomous vehicle. | The method involves obtaining a first compressed intermediate environmental representation from a first autonomous vehicle by a computing system. A first decompressed intermediate environmental representation by decompressing the first compressed intermediate environmental representation is generated by the computing system. An updated intermediate environmental representation is determined based on the first decompressed intermediate environmental representation and a second intermediate environmental representation generated by a second autonomous vehicle through the computing system using one or more machine-learned models. An autonomy output is generated for the second autonomous vehicle based on the updated intermediate environmental representation by the computing system. An INDEPENDENT CLAIM is included for a system for performing operations in an autonomous vehicle by utilizing machine-learned models for vehicle-to-vehicle communications. Method for performing operations in an autonomous vehicle (claimed) by utilizing machine-learned models for vehicle-to-vehicle communications. The method enables estimating object's position, size, and shape, as well as a predicted future trajectory of the object accurately and improving ability of the autonomous vehicle to safely plan its motion though its environment. The method enables allowing an ecosystem of autonomous vehicles or systems within a geographic area to provide inter-vehicle or system communications that improve the vehicles or systems autonomous operations, thus reducing communication bandwidth and potential information loss, and allowing for an effective and efficient sharing of vehicle information when intermediate environmental representations can be compressed without losing information. The method enables improving safety of passengers of an autonomous vehicle, safety of the surroundings of the autonomous vehicle, and experience of the rider or operator of the autonomous vehicle. ` The drawing shows a block diagram of a system for an autonomous vehicle.104 Operations Computing System 106Remote Computing Device 108 Communication Networks 112Vehicle Computing System 118Positioning System |
Please summarize the input | INTELLIGENT DETECTION OF EMERGENCY VEHICLESAn autonomous vehicle detects an approaching emergency vehicle by performing a plurality of types of analysis of signals output by sensors in the vehicle. Each type of analysis generates a different prediction that indicates a likelihood of a proximity of an emergency vehicle to the autonomous vehicle. The predictions are processed and fused to generate a determination that indicates whether an emergency vehicle is proximate to the autonomous vehicle. In response to the determination indicating the emergency vehicle is proximate, the autonomous vehicle performs an action.We claim:
| 1. A method comprising:
processing a signal received from a sensor associated with an autonomous vehicle, the processing outputting a prediction indicating a likelihood that an emergency vehicle is present in an environment proximate to the autonomous vehicle;
receiving, at the autonomous vehicle, a vehicle-to-vehicle communication message from another vehicle that contains a notification of an approaching emergency vehicle;
generating a determination, based on the output prediction and the vehicle-to-vehicle communication message, that indicates whether an emergency vehicle is proximate to the autonomous vehicle;
in response to the determination indicating the emergency vehicle is proximate, causing the autonomous vehicle to perform an action.
| 2. The method of claim 1, further comprising:
receiving information from a remote computing device that indicates a path of an emergency vehicle;
wherein the determination indicating whether the emergency vehicle is proximate to the autonomous vehicle is further generated based on the information received from the remote computing device.
| 3. The method of claim 1, wherein the action comprises autonomously navigating out of a path of the emergency vehicle.
| 4. The method of claim 1, wherein the action comprises transmitting a vehicle-to-vehicle message indicating the emergency vehicle's presence.
| 5. The method of claim 1, wherein the action comprises uploading a notification of the emergency vehicle's presence to a remote computing device.
| 6. The method of claim 1, wherein processing the signal received from the sensor further comprises predicting a direction from which the emergency vehicle is approaching the autonomous vehicle.
| 7. The method of claim 6, wherein the action is selected based on the predicted direction.
| 8. A non-transitory computer readable storage medium storing executable instructions, the instructions when executed by one or more processors causing the one or more processors to:
obtain computer vision data captured by sensors associated with one or more vehicles during operation of the one or more vehicles;
train a model using the obtained computer vision data, the model when trained configured to receive real-time computer vision data indicative of an environment of an autonomous vehicle and output a classification of a behavior of traffic in the environment as either indicative of a presence of an emergency vehicle or not indicative of a presence of an emergency vehicle;
receiving classifications output by the trained model during operation of a target vehicle and predictions indicating a likelihood that an emergency vehicle is proximate to the target vehicle, the predictions generated based on processing of a signal received from a sensor in the target vehicle; and
retraining the model based on a difference between the classifications and the predictions.
| 9. The non-transitory computer readable storage medium of claim 8, wherein the instructions further cause the one or more processors to:
train a second model using the obtained computer vision data, the second model when trained configured to receive the real-time computer vision data indicative of the environment of the target vehicle and output a classification of vehicle lights detected in the environment as either indicative of a presence of an emergency vehicle or not indicative of a presence of an emergency vehicle.
| 10. The non-transitory computer readable storage medium of claim 9, wherein the second model when trained further outputs a classification of a type of the emergency vehicle.
| 11. The non-transitory computer readable storage medium of claim 8, wherein the instructions further cause the one or more processors to:
train a third model using the obtained computer vision data, the third model when trained configured to receive the real-time computer vision data indicative of the environment of the target vehicle and output a classification of a direction an emergency vehicle is traveling relative to the target vehicle.
| 12. An autonomous vehicle, comprising:
a plurality of sensors configured to measure parameters of an environment around the autonomous vehicle;
one or more processors; and
a non-transitory computer readable storage medium storing executable instructions, the instructions when executed by the one or more processors causing the one or more processors to:
perform a plurality of types of analysis of signals output by the plurality of sensors, each type of analysis generating a prediction that indicates a likelihood of a proximity of an emergency vehicle to the autonomous vehicle based on one or more of the signals;
process the generated predictions to generate a determination that indicates whether an emergency vehicle is proximate to the autonomous vehicle; and
in response to the determination indicating the emergency vehicle is proximate, cause the autonomous vehicle to perform an action.
| 13. The autonomous vehicle of claim 12, wherein the instructions further cause the one or more processors to:
receive information from a remote computing device that indicates a path of an emergency vehicle;
wherein processing the generated predictions further comprises using the received information to generate the determination indicating whether the emergency vehicle is proximate to the autonomous vehicle.
| 14. The autonomous vehicle of claim 12, wherein the plurality of types of analysis comprise a siren sound detection analysis, and wherein performing the siren sound detection analysis comprises:
extracting one or more features from a sound signal received from a microphone in the autonomous vehicle;
comparing the extracted features to expected features of an emergency vehicle siren; and
in response to identifying a match between the extracted features and the expected features of the emergency vehicle siren, outputting a prediction that the emergency vehicle is proximate to the autonomous vehicle.
| 15. The autonomous vehicle of claim 12, wherein the plurality of types of analysis comprise an emergency vehicle light detection analysis, and wherein performing the emergency vehicle light detection analysis comprises:
extracting lighting features from a perception data signal received from a computer vision system in the autonomous vehicle;
comparing the extracted lighting features to expected features of emergency vehicle lights; and
in response to identifying a match between the extracted lighting features and the expected features of emergency vehicle lights, outputting a prediction that the emergency vehicle is proximate to the autonomous vehicle.
| 16. The autonomous vehicle of claim 12, wherein the plurality of types of analysis comprise a traffic behavior analysis, and wherein performing the traffic behavior analysis comprises:
providing perception data received from a computer vision system in the autonomous vehicle to a machine learning model, the machine learning model configured to output a classification of traffic behaviors captured in the perception data as indicative of or not indicative of an approaching emergency vehicle.
| 17. The autonomous vehicle of claim 12, wherein the plurality of types of analysis comprise processing a vehicle-to-vehicle message received from another vehicle to determine if the vehicle-to-vehicle message contains an indication that an emergency vehicle is approaching the autonomous vehicle.
| 18. The autonomous vehicle of claim 12, wherein processing the generated predictions to generate the determination comprises:
determining the emergency vehicle is proximate to the autonomous vehicle if a specified number of the types of analysis generated a prediction indicating an emergency vehicle is likely to be proximate to the autonomous vehicle.
| 19. The autonomous vehicle of claim 12, wherein processing the generated predictions to generate the determination comprises:
determining the emergency vehicle is proximate to the autonomous vehicle if:
a first type of analysis generated a first prediction indicating a likely proximity of the emergency vehicle; and
a second type of analysis generated a second prediction indicating a likely proximity of the emergency vehicle.
| 20. The autonomous vehicle of claim 12, wherein processing the generated predictions to generate the determination comprises:
applying a weighting function to the predictions generated by each of the plurality of types of analysis. | The method involves processing a signal received from a sensor (110) associated with an autonomous vehicle (100), where the processing outputs a prediction indicating a likelihood that an emergency vehicle is present in an environment proximate to the autonomous vehicle. A vehicle-to-vehicle communication message is received from another vehicle that contains a notification of an approaching emergency vehicle. The autonomous vehicle is caused to perform an action in response to the determination indicating the emergency vehicle proximate, where a determination is generated based on the output prediction and the vehicle-to-vehicle communication message that indicates whether the vehicle is proximate. INDEPENDENT CLAIMS are also included for:a computer readable storage medium comprising a set of instructions for performing intelligent detection of an emergency vehicle; andan autonomous vehicle. Computer-implemented method for performing intelligent detection of an emergency vehicle e.g. autonomous vehicle such as police car, fire lorry or ambulance, on a public road. The method enables providing accurate and timely detection of emergency vehicles to enable the autonomous vehicles to observe applicable traffic laws. The drawing shows a block diagram of components of the autonomous vehicle.100Autonomous vehicle110Sensor120Sensor analysis module122Siren sound detection module124Emergency vehicle light detection module |
Please summarize the input | Direct autonomous vehicle to autonomous vehicle communicationsA system comprises a lead autonomous vehicle (AV), a control device associated with the lead AV, and a following AV. The control device receives a command to navigate the lead AV to avoid an unexpected road condition. The control device receives sensor data from a sensor of the lead AV, comprising location coordinates of objects ahead of the lead AV. The control device accesses environmental data associated with a portion of a road between the lead AV and following AV. The environmental data comprises location coordinates of objects between the lead AV and following AV. The control device determines whether an object in the sensor data or environmental data impedes performing the command by the following AV. The control device updates the command, if the control device determines that an object impedes performing the command by the following AV, and communicates the updated command to the following AV.What is claimed is:
| 1. A system, comprising:
a lead autonomous vehicle comprising at least one vehicle sensor, wherein the lead autonomous vehicle is configured to travel along a road;
a following autonomous vehicle, different from the lead autonomous vehicle and communicatively coupled with the lead autonomous vehicle, wherein the following autonomous vehicle is traveling along the road behind the lead autonomous vehicle;
a first control device associated with the lead autonomous vehicle and comprising a first processor configured to:
receive a command to navigate the lead autonomous vehicle to avoid an unexpected road condition ahead of the lead autonomous vehicle;
receive, from the at least one vehicle sensor, sensor data comprising location coordinates of a first plurality of objects ahead of the lead autonomous vehicle;
access a first set of environmental data associated with a portion of the road between the lead autonomous vehicle and the following autonomous vehicle, wherein the first set of environmental data comprises location coordinates of a second plurality of objects between the lead autonomous vehicle and the following autonomous vehicle;
determine whether at least one object from the first and second plurality of objects impedes performing the command by the following autonomous vehicle;
in response to determining that the at least one object impedes performing the command by the following autonomous vehicle, update the command for the following autonomous vehicle, based at least in part upon the sensor data and the first set of environmental data, such that the updated command comprises one or more navigation instructions to avoid the at least one object while performing the command; and
communicate the updated command to the following autonomous vehicle.
| 2. The system of claim 1, wherein the unexpected road condition comprises at least one of an unexpected weather condition and an unexpected traffic congestion.
| 3. The system of claim 1, wherein the first set of environmental data is associated with a time window during which the following autonomous vehicle is traveling along the road.
| 4. The system of claim 1, further comprising a second control device associated with the following autonomous vehicle and comprising a second processor configured to:
receive, from the lead autonomous vehicle, the updated command; and
navigate the following autonomous vehicle according to the updated command.
| 5. The system of claim 4, wherein the following autonomous vehicle comprises a first following autonomous vehicle and the system further comprising a second following autonomous vehicle, communicatively coupled with the first following autonomous vehicle, the second following autonomous vehicle is traveling along the road behind the first following autonomous vehicle, wherein:
the second processor is further configured to:
access a second set of environmental data associated with a portion of the road between the first following autonomous vehicle and the second following autonomous vehicle, wherein the second set of environmental data comprises location coordinates of a third plurality of objects between the first following autonomous vehicle and the second following autonomous vehicle;
determine whether at least one object from the third plurality of objects impedes performing the updated command;
in response to determining that the at least one object impedes performing the updated command, generate a second updated command by updating the updated command based at least in part upon the aggregate of the first and second sets of environmental data, such that the second updated command comprises one or more navigation instructions to avoid the at least one object from the third plurality of objects while performing the updated command; and
communicate the second updated command to the second following autonomous vehicle.
| 6. The system of claim 1, wherein:
the lead autonomous vehicle and the following autonomous vehicle are within a Vehicle-to-Vehicle communication range, and
the Vehicle-to-Vehicle communication range corresponds to a threshold distance of a Vehicle-to-Vehicle module implemented in the lead autonomous vehicle and the following autonomous vehicle to establish a communication path between the lead autonomous vehicle and the following autonomous vehicle.
| 7. The system of claim 5, wherein the first processor is further configured to:
in response to the lead autonomous vehicle and the second following autonomous vehicle being within a Vehicle-to-Vehicle communication range, generate the second updated command for the second following autonomous vehicle, based at least in part upon the aggregate of the first and second sets of environmental data; and
communicate the second updated command to the second following autonomous vehicle.
| 8. A method comprising:
receiving a command to navigate a lead autonomous vehicle to avoid an unexpected road condition ahead of the lead autonomous vehicle, wherein:
the lead autonomous vehicle comprises at least one vehicle sensor; and
the lead autonomous vehicle is configured to travel along a road;
receiving, from the at least one vehicle sensor, sensor data comprising location coordinates of a first plurality of objects ahead of the lead autonomous vehicle;
accessing a first set of environmental data associated with a portion of the road between the lead autonomous vehicle and a following autonomous vehicle, wherein:
the following autonomous vehicle is communicatively coupled with the lead autonomous vehicle, and is traveling along the road behind the lead autonomous vehicle; and
the first set of environmental data comprises location coordinates of a second plurality of objects between the lead autonomous vehicle and the following autonomous vehicle;
determining whether at least one object from the first and second plurality of objects impedes performing the command by the following autonomous vehicle;
in response to determining that the at least one object impedes performing the command by the following autonomous vehicle, updating the command for the following autonomous vehicle, based at least in part upon the sensor data and the first set of environmental data, such that the updated command comprises one or more navigation instructions to avoid the at least one object while performing the command; and
communicating the updated command to the following autonomous vehicle.
| 9. The method of claim 8, further comprising:
generating a second updated command by updating the command for navigating the lead autonomous vehicle, based at least in part upon the sensor data, such that the second updated command comprises one or more navigation instructions to avoid the first plurality of objects while performing the command; and
navigating the lead autonomous vehicle according to the second updated command.
| 10. The method of claim 8, further comprising:
accessing environmental data associated with a portion of the road ahead of the lead autonomous vehicle, wherein the environmental data is associated with a time window during which the lead autonomous vehicle is traveling along the road;
comparing the environmental data with map data that comprises expected road conditions ahead of the lead autonomous vehicle;
based at least in part upon comparing the environmental data with the map data, determining whether the environmental data comprises an unexpected road condition that is not included in the map data; and
in response to determining that the environmental data comprises the unexpected road condition that is not included in the map data:
determining a location coordinate of the unexpected road condition; and
communicating the command to the lead autonomous vehicle to maneuver to avoid the unexpected road condition.
| 11. The method of claim 8, further comprising:
comparing the sensor data with map data, wherein the map data comprises location coordinates of expected objects on the road ahead of the lead autonomous vehicle;
based at least in part upon comparing the sensor data with the map data, determining whether the sensor data indicates an unexpected object that is not in the map data;
in response to determining that the sensor data indicates the unexpected object that is not in the map data:
determining a location coordinate of the unexpected object; and
determining a proposed navigation instruction for the lead autonomous vehicle to avoid the unexpected object.
| 12. The method of claim 11, further comprising, in response to determining that the sensor data indicates the unexpected object that is not in the map data, performing the proposed navigation instruction.
| 13. The method of claim 11, further comprising, in response to determining that the sensor data indicates the unexpected object that is not in the map data:
communicating the proposed navigation instruction to an operation server;
determining whether a confirmation is received from the operation server to perform the proposed navigation instruction; and
in response to receiving the confirmation from the operation server, performing the proposed navigation instruction.
| 14. The method of claim 11, further comprising, in response to determining that the sensor data indicates the unexpected object that is not in the map data:
communicating the sensor data to an operation server; and
requesting the operation server to provide instructions to avoid the unexpected object based at least in part upon the sensor data.
| 15. The method of claim 8, wherein the command is related to at least one of:
a transition, by the lead autonomous vehicle, from autonomous driving to manual driving;
avoiding, by the lead autonomous vehicle, obstacles on the road ahead of the lead autonomous vehicle;
avoiding, by the lead autonomous vehicle, one or more certain lanes on which one or more obstacles are detected;
avoiding, by the lead autonomous vehicle, one or more certain routes on which the unexpected road condition is detected;
taking, by the lead autonomous vehicle, a particular re-route; and
driving, by the lead autonomous vehicle, slower or faster than the speed indicated in a driving instruction associated with the lead autonomous vehicle.
| 16. A non-transitory computer-readable medium storing instructions that when executed by one or more processors cause the one or more processors to:
receive a command to navigate a lead autonomous vehicle to avoid an unexpected road condition ahead of the lead autonomous vehicle, wherein:
the lead autonomous vehicle comprises at least one vehicle sensor; and
the lead autonomous vehicle is configured to travel along a road;
receive, from the at least one vehicle sensor, sensor data comprising location coordinates of a first plurality of objects ahead of the lead autonomous vehicle;
access a first set of environmental data associated with a portion of the road between the lead autonomous vehicle and a following autonomous vehicle, wherein:
the following autonomous vehicle is communicatively coupled with the lead autonomous vehicle, and is traveling along the road behind the lead autonomous vehicle; and
the first set of environmental data comprises location coordinates of a second plurality of objects between the lead autonomous vehicle and the following autonomous vehicle;
determine whether at least one object from the first and second plurality of objects impedes performing the command by the following autonomous vehicle;
in response to determining that the at least one object impedes performing the command by the following autonomous vehicle, update the command for the following autonomous vehicle, based at least in part upon the sensor data and the first set of environmental data, such that the updated command comprises one or more navigation instructions to avoid the at least one object while performing the command; and
communicate the updated command to the following autonomous vehicle.
| 17. The non-transitory computer-readable medium of claim 16, wherein:
the command comprises a broad command directed to the lead autonomous vehicle and one or more following autonomous vehicles that are behind the lead autonomous vehicle on the road; and
the broad command comprises one or more navigation instructions to avoid a particular unexpected road condition ahead of the lead autonomous vehicle.
| 18. The non-transitory computer-readable medium of claim 16, wherein:
the command comprises a specific command directed to the lead autonomous vehicle; and
the specific command comprises one or more navigation instructions to avoid a particular unexpected road condition ahead of the lead autonomous vehicle.
| 19. The non-transitory computer-readable medium of claim 16, wherein:
the command comprises a configuration command; and
the configuration command comprises at least one of changing a direction of the at least one vehicle sensor and changing a data sampling frequency of the at least one vehicle sensor.
| 20. The non-transitory computer-readable medium of claim 16, wherein the at least one vehicle sensor comprises at least one of a camera, Light Detection and Ranging (LiDAR) sensor, motion sensor, and infrared sensor. | The system has a lead autonomous vehicle (AV) has a vehicle sensor. A following autonomous vehicle is communicatively coupled with the autonomous vehicle. A control device is associated with the vehicle. A processor receives a command to navigate the vehicle to avoid an unexpected road condition ahead of the vehicle. The processor receives sensor data has location coordinates of a set of objects from the sensor. The control device updates the command for the vehicle based upon the sensor data and environmental data such that the updated command comprises navigation instructions to avoid the object while performs the command, and communicates the command to the following vehicle. INDEPENDENT CLAIMS are included for:(1) a method for directing autonomous vehicle to autonomous vehicle communications;(2) a computer program with instructions for directing autonomous vehicle to autonomous vehicle communications; System for directing autonomous vehicle (AV) to autonomous vehicle communications. System minimizes data transmission overhead at one access/network, and improves tracking the AVs. The drawing shows a block diagram of a system.102Road 110Network 120Operation server 130Command 850aControl device |
Please summarize the input | SYSTEM AND METHOD FOR AN OPTIMIZED ROUTING OF AUTONOMOUS VEHICLES WITH RISK AWARE MAPSAn autonomous vehicle (AV) route planning system and method comprising receiving data indicating a risk on each road segments of a road network, generating a risk aware map of the road network having a dynamic risk layer determined by the received data on each of the road segments of the road network, generating a set of feasible routes between an origin and destination, selecting an optimal route from the set of feasible routes, and transmitting the optimal route to the AV, where the every route in the set of feasible route has an overall risk below a predetermined risk level specified by an oversight system or a third-party. The autonomous vehicle may operate in conjunction with an oversight system, such as when coordinating a fleet of autonomous vehiclesWhat is claimed is:
| 1. An autonomous vehicle (AV) route planning method, comprising:
receiving data indicating a risk on each of road segments of a road network;
generating a risk aware map of the road network having a dynamic risk layer determined by the received data on the each of the road segments of the road network;
generating a set of feasible routes between an origin and destination,
wherein each route in the set of feasible route has an overall risk below a predetermined risk level;
selecting an optimal route between the origin and destination from the set of feasible routes,
wherein the optimal route prioritizes one or more desired parameters; and
transmitting the optimal route to the AV.
| 2. The method of claim 1, wherein the overall risk is a weighted aggregate risk on each of the road segments comprising each route in the set of feasible routes.
| 3. The method of claim 1, the optimal route is transmitted to the AV through vehicle-to-vehicle or vehicle-to-infrastructure communication systems.
| 4. The method of claim 1, wherein the one or more desired parameters are selected by a third party.
| 5. The method of claim 1, wherein the data indicating the risk is received from vehicle-to-vehicle or vehicle-to-infrastructure communication systems.
| 6. The method of claim 1, wherein the dynamical layer includes a level of severity of the risk on each of the road segments, wherein the level of severity is selected from a matrix of risk severities.
| 7. An autonomous vehicle (AV) route planning system, comprising:
a receiver configured to receive data indicating a risk on each of road segments of a road network;
a mapper configured to generate a risk aware map of the road network having a dynamic risk layer determined by the received data on each of the road segments of the road network;
an optimizer configured to determine a set of feasible routes between an origin and destination and to select an optimal route from the set of feasible routes,
wherein each route in the set of feasible route has an overall risk below a predetermined risk level and the optimal route prioritizes one or more desired parameters; and
a transmitter configured to transmit the optimal route to the AV.
| 8. The system of claim 7, wherein the optimal route is transmitted to the AV through vehicle-to-vehicle or vehicle-to-infrastructure communication systems.
| 9. The system of claim 7, wherein the optimal route is transmitted as an undirected graph representing the optimal route.
| 10. The system of claim 7, wherein the one or more desired parameters are selected by a third party.
| 11. The system of claim 7, wherein the one or more desired parameters cause the optimizer to minimize a fuel consumption of the AV.
| 12. The system of claim 7, wherein the one or more desired parameters cause the optimizer to minimize travel time of the AV.
| 13. The system of claim 7, wherein the dynamical risk layer includes a level of severity of the risk on each of the road segments, wherein the level of severity is selected from a matrix of risk severities.
| 14. The system of claim 7, wherein the data indicating the risk is received from vehicle-to-vehicle or vehicle-to-infrastructure communication systems.
| 15. An autonomous vehicle (AV) comprising:
a vehicle sensor subsystem,
wherein the vehicle sensor subsystem senses the current environmental conditions surrounding a road segment;
a storage device for adding the current environmental conditions to a database of historical environmental conditions previously sensed of the road segment;
a transmitter configured to transmit the database of historical environmental conditions to an oversight system; and
a receiver configured to receive, from the oversight system, an optimal routing instruction specifying a path between an origin and destination,
wherein the optimal routing instruction is selected from a set of routing instructions that have an overall risk the path between the origin and destination below a predetermined risk level.
| 16. The AV of claim 15, wherein the sensing of the current environmental conditions further comprise of detecting an accident, adverse weather, or work construction.
| 17. The AV of claim 15, wherein the transmitter transmits the database of historical environmental conditions through a roadside infrastructure unit or a vehicle-to-vehicle communication system.
| 18. The AV of claim 15, wherein the receiver receives the optimal route through a roadside infrastructure unit or a vehicle-to-vehicle communication system.
| 19. The AV of claim 15, wherein the optimal routing instruction minimize a fuel consumption of the AV.
| 20. The AV of claim 15, wherein the optimal routing instruction minimize travel time of the AV. | The vehicle (105) has a vehicle sensor subsystem (144). The vehicle sensor subsystem senses the current environmental conditions surrounding a road segment. A storage device adds the current environmental conditions to a database of historical environmental conditions previously sensed of the road segment. A transmitter is configured to transmit the database of historical environmental conditions to an oversight system. A receiver is configured to receive an optimal routing instruction specifying a path between an origin and destination from the oversight system. The optimal routing instruction is selected from a set of routing instructions that have an overall risk the path between the origin and destination below a predetermined risk level. INDEPENDENT CLAIMS are included for the following:an autonomous vehicle route planning method; andan autonomous vehicle route planning system. Autonomous vehicle e.g. autonomous truck used in autonomous vehicle (AV) route planning system (claimed). Uses include but are not limited to semi tractor-trailer, 18 wheeler, lorry, class 8 vehicle, passenger vehicle, transport van, cargo van, recreational vehicle, golf cart and transport cart. The optimal routing instruction minimize a fuel consumption of the AV. The optimal route is transmitted to the AV enables a vehicle control subsystem of the AV to control operation of the AV based on the optimal route. The drawing shows a block diagram of a autonomous vehicle route planning system. 105Autonomous vehicle140Vehicle subsystems142Vehicle drive subsystem144Vehicle sensor subsystem160Vehicle subsystem interface |
Please summarize the input | SYSTEM AND METHOD FOR COMMUNICATING A DRIVING MODE OF AN AUTONOMOUS VEHICLEA system for communicating a driving mode of an autonomous vehicle (AV) comprises the AV, a control device, and a notification device. The control device defines a threshold region around the AV. The control device receives sensor data from sensors of the AV. The control device detects presence of a vehicle from the sensor data. The control device determines a distance between the vehicle and the AV. The control device determines that the vehicle is within the threshold region based on determining that the distance between the vehicle and the AV is within the threshold region. While the AV is operating in the autonomous mode, the control device triggers the notification device to notify the vehicle that the AV is operating in the autonomous mode, where notifying that the AV is operating in the autonomous mode comprises presenting a visual notification and/or communicating a data message to other vehicles.|1. A system, comprising:
a control device associated with an autonomous vehicle configured to travel along a road, the control device comprising at least one processor configured to perform operations comprising:
operating the autonomous vehicle in an autonomous mode;
defining a threshold region around the autonomous vehicle;
receiving sensor data from at least one vehicle sensor located on the autonomous vehicle;
detecting, based on the received sensor data, a presence of another vehicle;
determining a distance between the autonomous vehicle and the other vehicle;
determining, based on the distance between the autonomous vehicle and the other vehicle, that the other vehicle is within the threshold region; and
notifying the other vehicle that the autonomous vehicle is operating in the autonomous mode by at least presenting, by a notification device located on the autonomous vehicle, a visual notification.
| 2. The system of claim 1, wherein notifying the other vehicle that the autonomous vehicle is operating in the autonomous mode further comprises communicating a data message to the other vehicle,
wherein the data message indicates the autonomous vehicle is operating in the autonomous mode, and
wherein the data message comprises an identifier associated with the autonomous vehicle.
| 3. The system of claim 1, wherein the notification device comprises a flashing light source, and
wherein presenting the visual notification comprises powering on the flashing light source.
| 4. The system of claim 1, wherein the notification device comprises a display board including a two dimensional array of light emitting diodes, and
wherein presenting the visual notification comprises displaying, on the display board, text indicating the autonomous vehicle is operating in the autonomous mode.
| 5. The system of claim 1, wherein the notification device comprises a device configured to display an image indicating the autonomous vehicle is operating in the autonomous mode, and
wherein presenting the visual notification comprises presenting the image.
| 6. The system of claim 1, wherein one or more notification devices are located on one or more sides of the autonomous vehicle.
| 7. The system of claim 1, wherein one or more notification devices are located on one or more rear view windows of the autonomous vehicle.
| 8. A method, comprising:
operating an autonomous vehicle in an autonomous mode;
defining a threshold region around the autonomous vehicle;
receiving sensor data from at least one vehicle sensor located on the autonomous vehicle;
detecting, based on the received sensor data, a presence of another vehicle;
determining a distance between the autonomous vehicle and the other vehicle;
determining, based on the distance between the autonomous vehicle and the other vehicle, that the other vehicle is within the threshold region; and
notifying the other vehicle that the autonomous vehicle is operating in the autonomous mode by at least presenting, by a notification device located on the autonomous vehicle, a visual notification.
| 9. The method of claim 8, further comprising
detecting, based on the received sensor data, a presence of another autonomous vehicle;
determining a second distance between the autonomous vehicle and the other autonomous vehicle;
determining, that the second distance is less than a vehicle to vehicle communication range; and
transmitting, in response to determining the second distance is less than the vehicle to vehicle communication range, a message from the autonomous vehicle to the other autonomous vehicle.
| 10. The method of claim 8, wherein the at least one vehicle sensor comprises at least one of a camera, a light detection and ranging sensor, or an infrared sensor.
| 11. The method of claim 9, wherein detecting the presence of the other autonomous vehicle further comprises determining that there is no driver in the other autonomous vehicle.
| 12. The method of claim 9, wherein detecting the presence of the other autonomous vehicle further comprises determining that a model of the other autonomous vehicle matches one of a plurality of autonomous vehicle models.
| 13. The method of claim 9, wherein the message transmitted by the autonomous vehicle comprises a type of the autonomous vehicle, and
wherein the type of the autonomous vehicle comprises a truck.
| 14. The method of claim 9, wherein the message transmitted by the autonomous vehicle comprises a software version currently installed in a control device of the autonomous vehicle.
| 15. The method of claim 9, wherein the message transmitted by the autonomous vehicle comprises a next navigation maneuver of the autonomous vehicle, and
wherein the next navigation maneuver comprises at least one of changing to a particular lane at a particular time, taking a particular exit at a particular time, and continuing on the current lane for a particular time period.
| 16. The method of claim 9, further comprising:
in response to detecting the presence of the other autonomous vehicle, increasing a distance between the autonomous vehicle and the other autonomous vehicle.
| 17. The method of claim 8, further comprising:
operating the autonomous vehicle in a nonautonomous mode; and
notifying other vehicle that the autonomous vehicle is operating in the nonautonomous mode.
| 18. The method of claim 8, wherein presenting the visual notification comprises displaying, on display board, text indicating the autonomous vehicle is operating in the autonomous mode, and
wherein the display board comprises a two dimensional array of light emitting diodes.
| 19. The method of claim 8, wherein presenting the visual notification further comprises triggering the notification device to power on a flashing light source.
| 20. A non-transitory computer-readable medium storing instructions, that when executed by one or more processors cause the one or more processors to perform operations comprising:
operating an autonomous vehicle in an autonomous mode;
defining a threshold region around the autonomous vehicle;
receiving sensor data from at least one vehicle sensor located on the autonomous vehicle;
detecting, based on the received sensor data, a presence of another vehicle;
determining a distance between the autonomous vehicle and the other vehicle;
determining, based on the distance between the autonomous vehicle and the other vehicle, that the other vehicle is within the threshold region; and
notifying the other vehicle that the autonomous vehicle is operating in the autonomous mode by at least presenting, by a notification device located on the autonomous vehicle, a visual notification. | The system (100) has a control device (750) that is associated with an autonomous vehicle (702) configured to travel along a road. The control device comprises a processor (122) configured to receive sensor data (130) comprising an image of multiple objects (104) on the road from a vehicle sensor (746) located on the autonomous vehicle. The processor is configured to determine that a light condition level (134) is less than a threshold light level (136) on a particular portion of the received image. The processor is configured to adjust a headlight (110) mounted to the autonomous vehicle and configured to illuminate a portion of the road ahead of the autonomous vehicle in response to determining that the light condition level is less than the threshold light level. The headlight is adjusted to increase illumination on a particular portion of the road that appears in the particular portion of the image. INDEPENDENT CLAIMS are included for the following:a method for implementing adaptive light distribution for autonomous vehicle;a non-transitoiy computer-readable medium storing program for implementing adaptive light distribution for autonomous vehicle; anda computer program for implementing adaptive light distribution for autonomous vehicle. System for implementing adaptive light distribution for autonomous vehicle. The control device diverts the current illumination pattern to another direction to avoid blinding drivers in the oncoming traffic until the on-coming traffic passes by the autonomous vehicle. The autonomous vehicle can travel more safely, and cars surrounding an autonomous vehicle also travels more safely. The system is integrated into a practical application of implementing adaptive light distributions for autonomous vehicles. This, in turn, provides additional practical applications of improving autonomous vehicle's perception of the road ahead of the vehicle and improving the vehicle's sensors' visibility. The drawing shows a schematic diagram of a system for implementing adaptive light distributions for an autonomous vehicle. 100System for implementing adaptive light distribution for autonomous vehicle104Object110Headlight122Processor130Sensor data134Light condition level136Threshold light level702Autonomous vehicle746Vehicle sensor750Control device |
Please summarize the input | System of automatic driving assistance, roadside assistance and vehicle-side assistanceThe present document describes an autonomous driving assistance system, a roadside assistance system and a vehicle-mounted assistance system. The autonomous driving assistance system may include at least one roadside sensor, a roadside device, a roadside Vehicle to Everything (V2X) communication device and a vehicle-mounted V2X communication device. The at least one roadside sensor may be configured to collect environment information of a surrounding environment and transmit the environment information to the roadside device. The roadside device may be configured to process the received environment information to obtain perception information and transmit the perception information to the roadside V2X communication device. The roadside V2X communication device may be configured to transmit the received perception information to the vehicle-mounted V2X communication device. The vehicle-mounted V2X communication device may be configured to transmit the received perception information to a vehicle-mounted autonomous driving system.What is claimed is:
| 1. An automatic driving assistance system, comprising a roadside assistance system and a vehicle-mounted assistance system, the roadside assistance system comprising a plurality of roadside sensors, a roadside device and a roadside Vehicle to Everything (V2X) communication device, and the vehicle-mounted assistance system comprising a vehicle-mounted V2X communication device, wherein:
each of the plurality of roadside sensors is configured to collect environment information of an ambient environment and transmit the environment information to the roadside device;
the roadside device is configured to:
generate a plurality of messages from the environment information collected by the plurality of roadside sensors, the plurality of messages including a plurality of pieces of sensed information, each piece of sensed information being generated from the environment information collected by a respective one of the plurality of roadside sensors,
combine the plurality of messages generated from the environment information collected by the plurality of roadside sensors into a single message at least by removing redundant sensed information based on a confidence level associated with each piece of sensed information, the single message comprising combined sensed information, and
transmit the combined sensed information to the roadside V2X communication device;
the roadside V2X communication device is configured to transmit the combined sensed information to the vehicle-mounted V2X communication device; and
the vehicle-mounted V2X communication device is configured to transmit the combined sensed information to a vehicle-mounted automatic driving system.
| 2. The automatic driving assistance system of claim 1, wherein the roadside V2X communication device is configured to encapsulate the combined sensed information into a V2X communication message and transmit the V2X communication message to an air interface; and
wherein the vehicle-mounted V2X communication device is configured to receive the V2X communication message from the air interface, parse the V2X communication message to obtain the combined sensed information, and transmit the combined sensed information to the vehicle-mounted automatic driving system.
| 3. The automatic driving assistance system of claim 2, wherein the roadside device comprises a roadside communication interface, and the vehicle-mounted automatic driving system comprises a vehicle-mounted communication interface, and wherein:
the roadside communication interface is configured to transmit the combined sensed information to the roadside V2X communication device;
the vehicle-mounted V2X communication device transmitting the combined sensed information to the vehicle-mounted automatic driving system comprises transmitting the combined sensed information to the vehicle-mounted communication interface; and
the vehicle-mounted communication interface is configured to transmit the combined sensed information to respective functional modules of the vehicle-mounted automatic driving system.
| 4. The automatic driving assistance system of claim 3, wherein:
the roadside communication interface is configured to encapsulate the combined sensed information into a Transmission Control Protocol (TCP)/User Datagram Protocol (UDP) message and transmit the TCP/UDP message to the roadside V2X communication device;
the roadside V2X communication device encapsulating the combined sensed information into the V2X communication message comprises: parsing the TCP/UDP message received from the roadside communication interface to obtain the combined sensed information and encapsulating the combined sensed information into the V2X communication message;
the vehicle-mounted V2X communication device transmitting the combined sensed information to the vehicle-mounted communication interface comprises: encapsulating the combined sensed information into a TCP/UDP message and transmitting the TCP/UDP message to the vehicle-mounted communication interface; and
the vehicle-mounted communication interface is configured to parse the TCP/UDP message received from the vehicle-mounted V2X communication device to obtain the combined sensed information.
| 5. The automatic driving assistance system of claim 4, wherein the roadside communication interface encapsulating the combined sensed information into the TCP/UDP message comprises:
serializing the combined sensed information in accordance with a predetermined serialization mechanism to obtain serial binary data; and
encapsulating the serial binary data as payload data in the TCP/UDP message; and
wherein the vehicle-mounted communication interface parsing the TCP/UDP message received from the vehicle-mounted V2X communication device to obtain the combined sensed information comprises:
removing TCP/Internet Protocol (IP) protocol stack format information;
extracting the payload data from the TCP/UDP message; and
deserializing the payload data in accordance with a predetermined deserialization mechanism to obtain the combined sensed information.
| 6. The automatic driving assistance system of claim 3, wherein the roadside communication interface is configured to transmit the combined sensed information to the roadside V2X communication device via a Universal Serial Bus (USB) interface or a serial interface; and
the vehicle-mounted V2X communication device transmitting the combined sensed information to the vehicle-mounted communication interface comprises: transmitting the combined sensed information to the vehicle-mounted communication interface via a USB interface or a serial interface.
| 7. The automatic driving assistance system of claim 3, wherein each of the roadside device and the automatic driving system is based on a framework comprising open-source resources and configured to support data sharing;
the roadside device further comprises driving nodes each corresponding to one of the plurality of roadside sensors;
each of the driving nodes treats the environment information collected by its corresponding roadside sensor as a message and posts the message in form of a topic.
| 8. The automatic driving assistance system of claim 1, wherein each of the plurality of roadside sensors comprises one of: a camera, a laser radar, a millimeter wave radar, a positioning sensor, an illumination sensor, a temperature sensor, or a humidity sensor.
| 9. A roadside assistance system, comprising a plurality of roadside sensors, a roadside device, and a roadside Vehicle to Everything (V2X) communication device, wherein:
each of the plurality of roadside sensors is configured to collect environment information of an ambient environment and transmit the environment information to the roadside device;
the roadside device is configured to:
generate a plurality of messages from the environment information collected by the plurality of roadside sensors, the plurality of messages including a plurality of pieces of sensed information, each piece of sensed information being generated from the environment information collected by a respective one of the plurality of roadside sensors,
combine the plurality of messages generated from the environment information collected by the plurality of roadside sensors into a single message at least by removing redundant sensed information based on a confidence level associated with each piece of sensed information, the single message comprising combined sensed information, and
transmit the combined sensed information to the roadside V2X communication device; and
the roadside V2X communication device is configured to transmit the combined sensed information to a vehicle-mounted V2X communication device.
| 10. The roadside assistance system of claim 9, wherein the roadside V2X communication device is configured to encapsulate the combined sensed information into a V2X communication message and transmit the V2X communication message to an air interface.
| 11. The roadside assistance system of claim 10, wherein the roadside device comprises a roadside communication interface, and wherein the roadside communication interface is configured to transmit the combined sensed information to the roadside V2X communication device.
| 12. The roadside assistance system of claim 11, wherein the roadside communication interface is configured to encapsulate the combined sensed information into a Transmission Control Protocol (TCP)/User Datagram Protocol (UDP) message and transmit the TCP/UDP message to the roadside V2X communication device; and
wherein the roadside V2X communication device encapsulating the combined sensed information into the V2X communication message comprises:
parsing the TCP/UDP message received from the roadside communication interface to obtain the combined sensed information; and
encapsulating the combined sensed information into the V2X communication message.
| 13. The roadside assistance system of claim 12, wherein the roadside communication interface encapsulating the combined sensed information into the TCP/UDP message comprises:
serializing the combined sensed information in accordance with a predetermined serialization mechanism to obtain serial binary data; and
encapsulating the serial binary data as payload data in the TCP/UDP message.
| 14. The roadside assistance system of claim 11, wherein the roadside communication interface is configured to transmit the combined sensed information to the roadside V2X communication device via a Universal Serial Bus (USB) interface or a serial interface.
| 15. The roadside assistance system of claim 11, wherein:
the roadside device is based on a framework comprising open-source resources and configured to support data sharing;
the roadside device further comprises driving nodes each corresponding to one of the plurality of roadside sensors; and
each of the driving nodes treats the environment information collected by its corresponding roadside sensor as a message and posts the message in form of a topic.
| 16. The roadside assistance system of claim 9, wherein each of the plurality of roadside sensors comprises one: a camera, a laser radar, a millimeter wave radar, a positioning sensor, an illumination sensor, a temperature sensor, or a humidity sensor.
| 17. A vehicle-mounted assistance system, comprising a vehicle-mounted Vehicle to Everything (V2X) communication device connected to a vehicle-mounted automatic driving system, wherein:
the vehicle-mounted V2X communication device is configured to receive combined sensed information from a roadside V2X communication device and transmit the combined sensed information to the vehicle-mounted automatic driving system,
the roadside V2X communication device is coupled to a plurality of roadside sensors, wherein each of the plurality of roadside sensors is configured to collect environment information of an ambient environment and transmit the environment information to a roadside device, and
the roadside V2X communication device is coupled to the roadside device that is configured to:
generate a plurality of messages from the environment information collected by the plurality of roadside sensors, the plurality of messages including a plurality of pieces of sensed information, each piece of sensed information being generated from the environment information collected by a respective one of the plurality of roadside sensors, and
combine the plurality of messages generated from the environment information collected by the plurality of roadside sensors into a single message at least by removing redundant sensed information based on a confidence level associated with each piece of sensed information, the single message comprising combined sensed information.
| 18. The vehicle-mounted assistance system of claim 17, wherein the vehicle-mounted V2X communication device is configured to receive the V2X communication message transmitted from the roadside V2X communication device from an air interface, parse the V2X communication message to obtain the combined sensed information and transmit the combined sensed information to the vehicle-mounted automatic driving system.
| 19. The vehicle-mounted assistance system of claim 18, wherein the vehicle-mounted automatic driving system comprises a vehicle-mounted communication interface, and wherein:
the vehicle-mounted V2X communication device transmitting the combined sensed information to the vehicle-mounted automatic driving system comprises transmitting the combined sensed information to the vehicle-mounted communication interface; and
the vehicle-mounted communication interface is configured to transmit the combined sensed information to respective functional modules of the vehicle-mounted automatic driving system.
| 20. The vehicle-mounted assistance system of claim 19, wherein the vehicle-mounted V2X communication device transmitting the combined sensed information to the vehicle-mounted communication interface comprises: encapsulating the combined sensed information into a Transmission Control Protocol (TCP)/User Datagram Protocol (UDP) message and transmitting the TCP/UDP message to the vehicle-mounted communication interface; and
wherein the vehicle-mounted communication interface is configured to parse the TCP/UDP message received from the vehicle-mounted V2X communication device to obtain the combined sensed information.
| 21. The vehicle-mounted assistance system of claim 20, wherein the vehicle-mounted communication interface parsing the TCP/UDP message received from the vehicle-mounted V2X communication device to obtain the combined sensed information comprises:
removing TCP/Internet Protocol (IP) protocol stack format information and extracting the payload data from the TCP/UDP message; and
deserializing the payload data in accordance with a predetermined deserialization mechanism to obtain the combined sensed information.
| 22. The vehicle-mounted assistance system of claim 19, wherein the vehicle-mounted V2X communication device transmitting the combined sensed information to the vehicle-mounted communication interface comprises transmitting the combined sensed information to the vehicle-mounted communication interface via a Universal Serial Bus (USB) interface or a serial interface.
| 23. The vehicle-mounted assistance system of claim 19, wherein the automatic driving system is based on a framework comprising open-source resources and configured to support data sharing; and
wherein the vehicle-mounted communication interface treats the combined sensed information as a message and posts the message in form of a topic. | The system has a road side assistance system connected with road side sensors, a road side unit, a road side terminal and a vehicle-V2X communicating device. A vehicle terminal is connected with an auxiliary system that is provided with the vehicle-V2X communicating device. The road side sensor collects environmental information of a periphery device when the environment information to a road side device. The vehicle-V2X communicating device converts received sensing information when the received sensing information is transmitted to the vehicle-V2X communicating device. The vehicle-V2X communicating device transmits the received sensing information to the automatic driving system. INDEPENDENT CLAIMS are also included for the following:a road side auxiliary systema vehicle mounted auxiliary system. Automatic driving assistance system. The drawing shows a block diagram of an automatic driving assistance system. '(Drawing includes non-English language text)' |
Please summarize the input | Cellular systemA system includes a cellular transceiver to communicate with a predetermined target; one or more antennas coupled to the 5G or 6G transceiver each electrically or mechanically steerable to the predetermined target; a processor to control a directionality of the one or more antennas in communication with the predetermined target; and an edge processing module coupled to the processor and the one or more antennas to provide low-latency computation for the predetermined target.What is claimed is:
| 1. A system to perform edge processing for a predetermined target, comprising:
one or more cellular transceivers with one or more antennas that are electrically or mechanically steerable to the predetermined target;
a processor to control communication with the predetermined target; and
one or more edge processing modules coupled to the processor and the one or more antennas to provide low-latency computation for the predetermined target; and
a container to house the transceiver, processor running a virtual radio access network, and one or more edge processing modules, the container including a heat spreader coupled to the transceiver.
| 2. The system of claim 1, wherein the container is deployed without a construction permit.
| 3. The system of claim 1, wherein the processor is coupled to fiber optics cable to communicate with a cloud-based radio access network (RAN) or a remote RAN.
| 4. The system of claim 1, comprising an antenna mast, wherein the antenna mast is inside the container or external to the container.
| 5. The system of claim 1, wherein the edge processing module comprises at least a processor, a graphical processing unit (GPU), a neural network, a statistical engine, or a programmable logic device (PLD).
| 6. The system of claim 1, wherein the edge processing module and the antenna comprise one unit.
| 7. The system of claim 1, comprising a cryogenic cooling system to cool the container.
| 8. The system of claim 1, wherein the cellular transceiver comprises a 5G or 6G transceiver.
| 9. The system of claim 1, wherein the processor coordinates beam sweeping by the one or more antennas with radio nodes or user equipment (UE) devices based upon service level agreement, performance requirement, traffic distribution data, networking requirements or prior beam sweeping history.
| 10. The system of claim 9, wherein the beam sweeping is directed at a group of autonomous vehicles, a group of virtual reality devices, or a group of devices having a service agreement with a cellular provider.
| 11. The system of claim 1, comprising a neural network coupled to a control plane, a management plane, or a data plane to optimize 5G or 6G parameters.
| 12. The system of claim 1, comprising one or more cameras and sensors to capture security information.
| 13. The system of claim 1, wherein the container includes edge sensors including LIDAR and RADAR.
| 14. The system of claim 1, comprising a camera for individual identity identification.
| 15. The system of claim 1, wherein the edge processing module streams data to the predetermined target to minimize loading the target.
| 16. The system of claim 1, wherein the edge processing module shares workload with a core processing module located at a head-end and a cloud module located at a cloud data center, each processing module having increased latency and each having a processor, a graphical processing unit (GPU), a neural network, a statistical engine, or a programmable logic device (PLD).
| 17. The system of claim 1, comprising an edge learning machine in the housing to provide local edge processing for Internet-of-Things (IOT) sensors with reduced off-chip memory access.
| 18. The system of claim 17, wherein the edge learning machine uses pre-trained models and modifies the pre-trained models for a selected task.
| 19. The system of claim 1, comprising a cellular device for a person crossing a street near a city light or street light, the cellular device emitting a person to vehicle (P2V) or a vehicle to person (V2P) safety message.
| 20. The system of claim 1, comprising a cloud trained neural network whose network parameters are down-sampled and filter count reduced before transferring to the edge neural network. | System comprises one or more cellular transceivers with one or more antennas that are electrically or mechanically steerable to predetermined target. The processor to control communication with the predetermined target. The one or more edge processing modules coupled to the processor and the one or more antennas to provide low-latency computation for the predetermined target. The container to house the transceiver, processor running a virtual radio access network, and one or more edge processing modules, the container including a heat spreader coupled to the transceiver. System that is used for performing edge processing for predetermined target (claimed). The system includes a cellular transceiver to communicate with a predetermined target, one or more antennas coupled to the 5G or 6G transceiver each electrically or mechanically steerable to the predetermined target, a processor to control a directionality of the one or more antennas in communication with the predetermined target, and an edge processing module coupled to the processor and the one or more antennas to provide low-latency computation for the predetermined target. The drawing shows a schematic view of an active antenna system. |
Please summarize the input | Cellular systemA system includes a housing with one or more edge processors to handle processing on behalf of a mobile target or to provide local data to the mobile target or to provide artificial intelligence for the mobile target; one or more antennas coupled to the housing; and a processor to control a directionality of the antennas in communication with the mobile target using 5G or 6G protocols.What is claimed is:
| 1. A system, comprising:
a mobile target to receive road or traffic parameters from one or more traffic sensors, road sensors or cameras;
a housing with one or more edge processors coupled to the one or more traffic sensors, road sensors or cameras to handle processing on behalf of the mobile target with a predetermined latency to provide augmented or virtual reality rendering data to the mobile target or to provide artificial intelligence for the mobile target, wherein the mobile target optimizes performance and power consumption by offloading augmented or virtual processing to the one or more edge processors and applying a received edge processing result within the predetermined latency to augment processing by the mobile target; one or more antennas coupled to the housing; and
a processor to communicate with the mobile target using 5G protocols.
| 2. The system of claim 1, wherein the processor calibrates a radio link between a transceiver in the housing and a client device.
| 3. The system of claim 1, wherein local data comprises images and wherein the processor process images from one or more mobile target cameras for location identification, ridesharing pick-up, or delivery.
| 4. The system of claim 1, wherein local data comprises images and wherein the one or more edge processors detect real time hazard detection or road signs.
| 5. The system of claim 1, wherein the processor moves actuators coupled to the antennas.
| 6. The system of claim 1, wherein local data comprises weather or location data.
| 7. The system of claim 1, wherein the one or more edge processors handle video content, healthcare, robotics, autonomous vehicle, augmented reality, virtual reality, extended reality, factory automation, gaming, asset tracking, or surveillance.
| 8. The system of claim 1, wherein the mobile target receives high definition local road map data from the edge processors.
| 9. The system of claim 1, wherein the local data comprises data affecting road conditions, and wherein the one or more edge processors provide traffic, transit, search, routing, telematics, weather, tracking, positioning, high-definition map, or geoenrichment data.
| 10. The system of claim 1, wherein the one or more edge processors comprise one or more learning machines or neural networks.
| 11. The system of claim 1, comprising one or more cameras and sensors in the housing to capture security information.
| 12. The system of claim 1, wherein the one or more edge processors perform predictive analytics, consumer targeting, fraud detection, or demand forecast.
| 13. The system of claim 1, comprising a camera and a processor for individual identity identification.
| 14. The system of claim 1, wherein the one or more edge processors applies artificial intelligence to location data.
| 15. The system of claim 1, wherein the one or more edge processors analyze sound, scent, or chemical data from sensors in the housing.
| 16. The system of claim 1, comprising an edge learning machine in the housing to provide local edge processing for one or more Internet-of-Things (IOT) sensors.
| 17. The system of claim 1, comprising an edge learning machine that uses pre-trained models and modifies the pre-trained models for a selected task.
| 18. The system of claim 1, comprising a cellular device for a person crossing a street near a city light or street light, the cellular device emitting a person to vehicle (P2V) or a vehicle to person (V2P) safety message.
| 19. A system for A system for real-time resource allocation in a wireless network, comprising:
a mobile target to receive parameters from one or more sensors;
a housing with one or more edge processors coupled to one or more sensors to handle processing on behalf of a target with a predetermined latency or to provide artificial intelligence for a target;
determining resource allocation in response to numbers of users and use cases on the wireless network;
applying artificial intelligence (AI) to allocate resources based on real-time demand and network conditions for beam management, spectrum allocation, and scheduling function to handle resource allocation demands and resource utilization with AI to process data with a predetermined latency;
one or more antennas coupled to the housing; and
a processor to communicate with the mobile target using 5G protocols.
| 20. A method in a wireless network, comprising:
receiving traffic parameters from one or more sensors or cameras;
with one or more edge processors coupled to the one or more sensors or cameras, processing on behalf of a target with a predetermined latency of providing artificial intelligence operation for the target;
determining resource allocation in response to numbers of users and use cases on the wireless network; and
applying AI to a physical layer (PHY) to perform digital predistortion, channel estimation, and channel resource optimization;
adjusting transceiver parameters for optimizing resource allocation with the applied AI at the PHY. | System comprises a mobile target to receive road or traffic parameters from traffic sensors, road sensors, or cameras, a housing with edge processors coupled to the traffic sensors, road sensors, or cameras to handle processing on behalf of the mobile target with a predetermined latency to provide reality rendering data to the mobile target or to provide artificial intelligence for the mobile target. The mobile target optimizes performance and power consumption by offloading processing to the edge processors and applying a received edge processing result within the predetermined latency to augment processing by the mobile target. Antennas (11) are coupled to the housing. A processor is provided to communicate with the mobile target using fifth-generation or sixth-generation protocols. System, preferably cellular system e.g. fifth generation (5G) cellular system, long term evolution system and worldwide interoperability for microwave access system. The liquid lens antenna system comprises a liquid lens that is provided with a moveable surface, where liquid is added or removed to adjust the curvature of the movable surface and an antenna is mounted on the moveable surfaces to change a direction of the antenna to a predetermined target, and thus enables to improve the signal-to-noise ratio of the wireless communication system. The drawing shows a schematic view of the man-hole cover with a small cell and steerable antennas.11Antennas 22, 24Latches |
Please summarize the input | Cellular systemA system includes a housing with one or more edge processors to handle processing on behalf of a mobile target or to provide local data to the mobile target or to provide artificial intelligence for the mobile target; one or more antennas coupled to the housing; and a processor to control a directionality of the antennas in communication with the mobile target using 5G or 6G protocols.What is claimed is:
| 1. A system, comprising:
a housing with one or more edge processors to handle processing on behalf of a mobile target with a predetermined latency to provide reality rendering data to the mobile target or to provide artificial intelligence for the mobile target, wherein the mobile target optimizes performance and power consumption by offloading processing to the one or more edge processors and applying a received edge processing result within the predetermined latency to augment processing by the mobile target;
one or more sensors to capture local data;
one or more antennas coupled to the housing; and
a processor to communicate with the mobile target using 5G or 6G protocols.
| 2. The system of claim 1, wherein the processor calibrates a radio link between a transceiver in the housing and a client device.
| 3. The system of claim 1, wherein the local data comprises images and wherein the processor process images from one or more mobile target cameras for location identification, ridesharing pick-up, or delivery.
| 4. The system of claim 1, wherein the local data comprises images and wherein the one or more edge processors detect real time hazard detection or road signs.
| 5. The system of claim 1, wherein the processor moves actuators coupled to the antennas.
| 6. The system of claim 1, wherein the local data comprises weather or location data.
| 7. The system of claim 1, wherein the one or more edge processors handle video content, healthcare, robotics, autonomous vehicle, augmented reality, virtual reality, extended reality, factory automation, gaining, asset tracking, or surveillance.
| 8. The system of claim 1, wherein the mobile target comprises plant or manufacturing equipment.
| 9. The system of claim 1, wherein the local data comprises data affecting road conditions, and wherein the one or more edge processors provide traffic, transit, search, routing, telematics, weather, tracking, positioning, high-definition map, or geo-enrichment data.
| 10. The system of claim 1, wherein processor focuses 5G signals to the target with iterative changes in electrical or mechanical orientation of the one or more antennas.
| 11. The system of claim 1, wherein the edge processors comprise one or more learning machines or neural networks.
| 12. The system of claim 1, comprising one or more cameras and sensors in the housing to capture security information.
| 13. The system of claim 1, wherein the one or more edge processors perform predictive analytics, consumer targeting, fraud detection, or demand forecast.
| 14. The system of claim 1, comprising a camera and a processor for individual identity identification.
| 15. The system of claim 1, wherein the one or more edge processors applies artificial intelligence to location data.
| 16. The system of claim 1, wherein the one or more edge processors analyze sound, scent, or chemical data from sensors in the housing.
| 17. The system of claim 1, comprising an edge learning machine in the housing to provide local edge processing for one or more Internet-of-Things (IOT) sensors.
| 18. The system of claim 1, comprising an edge learning machine that uses pre-trained models and modifies the pre-trained models for a selected task.
| 19. The system of claim 1, comprising a cellular device for a person crossing a street near the city light or street light, the cellular device emitting a person to vehicle (P2V) or a vehicle to person (V2P) safety message.
| 20. The system of claim 1, comprising a cloud trained neural network whose network parameters are reduced before transfer to the edge neural network.
| 21. A processing method, comprising:
wirelessly receiving at a pole, tower, or container a remote processing request from a mobile device with a predetermined latency;
capturing local data with sensors coupled to the pole, tower, or container;
offloading processing of the remote processing request with the local data to the one or more edge processors;
completing the remote processing request by receiving a result from one or more edge processors at the pole, tower, or container within the predetermined latency and applying the result to augment processing by the mobile device to provide reality rendering data to the mobile target or to augment artificial intelligence processing for the mobile target while optimizing performance and power consumption by the mobile device. | The cellular system includes a housing with one or more edge processors to handle processing on behalf of a mobile target or to provide local data to the mobile target or to provide artificial intelligence for the mobile target. One or more antennas (11) are coupled to the housing. A processor controls a directionality of the antennas in communication with the mobile target using 5G or 6G protocols. Cellular system, e.g., manhole cover or security camera. The receive and transmit digital beam former (DBF) coefficients are adjusted to help maintain an improved or maximum signal quality, to help reduce or minimize in-band interference and to help maximize receive power level. By integrating the remote radio head functionality into the antenna, the esthetics of the site can be improved and wind load reduced, resulting in lower leasing and installation costs. The drawing shows views of a manhole cover with a small cell and steerable antennas. 4Manhole cover body11Antennas22Latches26CLatching control surface30Bands |
Please summarize the input | WIRELESS SYSTEMA system includes a transceiver to communicate with a predetermined target; one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target; and an edge processing module coupled to the transceiver and one or more antennas to provide low-latency computation for the predetermined target.What is claimed is:
| 1. A system, comprising:
a transceiver to communicate with a predetermined target;
one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target;
artificial intelligence (AI) software to allocate slices of frequencies to different wireless devices to optimize frequency utilization across available bandwidth; and
an edge processing module coupled to the transceiver and one or more antennas to provide low-latency computation for the predetermined target.
| 2. The system of claim 1, comprising a quantum computer coupled to the edge processing module.
| 3. The system of claim 1, comprising a parser that receives classical specification and data and determines if a portion of such specification runs on a quantum computer, and if so maps classical specification to quantum algorithm and the modified code is provided to an execution unit that selects one or more quantum computers, one or more classical processor, one or more graphical processing units (GPUs), or one or more neuromorphic processors.
| 4. The system of claim 1, wherein the processor calibrates a connection by analyzing RSSI and TSSI and moves the antennas until predetermined cellular parameters are reached.
| 5. The system of claim 1, wherein the edge processing module comprises at least a processor, a graphical processing unit (GPU), a neural network, a quantum computer, a statistical engine, or a programmable logic device (PLD).
| 6. The system of claim 1, wherein the edge processing module and the antenna are enclosed in a housing or shipping container, or the edge processing module is in a separate shipping container adjacent the antenna.
| 7. The system of claim 1, wherein the transceiver comprises a 5G or 6G cellular transceiver.
| 8. The system of claim 1, wherein the edge processing module communicates at a plurality of AI selected frequency with AI selected frequency hopping to use a full frequency allocation.
| 9. A system, comprising:
a transceiver to communicate with a predetermined target;
one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target; and
a beam sweeping module controlling the antenna in accordance with one of: artificial intelligence frequency hopping selection to maximize bandwidth allocation, a service level agreement, a performance requirement, a traffic distribution data, a networking requirement or prior beam sweeping history.
| 10. The system of claim 9, wherein the beam sweeping is directed at a group of autonomous vehicles, a group of virtual reality devices, or a group of devices performing similar functions.
| 11. The system of claim 1, comprising a neural network coupled to a control plane, a management plane, and a data plane to optimize 5G parameters.
| 12. The system of claim 1, comprising one or more cameras and sensors in the housing to capture security information.
| 13. The system of claim 1, comprising edge sensors including LIDAR and RADAR.
| 14. The system of claim 1, comprising a camera for individual identity identification.
| 15. The system of claim 1, wherein the edge processing module streams data to the predetermined target to minimize loading the target.
| 16. The system of claim 1, wherein the edge processing module shares workload with a core processing module located at a head-end and a cloud module located at a cloud data center, each processing module having increased latency and each having a processor, a graphical processing unit (GPU), a neural network, a quantum computer, a statistical engine, or a programmable logic device (PLD).
| 17. The system of claim 1, comprising an edge learning machine in a housing or shipping container to provide local edge processing for Internet-of-Things (JOT) sensors.
| 18. The system of claim 17, wherein the edge learning machine uses pre-trained models and modifies the pre-trained models for a selected task.
| 19. The system of claim 1, comprising a cellular device for a person crossing a street near a city light or street light, the cellular device emitting a person to vehicle (P2V) or a vehicle to person (V2P) safety message.
| 20. The system of claim 1, comprising a cloud trained neural network whose network parameters are down-sampled and filter count reduced before transferring to the edge neural network. | System has a transceiver (124) to communicate with a predetermined target, and a set of antennas coupled to the transceiver, where each antenna (11) is electrically or mechanically steerable to the predetermined target. An artificial intelligence (AI) software allocates slices of frequencies to different wireless devices to optimize frequency utilization across available bandwidth. An edge processing module e.g. neural network (102), is coupled to transceiver and the antennas to provide low-latency computation for the target. A parser receives classical specification and data and determines if a portion of the specification runs on a quantum computer. System for providing local edge processing for Internet-of-Things (JOT) sensors with reduced off-chip memory access. The system allows a fleet of drones to operate and navigate as a flock of birds to provide real-time adjustment in coverage as needed. The system provides power and autonomous navigation and self-assemble and scatter as needed to avoid physical and wireless communication obstacles. Preferred Components: The edge processing module comprises at least a processor, a graphical processing unit (GPU), a neural network, a quantum computer, a statistical engine, or a programmable logic device (PLD). The transceiver comprises a fifth-generation (5G) or sixth-generation (6G) cellular transceiver. The edge processing module communicates at a plurality of artificial intelligence (AI) selected frequency with AI selected frequency hopping to use a full frequency allocation. The edge sensors including light detection and ranging (LIDAR) and radio detection and ranging (RADAR). The drawing shows an exemplary fourth generation-fifth generation network.11Antenna 102Network 106Communications tower 110Technician 124Transceiver |
Please summarize the input | Cellular communicationA system includes a cellular transceiver to communicate with a predetermined target; one or more antennas coupled to the 5G or 6G transceiver each electrically or mechanically steerable to the predetermined target; a processor to control a directionality of the one or more antennas in communication with the predetermined target; and an edge processing module coupled to the processor and the one or more antennas to provide low-latency computation for the predetermined target.What is claimed is:
| 1. A system to perform edge processing for a predetermined target, comprising:
one or more cellular transceivers with one or more antennas that are electrically or mechanically steerable to the predetermined target;
a processor to control communication with the predetermined target; and
one or more edge processing modules coupled to the processor and the one or more antennas to provide low-latency computation for the predetermined target; and
a container to house the transceiver, processor running a virtual radio access network, and one or more edge processing modules, the container moveable to a location requiring increased edge processing.
| 2. The system of claim 1, wherein the container fits requirement to be deployed without a construction permit.
| 3. The system of claim 1, wherein the processor is coupled to fiber optics cable to communicate with a cloud-based radio access network (RAN) or a remote RAN.
| 4. The system of claim 1, comprising an antenna mast, wherein the antenna mast is inside the container or external to the container.
| 5. The system of claim 1, wherein the edge processing module comprises at least a processor, a graphical processing unit (GPU), a neural network, a statistical engine, or a programmable logic device (PLD).
| 6. The system of claim 1, wherein the edge processing module and the antenna comprise one unit.
| 7. The system of claim 1, comprising a cryogenic cooling system to cool the container.
| 8. The system of claim 1, wherein the cellular transceiver comprises a 5G or 6G transceiver.
| 9. The system of claim 1, wherein the processor coordinates beam sweeping by the one or more antennas with radio nodes or user equipment (UE) devices based upon service level agreement, performance requirement, traffic distribution data, networking requirements or prior beam sweeping history.
| 10. The system of claim 9, wherein the beam sweeping is directed at a group of autonomous vehicles, a group of virtual reality devices, or a group of devices having a service agreement with a cellular provider.
| 11. The system of claim 1, comprising a neural network coupled to a control plane, a management plane, or a data plane to optimize 5G or 6G parameters.
| 12. The system of claim 1, comprising one or more cameras and sensors to capture security information.
| 13. The system of claim 1, wherein the container includes edge sensors including LIDAR and RADAR.
| 14. The system of claim 1, comprising a camera for individual identity identification.
| 15. The system of claim 1, wherein the edge processing module streams data to the predetermined target to minimize loading the target.
| 16. The system of claim 1, wherein the edge processing module shares workload with a core processing module located at a head-end and a cloud module located at a cloud data center, each processing module having increased latency and each having a processor, a graphical processing unit (GPU), a neural network, a statistical engine, or a programmable logic device (PLD).
| 17. The system of claim 1, comprising an edge learning machine in the housing to provide local edge processing for Internet-of-Things (IOT) sensors with reduced off-chip memory access.
| 18. The system of claim 17, wherein the edge learning machine uses pre-trained models and modifies the pre-trained models for a selected task.
| 19. The system of claim 1, comprising a cellular device for a person crossing a street near a city light or street light, the cellular device emitting a person to vehicle (P2V) or a vehicle to person (V2P) safety message.
| 20. The system of claim 1, comprising a cloud trained neural network whose network parameters are reduced before transferring to an edge neural network in the container. | System (1) comprises one or more cellular transceivers with one or more antennas that are electrically or mechanically steerable to the predetermined target, a processor to control communication with the predetermined target; and one or more edge processing modules coupled to the processor and the one or more antennas to provide low-latency computation for the predetermined target, and a container to house the transceiver, processor running a virtual radio access network, and one or more edge processing modules, the container including a heat spreader coupled to the transceiver. System for performing edge processing for predetermined target. The 5G or 6G transceiver can be part of a portable computer, laptop computer, tablet computer, brief case, or any utensil/appliance that can be away from the body to reduce RF energy on the human body, but still linked to the display and mike/speaker to act as a UI for the user. The drawing shows an exemplary city light small cell environment with crime/pollution sniffing capabilities.1System 10Computing unit 11Light post 18Manhole cover support surface 19User interface |
Please summarize the input | Computing systemA system includes a transceiver to communicate with a predetermined target; one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target; and an edge processing module coupled to the transceiver and one or more antennas to provide low-latency computation for the predetermined target.What is claimed is:
| 1. A system, comprising:
a transceiver to communicate with a predetermined target;
one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target;
an edge processing module coupled to the transceiver and one or more antennas to provide low-latency computation for the predetermined target; and
a parser that receives classical specification and data and determines if a portion of such specification runs on a quantum computer, and if so maps classical specification to quantum algorithm and selects code execution from one or more quantum computers, one or more classical processor, one or more graphical processing units (GPUs), or one or more neuromorphic processors.
| 2. The system of claim 1, wherein the transceiver comprises a 5G or 6G cellular transceiver.
| 3. A system, comprising:
a transceiver to communicate with a predetermined target;
one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target; and
a beam sweeping module controlling the antenna in accordance with one of: a service level agreement, a performance requirement, a traffic distribution data, a networking requirement or prior beam sweeping history; and
an edge processing module shares workload with a core processing module located at a head-end and a cloud module located at a cloud data center, each processing module having increased latency and each having a processor, a graphical processing unit (GPU), a neural network, a quantum computer, a statistical engine, or a programmable logic device (PLD).
| 4. The system of claim 3, wherein the beam sweeping is directed at a group of autonomous vehicles, a group of virtual reality devices, or a group of devices performing similar functions.
| 5. A system, comprising:
a transceiver to communicate with a predetermined target;
one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target; and
an edge processing module coupled to the transceiver and one or more antennas to provide low-latency computation for the predetermined target; and
a neural network or a learning machine coupled to a control plane, a management
plane, and a data plane to optimize 5G parameters.
| 6. The system of claim 5, comprising a quantum computer coupled to the edge processing module.
| 7. A system, comprising:
a transceiver to communicate with a predetermined target;
one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target; and
an edge processing module coupled to the transceiver and one or more antennas to provide low-latency computation for the predetermined target, wherein the edge processing module shares workload with a core processing module located at a head-end and a cloud module located at a cloud data center, each processing module having increased latency and each having a processor, a graphical processing unit (GPU), a neural network, a quantum computer, a statistical engine, or a programmable logic device (PLD).
| 8. The system of claim 7, wherein the processor calibrates a connection by analyzing RSSI and TSSI and moves the antennas until predetermined cellular parameters are reached.
| 9. A system, comprising:
a transceiver to communicate with a predetermined target;
one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target; and
an edge processing module coupled to the transceiver and one or more antennas to provide low-latency computation for the predetermined target; and
a cloud trained neural network whose network parameters are down-sampled or filter count reduced before transferring to the edge neural network.
| 10. The system of claim 9, wherein the edge processing module comprises at least a processor, a graphical processing unit (GPU), a neural network, a quantum computer, a statistical engine, or a programmable logic device (PLD).
| 11. The system of claim 9, wherein the edge processing module and the antenna are enclosed in a housing or shipping container, or the edge processing module is in a separate shipping container adjacent the antenna.
| 12. The system of claim 9, wherein the edge processing module shares workload with a core processing module located at a head-end and a cloud module located at a cloud data center, each processing module having increased latency and each having a processor, a graphical processing unit (GPU), a neural network, a quantum computer, a statistical engine, or a programmable logic device (PLD).
| 13. The system of claim 9, comprising one or more cameras and sensors in the housing to capture security information.
| 14. The system of claim 9, comprising edge sensors including LIDAR and RADAR.
| 15. The system of claim 9, comprising a camera for individual identity identification.
| 16. The system of claim 9, wherein the edge processing module streams data to the predetermined target to minimize loading the target.
| 17. The system of claim 9, comprising an edge learning machine in a housing or shipping container to provide local edge processing for Internet-of-Things (TOT) sensors.
| 18. The system of claim 17, wherein the edge learning machine uses pre-trained models and modifies the pre-trained models for a selected task.
| 19. The system of claim 9 comprising a cellular device for a person crossing a street near a city light or street light, the cellular device emitting a person to vehicle (P2V) or a vehicle to person (V2P) safety message.
| 20. The system of claim 9, wherein the edge processing module comprises a learning machine, a neural network, a quantum computer, a statistical engine, or a programmable logic device (PLD). | The system has a transceiver to communicate with a predetermined target, and antennas (11) coupled to the transceiver, where each antenna is electrically or mechanically steerable to the predetermined target. An edge processing module e.g. neural network, is coupled to transceiver and the antennas to provide low-latency computation for the target. A parser receives classical specification and data, and determines if a portion of the specification runs on a quantum computer. A beam sweeping module controls the antenna in accordance with one of a service level agreement, a performance requirement, a traffic distribution data, a networking requirement or prior beam sweeping history. System for providing local edge processing for internet-of-things (IOT) sensors with reduced off-chip memory access and low latency. Uses include but are not limited to light poles, top of buildings, street lights, autonomous vehicles and virtual reality devices. The system allows a fleet of drones to operate and navigate as a flock of birds to provide real-time adjustment in coverage as needed. The system uses smart antenna techniques to support higher data rate and coverage in an effective manner. The flock of bird antenna has power and autonomous navigation and can self-assemble and scatter as needed to avoid physical and wireless communication obstacles. The drawing shows a schematic view of an exemplary man-hole cover with a small cell and steerable antennas.11Antenna 30Bands |
Please summarize the input | Cellular systemA system includes a housing with one or more edge processors to handle processing on behalf of a mobile target or to provide local data to the mobile target or to provide artificial intelligence for the mobile target; one or more antennas coupled to the housing; and a processor to control a directionality of the antennas in communication with the mobile target using 5G or 6G protocols.What is claimed is:
| 1. A system to communicate a local data with a mobile target, the local data including road or traffic parameters from one or more remote traffic sensors, road sensors or cameras, the system comprising:
a fixed position housing with one or more edge processors wirelessly coupled to the one or more traffic sensors, road sensors or cameras to handle processing on behalf of the mobile target with a predetermined latency to provide virtual, augment or extended reality (AR/VR/XR) processing to the mobile target or to provide artificial intelligence (AI) processing for the mobile target, wherein the mobile target offloads the AR/VR/XR or AI processing as applied to the local data to the one or more edge processors and wherein the mobile target applies a received edge processing result within the predetermined latency to augment processing by the mobile target; and
one or more antennas coupled to the housing to communicate with the mobile target using 5G or 6G protocols.
| 2. The system of claim 1, wherein the processor calibrates a radio link between a transceiver in the housing and a client device.
| 3. The system of claim 1, wherein the local data comprises images and wherein the processor process images from one or more mobile target cameras for location identification, ridesharing pick-up, or delivery.
| 4. The system of claim 1, wherein the local data comprises images and wherein the one or more edge processors detect real time hazard detection or road signs.
| 5. The system of claim 1, wherein the processor moves actuators coupled to the antennas.
| 6. The system of claim 1, wherein the local data comprises weather or location data.
| 7. The system of claim 1, wherein the one or more edge processors handle video content, healthcare, robotics, autonomous vehicle, augmented reality, virtual reality, extended reality, factory automation, gaining, asset tracking, or surveillance.
| 8. The system of claim 1, wherein the mobile target receives high definition local road map data from the edge processors.
| 9. The system of claim 1, wherein the local data comprises data affecting road conditions, and wherein the one or more edge processors provide traffic, transit, search, routing, telematics, weather, tracking, positioning, high-definition map, or geo-enrichment data.
| 10. The system of claim 1, wherein processor focuses 5G or 6G signals to the target with iterative changes in electrical or mechanical orientation of the one or more antennas.
| 11. The system of claim 1, wherein the edge processors comprise one or more learning machines or neural networks.
| 12. The system of claim 1, comprising one or more cameras and sensors in the housing to capture security information.
| 13. The system of claim 1, wherein the one or more edge processors perform predictive analytics, consumer targeting, fraud detection, or demand forecast.
| 14. The system of claim 1, comprising a camera and a processor for individual identity identification.
| 15. The system of claim 1, wherein the one or more edge processors applies artificial intelligence to location data.
| 16. The system of claim 1, wherein the one or more edge processors analyze sound, scent, or chemical data from sensors in the housing.
| 17. The system of claim 1, comprising an edge learning machine in the housing to provide local edge processing for one or more Internet-of-Things (TOT) sensors.
| 18. The system of claim 1, comprising an edge learning machine that uses pre-trained models and modifies the pre-trained models for a selected task.
| 19. The system of claim 1, comprising a cellular device for a person crossing a street near the city light or street light, the cellular device emitting a person to vehicle (P2V) or a vehicle to person (V2P) safety message.
| 20. The system of claim 1, comprising a cloud trained neural network whose network parameters are reduced before transfer to the edge neural network. | The system has a mobile target which receives the road or traffic parameters from traffic sensors, road sensors or cameras. A housing is provided with edge processors which are coupled to the sensors to handle processing on behalf of the target with a predetermined latency to provide reality rendering data to the target or to provide artificial intelligence for the target. The target optimizes performance and power consumption by offloading processing to the processors and applying a received edge processing result within the latency to augment processing by the target. Antennas (11) are coupled to a housing. A processor communicates with the target using Fifth-generation (5G) or Sixth-generation (6G) protocols. Cellular system e.g. Fifth-generation (5G) cellular system. The liquid lens antenna system comprises a liquid lens that is provided with a moveable surface, where liquid is added or removed to adjust the curvature of the movable surface and an antenna is mounted on the moveable surfaces to change a direction of the antenna to a predetermined target, and thus enables to improve the signal-to-noise ratio of the wireless communication system. The drawing shows the perspective view and top view of the man-hole cover with a small cell and steerable antennas.4Manhole cover portion 10APeripheral edge portion 11Antenna 22Latch 32Digital beam former network |
Please summarize the input | Wireless Communication SystemA system includes a transceiver to communicate with a predetermined target; one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target; and an edge processing module coupled to the transceiver and one or more antennas to provide low-latency computation for the predetermined target.What is claimed is:
| 1. A system, comprising:
a transceiver to communicate with a predetermined target;
one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target; and
an AI processing module coupled to the transceiver and one or more antennas to provide low-latency computation for the predetermined target based on AI determination.
| 2. The system of claim 1, comprising a quantum computer coupled to the edge processing module.
| 3. The system of claim 1, comprising a parser that receives classical specification and data and determines if a portion of such specification runs on a quantum computer, and if so maps classical specification to quantum algorithm and the modified code is provided to an execution unit that selects one or more quantum computers, one or more classical processor, one or more graphical processing units (GPUs), or one or more neuromorphic processors.
| 4. The system of claim 1, wherein the processor calibrates a connection by analyzing RSSI and TSSI and moves the antennas until predetermined cellular parameters are reached.
| 5. The system of claim 1, wherein the edge processing module comprises at least a processor, a graphical processing unit (GPU), a neural network, a quantum computer, a statistical engine, or a programmable logic device (PUD).
| 6. The system of claim 1, wherein the edge processing module and the antenna are enclosed in a housing or shipping container, or the edge processing module is in a separate shipping container adjacent the antenna.
| 7. The system of claim 1, wherein the transceiver comprises a 5G or 6G cellular transceiver.
| 9. A system, comprising:
a transceiver to communicate with a predetermined target;
one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target; and
a beam sweeping module controlling the antenna in accordance with one of: a service level agreement, a performance requirement, a traffic distribution data, a networking requirement or prior beam sweeping history.
| 10. The system of claim 9, wherein the beam sweeping is directed at a group of autonomous vehicles, a group of virtual reality devices, or a group of devices performing similar functions.
| 11. The system of claim 1, comprising a neural network coupled to a control plane, a management plane, and a data plane to optimize 5G parameters.
| 12. The system of claim 1, comprising one or more cameras and sensors in the housing to capture security information.
| 13. The system of claim 1, comprising edge sensors including LIDAR and RADAR.
| 14. The system of claim 1, comprising a camera for individual identity identification.
| 15. The system of claim 1, wherein the edge processing module streams data to the predetermined target to minimize loading the target.
| 16. The system of claim 1, wherein the edge processing module shares workload with a core processing module located at a head-end and a cloud module located at a cloud data center, each processing module having increased latency and each having a processor, a graphical processing unit (GPU), a neural network, a quantum computer, a statistical engine, or a programmable logic device (PLD).
| 17. The system of claim 1, comprising an edge learning machine in a housing or shipping container to provide local edge processing for Internet-of-Things (IoT) sensors.
| 18. The system of claim 17, wherein the edge learning machine uses pre-trained models and modifies the pre-trained models for a selected task.
| 19. The system of claim 1, comprising a cellular device for a person crossing a street near a city light or street light, the cellular device emitting a person to vehicle (P2V) or a vehicle to person (V2P) safety message.
| 20. The system of claim 1, comprising a cloud trained neural network whose network parameters are down-sampled and filter count reduced before transferring to the edge neural network. | The system has a transceiver to communicate with a predetermined target. Antennas are coupled to the transceiver, where each antenna is electrically or mechanically steerable to the predetermined target, and a beam sweeping module controls the antenna in accordance with one of a service level agreement, a performance requirement, a traffic distribution data, a networking requirement or prior beam sweeping history. An artificial intelligence (AI) processing module provides low-latency computation for the target based on AI determination. A quantum computer is coupled to an edge processing module. A parser receives classical specification and data and determines if a portion of the specification runs on the computer. System for providing local edge processing for Internet-of-Things (IoT) sensors with reduced off-chip memory access. Uses include but are not limited to a pole, a building, or a light. The focusing of the 5G signals to the target client/device can be automatically done using processor with iterative changes in the orientation of the antenna by changing the curvature or shape of the surface until predetermined criteria is achieved such as the best transmission speed, TSSI RSSI, and signal-to-noise ratio (SNR). The fleet of drones can operate and navigate as a flock of birds to provide real time adjustment in coverage as needed. The flocks of birds antenna has power and autonomous navigation and can self-assemble and scatter as needed to avoid physical and wireless communication obstacles. The drawing shows a show an exemplary 5G network architecture. |
Please summarize the input | Computing systemA system includes a transceiver to communicate with a predetermined target; one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target; and an edge processing module coupled to the transceiver and one or more antennas to provide low-latency computation for the predetermined target.What is claimed is:
| 1. A system, comprising:
a transceiver to communicate with a predetermined target;
one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target;
an edge processing module coupled to the transceiver and one or more antennas to provide low-latency computation for the predetermined target; and
a quantum computer coupled to the edge processing module.
| 2. The system of claim 1, comprising a parser that receives classical specification and data and determines if a portion of the classical specification runs on the quantum computer, and if so maps the classical specification to a quantum algorithm which is provided to an execution unit that selects one or more quantum processors, one or more classical processors, one or more graphical processing units (GPUs), or one or more neuromorphic processors.
| 3. The system of claim 1, wherein a processor calibrates a connection by analyzing RSSI and TSSI and moves the antennas until predetermined cellular parameters are reached.
| 4. The system of claim 1, wherein the edge processing module comprises at least a processor, a graphical processing unit (GPU), a neural network, a quantum processor, a statistical engine, or a programmable logic device (PLD).
| 5. The system of claim 1, wherein the edge processing module and the one or more antennas are enclosed in a housing or shipping container, or the edge processing module is in a separate shipping container adjacent the one or more antennas.
| 6. A system, comprising:
a transceiver to communicate with a predetermined target;
one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target;
an edge processing module with a learning machine or neural network; and
a beam sweeping module controlling the one or more antennas in accordance with one of: a service level agreement, a performance requirement, a traffic distribution data, a networking requirement or prior beam sweeping history.
| 7. The system of claim 6, wherein the beam sweeping module is directed at a group of autonomous vehicles, a group of virtual reality devices, or a group of devices performing similar functions.
| 8. A system, comprising:
a transceiver to communicate with a predetermined target;
one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target;
an edge processing module including a learning machine or a neural network coupled to the transceiver and one or more antennas to provide low-latency computation for the predetermined target.
| 9. The system of claim 8, wherein the transceiver comprises a 5G or 6G cellular transceiver, and wherein the predetermined target comprises a plurality of antennas coupled to a case to receive signals from the transceiver.
| 10. The system of claim 8, comprising one or more cameras and sensors in the housing to capture security information.
| 11. The system of claim 8, comprising edge sensors including LIDAR and RADAR.
| 12. The system of claim 8, comprising a camera for individual identity identification.
| 13. The system of claim 8, wherein the edge processing module streams data to the predetermined target to minimize loading the predetermined target.
| 14. The system of claim 8, wherein the edge processing module shares workload with a core processing module located at a head-end and a cloud module located at a cloud data center, each processing module having increased latency and each having a processor, a graphical processing unit (GPU), a neural network, a quantum computer, a statistical engine, or a programmable logic device (PLD).
| 15. The system of claim 8, comprising a cellular device for a person crossing a street near a city light or street light, the cellular device emitting a person to vehicle (P2V) or a vehicle to person (V2P) safety message.
| 16. The system of claim 8, comprising a cloud trained neural network whose network parameters are down-sampled and filter count reduced before transferring to the edge processing module neural network.
| 17. The system of claim 8, comprising at least a sensor, a camera, or a microphone in communication with the edge processing module.
| 18. The system of claim 8, wherein the edge processing module offloads processing for a vehicle, a drone, a reality display, a virtual reality display, an augmented reality display, an extended reality display, a game device, a healthcare device, or a manufacturing device.
| 19. The system of claim 8, wherein the edge processing module protects privacy or secures data communication with the transceiver.
| 20. A system, comprising:
a transceiver to communicate with a predetermined target;
one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target; and
a module coupled to the transceiver and one or more antennas to provide low-latency computation for the predetermined target, the module including an edge learning machine in a housing or shipping container to provide local edge processing for Internet-of-Things (IOT) sensors.
| 21. The system of claim 20, wherein the edge learning machine uses pre-trained models and modifies the pre-trained models for a selected task. | The system comprises a transceiver that is provided to communicate with a predetermined target. A number of antennas (11) are coupled to the transceiver, which is electrically or mechanically steerable to the predetermined target. An edge processing module is coupled to the transceiver and antennas to provide low-latency computation for the predetermined target. A quantum computer is coupled to the edge processing module. A parser that receives classical specification and data and determines when a portion of the classical specification runs on the quantum computer. The edge processing module and antennas are enclosed in a housing. System used to select network resources, such as communication links, network devices, core network, and a data center. The esthetics of the site improves, wind load reduces, and lower installation costs by integrating the remote radio head functionality into the antenna. The drawing shows a schematic view of a network architecture. 11Antennas102Network104Base station106Communications tower108Administrator computing device |
Please summarize the input | COMPUTING SYSTEMA system includes a transceiver to communicate with a predetermined target; one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target; and an edge processing module coupled to the transceiver and one or more antennas to provide low-latency computation for the predetermined target.What is claimed is:
| 1. A system, comprising:
a transceiver to communicate with a predetermined target;
one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target; and
an edge processing module coupled to the transceiver and one or more antennas to provide low-latency computation for the predetermined target.
| 2. The system of claim 1, comprising a quantum computer coupled to the edge processing module.
| 3. The system of claim 1, comprising a parser that receives classical specification and data and determines if a portion of such specification runs on a quantum computer, and if so maps classical specification to quantum algorithm and the modified code is provided to an execution unit that selects one or more quantum computers, one or more classical processor, one or more graphical processing units (GPUs), or one or more neuromorphic processors.
| 4. The system of claim 1, wherein the processor calibrates a connection by analyzing RS SI and TSSI and moves the antennas until predetermined cellular parameters are reached.
| 5. The system of claim 1, wherein the edge processing module comprises at least a processor, a graphical processing unit (GPU), a neural network, a quantum computer, a statistical engine, or a programmable logic device (PLD).
| 6. The system of claim 1, wherein the edge processing module and the antenna are enclosed in a housing or shipping container, or the edge processing module is in a separate shipping container adjacent the antenna.
| 7. The system of claim 1, wherein the transceiver comprises a 5G or 6G cellular transceiver.
| 9. A system, comprising:
a transceiver to communicate with a predetermined target;
one or more antennas coupled to the transceiver each electrically or mechanically steerable to the predetermined target; and
a beam sweeping module controlling the antenna in accordance with one of: a service level agreement, a performance requirement, a traffic distribution data, a networking requirement or prior beam sweeping history.
| 10. The system of claim 9, wherein the beam sweeping is directed at a group of autonomous vehicles, a group of virtual reality devices, or a group of devices performing similar functions.
| 11. The system of claim 1, comprising a neural network coupled to a control plane, a management plane, and a data plane to optimize 5G parameters.
| 12. The system of claim 1, comprising one or more cameras and sensors in the housing to capture security information.
| 13. The system of claim 1, comprising edge sensors including LIDAR and RADAR.
| 14. The system of claim 1, comprising a camera for individual identity identification.
| 15. The system of claim 1, wherein the edge processing module streams data to the predetermined target to minimize loading the target.
| 16. The system of claim 1, wherein the edge processing module shares workload with a core processing module located at a head-end and a cloud module located at a cloud data center, each processing module having increased latency and each having a processor, a graphical processing unit (GPU), a neural network, a quantum computer, a statistical engine, or a programmable logic device (PLD).
| 17. The system of claim 1, comprising an edge learning machine in a housing or shipping container to provide local edge processing for Internet-of-Things (TOT) sensors.
| 18. The system of claim 17, wherein the edge learning machine uses pre-trained models and modifies the pre-trained models for a selected task.
| 19. The system of claim 1, comprising a cellular device for a person crossing a street near a city light or street light, the cellular device emitting a person to vehicle (P2V) or a vehicle to person (V2P) safety message.
| 20. The system of claim 1, comprising a cloud trained neural network whose network parameters are down-sampled and filter count reduced before transferring to the edge neural network. | The computing system has a transceiver to communicate with a predetermined target. One or more antennas are coupled to the transceiver and each electrically or mechanically steerable to the predetermined target. An edge processing module is coupled to the transceiver and one or more antennas to provide low-latency computation for the predetermined target. Computing system. The system provides potentially unlimited ability to process components and tasks at least because processing of the components and tasks may be divided up across the network in an intelligent and meaningful way that facilitates efficient use of resources and/or an improved user experience. By intelligently and dynamically determining how to assign processing of components and tasks based on predefined factors, the edge processing may adjust how components and tasks are performed on demand and in a manner that may facilitate efficient use of resources and/or an improved user experience. Edge computing ensures high quality of experience with highly contextualized service experience and efficient utilization of radio and network resources. The drawing shows different views of a manhole cover with a small cell and steerable antennas. 4Manhole cover body11Antennas20Latch mechanism26CLatching control surface30Bands |
Please summarize the input | CELLULAR SYSTEMA system includes a cellular transceiver to communicate with a predetermined target; one or more antennas coupled to the 5G or 6G transceiver each electrically or mechanically steerable to the predetermined target; a processor to control a directionality of the one or more antennas in communication with the predetermined target; and an edge processing module coupled to the processor and the one or more antennas to provide low-latency computation for the predetermined target.What is claimed is:
| 1. A system to perform edge processing for a predetermined target, comprising:
one or more cellular transceivers with one or more antennas that are electrically or mechanically steerable to the predetermined target;
a processor to control communication with the predetermined target; and
one or more edge processing modules coupled to the processor and the one or more antennas to provide low-latency computation for the predetermined target; and
a container to house the transceiver, processor, and one or more edge processing modules, wherein the container is stackable laterally or on top of each other, and wherein the container conforms to a shipping standard.
| 2. The system of claim 1, wherein the container is deployed without a construction permit.
| 3. The system of claim 1, wherein the processor is coupled to fiber optics cable to communicate with a cloud-based radio access network (RAN) or a remote RAN.
| 4. The system of claim 1, comprising an antenna mast, wherein the antenna mast is inside the container or external to the container.
| 5. The system of claim 1, wherein the edge processing module comprises at least a processor, a graphical processing unit (GPU), a neural network, a statistical engine, or a programmable logic device (PLD).
| 6. The system of claim 1, wherein the edge processing module and the antenna comprise one unit.
| 7. The system of claim 1, comprising a cryogenic cooling system to cool the container.
| 8. The system of claim 1, wherein the cellular transceiver comprises a 5G or 6G transceiver.
| 9. The system of claim 1, wherein the processor coordinates beam sweeping by the one or more antennas with radio nodes or user equipment (UE) devices based upon service level agreement, performance requirement, traffic distribution data, networking requirements or prior beam sweeping history.
| 10. The system of claim 9, wherein the beam sweeping is directed at a group of autonomous vehicles, a group of virtual reality devices, or a group of devices having a service agreement with a cellular provider.
| 11. The system of claim 1, comprising a neural network coupled to a control plane, a management plane, or a data plane to optimize 5G or 6G parameters.
| 12. The system of claim 1, comprising one or more cameras and sensors to capture security information.
| 13. The system of claim 1, wherein the container includes edge sensors including LIDAR and RADAR.
| 14. The system of claim 1, comprising a camera for individual identity identification.
| 15. The system of claim 1, wherein the edge processing module streams data to the predetermined target to minimize loading the target.
| 16. The system of claim 1, wherein the edge processing module shares workload with a core processing module located at a head-end and a cloud module located at a cloud data center, each processing module having increased latency and each having a processor, a graphical processing unit (GPU), a neural network, a statistical engine, or a programmable logic device (PLD).
| 17. The system of claim 1, comprising an edge learning machine in the housing to provide local edge processing for Internet-of-Things (TOT) sensors with reduced off-chip memory access.
| 18. The system of claim 17, wherein the edge learning machine uses pre-trained models and modifies the pre-trained models for a selected task.
| 19. The system of claim 1, comprising a cellular device for a person crossing a street near a city light or street light, the cellular device emitting a person to vehicle (P2V) or a vehicle to person (V2P) safety message.
| 20. The system of claim 1, comprising a cloud trained neural network whose network parameters are down-sampled and filter count reduced before transferring to the edge neural network. | The edge processing system has one or more cellular transceivers with one or more antennas (11) that are electrically or mechanically steerable to a predetermined target. A processor controls communication with the predetermined target. One or more edge processing modules coupled to the processor and one or more antennas to provide low-latency computation for the predetermined target. A container houses the transceiver, the processor and one or more edge processing modules. The container is stackable laterally or on top of each other and conforms to a shipping standard. Edge processing system for predetermined target. Enables improvements in network capacity and coverage. Improves esthetics of site and reduces wind load resulting in lower leasing and installation costs by integrating remote radio head functionality into antenna. Provides potentially unlimited ability to process components and tasks because processing of components and tasks may be divided up across network in intelligent and meaningful way that facilitates efficient use of resources and/or improved user experience. Ensures high quality of experience with highly contextualized service experience and efficient utilization of radio and network resources. Reduces size of neural networks for edge learning while maintaining accuracy to get high performance at low parameter count. The drawing shows the perspective view of a man-hole cover with a small cell and steerable antennas. 4Manhole cover body6First side8Second side11Antennas22Latches |
Please summarize the input | Cellular systemA system includes a cellular transceiver to communicate with a predetermined target; one or more antennas coupled to the 5G transceiver each electrically or mechanically steerable to the predetermined target; a processor to control a directionality of the one or more antennas in communication with the predetermined target; and an edge processing module coupled to the processor and the one or more antennas to provide low-latency computation for the predetermined target.What is claimed is:
| 1. A system to service mobile client devices with processing loads including machine learning processing load, extended reality processing load, or autonomous driving processing load, comprising:
a cellular transceiver to offload execution of a compute-intensive code object upon request from a client device with a processing load request;
one or more antennas coupled to the cellular transceiver each electrically or mechanically steerable to the client device;
a processor coupled to the one or more antennas in communication with the client device based on signals received from the client device, wherein the client device requests the processor to process at least a portion of a client processing load; and
an edge processing module coupled to the processor and mechanically coupled to the one or more antennas to process the compute-intensive code object on behalf of the client device and to wirelessly provide low-latency access for the client device,
wherein the edge processing module shares processing loads with a core processing module located at a 5G head-end and a cloud module located at a cloud data center such that processing loads requiring intermediate low latency access are transferred to the core processing module at the 5G head-end, while non-time sensitive large processing loads are offloaded to the cloud module at the cloud data center, and further wherein the edge processing module having a neural network with down sampled neural network parameters associated with processing loads from the client device.
| 2. The system of claim 1, wherein the processor calibrates a radio link between a transceiver in a housing and the client device.
| 3. The system of claim 1, wherein the processor is coupled to fiber optics cable to communicate with a cloud-based radio access network (RAN) or a remote RAN.
| 4. The system of claim 1, wherein the processor calibrates a connection by analyzing RSSI (Received Signal Strength Indicator) and TSSI (Transmit Signal Strength Indicator) and moves the antennas until predetermined cellular parameters are reached.
| 5. The system of claim 1, wherein the edge processing module comprises at least a processor, a graphical processing unit (GPU), a neural network, a statistical engine, or a programmable logic device (PLD).
| 6. The system of claim 1, wherein the edge processing module and the antenna comprise one unit.
| 7. The system of claim 6, comprising a pole, a building, or a light.
| 8. The system of claim 1, wherein the cellular transceiver comprises a 5G transceiver.
| 9. The system of claim 1, wherein the processor coordinates beam sweeping by the one or more antennas with radio nodes or user equipment (UE) devices based upon service level agreement, performance requirement, traffic distribution data, networking requirements or prior beam sweeping history.
| 10. The system of claim 9, wherein the beam sweeping is directed at a group of autonomous vehicles or a group of virtual reality devices.
| 11. The system of claim 1, comprising a neural network coupled to a control plane, a management plane, and a data plane to optimize 5G parameters.
| 12. The system of claim 1, comprising one or more cameras and sensors in a housing to capture security information.
| 13. The system of claim 1, comprising edge sensors including LIDAR (Light Detection And Ranging) and RADAR (Radio Detection And Ranging).
| 14. The system of claim 1, comprising a camera for individual identity identification.
| 15. The system of claim 1, wherein the edge processing module streams data to the client device to minimize loading the client device.
| 16. The system of claim 1, comprising an edge learning machine in a housing to provide local edge processing for Internet-of-Things (IOT) sensors.
| 17. The system of claim 16, wherein the edge learning machine uses pre-trained models and modifies the pre-trained models for a selected task.
| 18. The system of claim 1, a cellular device for a person crossing a street near a city light or street light, the cellular device emitting a person to vehicle (P2V) or a vehicle to person (V2P) safety message.
| 19. The system of claim 1, comprising a cloud trained neural network whose network parameters are filter count reduced before transferring to the edge neural network. | The system (1) has a cellular transceiver to offload execution of a compute-intensive code object. Antennas (11) are coupled to the cellular transceiver. A processor is coupled to the antennas in communication with the client device. An edge processing module is coupled to the processor and is mechanically coupled to the antennas. The edge processing module shares processing loads with a core processing module located at a 5G head-end. A cloud module is located at a cloud data center such that processing loads requiring intermediate low latency access are transferred to the core processing module at the 5G head-end, while non-time sensitive large processing loads are offloaded to the cloud module at the cloud data center. The edge processing module has a neural network with down sampled neural network parameters associated with processing loads from the client device. System for servicing mobile client devices with processing loads including machine learning processing load, extended reality processing load, or autonomous driving processing load. The receive and transmits digital beam former (DBF) coefficients are adjusted to help maintain an improved or maximum signal quality, reduce or minimize in-band interference and maximize receive power level. The active antenna system (AAS) provides site footprint reduction, distribution of radio functions within the antenna results in built-in redundancy and improved thermal performance, and distributed transceivers supports a host of advanced electronic beam-tilt features that enables improvements in network capacity and coverage. The esthetics of the site is improved and wind load is reduced, resulting in lower leasing and installation costs, by integrating the remote radio head functionality into the antenna. The edge computing ensures high quality of experience with highly contextualized service experience and efficient utilization of radio and network resources. The local neural network performs late down-sampling and filter count reduction so as to get high performance at a low parameter count and reduces size of the neural networks for edge learning while maintaining accuracy. The drawing shows a schematic diagram of the city light small cell environment with crime/pollution sniffing capabilities. 1System for servicing mobile client devices with processing loads10Computing unit11Antenna15Road18Database |
Please summarize the input | Cellular systemA system includes one or more antennas; and a processor to control a directionality of the antennas in communication with a predetermined target using 5G protocols.What is claimed is:
| 1. A method of communicating data with a user equipment (UE), comprising:
receiving a signal from the UE coupled to the or more steerable antenna beams from one or more antennas at a communication station;
determining a location direction of the UE using said signal;
generating digital beam forming coefficients to transmit from one of said steerable antenna beams in said location direction of the UE;
transmitting data from said communication station to said UE within said one transmit steerable antenna beam;
tracking said location direction of said UE as said communication station and said UE movement relative to other UEs;
adjusting said beam forming coefficients associated with one transmit steerable antenna beam in response to the tracking step to maintain said one transmit steerable antenna beam in the location direction of said UE;
further adjusting said beam forming coefficients associated with one transmit steerable antenna beam to improve a signal quality of communication signal received at said communication station.
| 2. The method of claim 1, comprising mounting one or more antennas on a pole, a tower, or a shipping container and directionally aiming the antennas from the pole, tower, or shipping container.
| 3. The method of claim 1, comprising remapping the beams to avoid obstructions or issues that affect 5G/6G transmissions.
| 4. The method of claim 1, comprising changing the beams according to load, usage, time of day, or other factors.
| 5. The method of claim 1, comprising mounting one or more antennas on a pole, a tower, or a shipping container and manually calibrating a wireless connection by examining an RSSI or a TSSI and scanning antenna actuators or moveable lens until predetermined RSSI or TSSI level or cellular parameter is reached.
| 6. The method of claim 1, comprising scanning and optimizing radio communication with a target client or device by moving actuators coupled to a surface.
| 7. The method of claim 1, comprising using an array of actuators or antennas, each antenna is independently steerable to optimize 5G transmission.
| 8. The method of claim 1, comprising using Fresnel lens can be used to improve SNR.
| 9. The method of claim 1, comprising focusing 5G signals to the target client or device with iterative changes in an orientation of the antenna by changing a housing location based on one or more predetermined criteria.
| 10. The method of claim 1, comprising using an array antenna onboard a cell tower with a digital beam former (DBF), said array antenna having a plurality of actuators moving the RF radiating elements for providing steerable antenna beams within an antenna footprint region, said DBF providing for each radiating element, beam forming coefficients for controlling characteristics of said steerable antenna beams.
| 11. The method of claim 1, comprising requesting a portion of a network for a group of devices, checking for available resources to satisfy the request and assigning a network slice deployment layout satisfying the requested portion of the network including antenna level layout, and managing resources at the antenna level as part of the requested portion of the network to provide communication for the group.
| 12. The method of claim 1, comprising a high power active antenna array mounted on a cell tower, a balloon, or a drone; and a plurality of medium power active antenna arrays wirelessly coupled to the high power active antenna, wherein the medium power antenna array relays data transmission between the high power active antenna array and the UE to reduce RF exposure on biologics.
| 13. The method of claim 1, comprising a high power active antenna array mounted on a cell tower, balloon, or a drone; and a plurality of medium power active antenna arrays wirelessly coupled to the high power active antenna, wherein the medium power antenna array relays data transmission between the high power active antenna array and the UE to reduce RF exposure on a person.
| 14. A system, comprising:
a mobile housing including at least a pole, a tower, or a shipping container to receive a signal from one or more predetermined targets;
one or more antennas to focus on one or more predetermined targets by generating coefficients to transmit data from one or more steerable antenna beams in the location direction of the predetermined target;
one or more millimeter wave transceivers coupled to the one or more antennas;
a processor to control the one or more transceivers and one or more antennas in communication with the predetermined target using 5G protocols upon determining a location direction of the predetermined target; and
an edge learning machine that uses pre-trained models and modifies the pre-trained models for a selected task.
| 15. The system of claim 14, wherein the processor calibrates a radio link between a transceiver and a client device.
| 16. The system of claim 14, wherein the processor is coupled to fiber optics cable to communicate with a cloud-based radio access network (RAN) or a remote RAN.
| 17. The system of claim 14, comprising a camera for individual identity identification.
| 18. The system of claim 14, wherein the processor analyzes walking gaits and facial features for identity identification.
| 19. The system of claim 14, comprising an edge processor to provide local edge processing for Internet-of-Things (IOT) sensors.
| 20. The system of claim 14, comprising network slice deployment layout descriptors corresponding to a network slice deployment layout with network slice life cycle management, configuration, performance management, monetary cost associated with the network slice deployment layout, or quality-of-service values associated with the network slice deployment layout.
| 21. The system of claim 14, comprising a cellular device for a person crossing a street near the city light or street light, the cellular device emitting a person to vehicle (P2V) or a vehicle to person (V2P) safety message.
| 22. The system of claim 14, comprising a neural network whose network parameters are reduced before transfer to an edge neural network.
| 23. The system of claim 14, wherein a portion of a network for a group of devices, checks for available resources to satisfy the request and assigns a network slice deployment layout satisfying the requested portion of the network including antenna level layout, and manages resources at the antenna level as part of the requested portion of the network to provide communication for the group.
| 24. A communication method, comprising:
providing a beam antenna and a millimeter-wave transceiver coupled to the beam antenna secured to a shipping container or a cell tower;
scanning the beam antenna in the direction of an autonomous vehicle; and
communicating using the millimeter-wave transceiver and the beam antenna with the vehicle pursuant to a defined communication protocol by:
receiving at the steerable antenna a receive radio signal from the vehicle;
determining beamforming coefficients transmit a transmit radio signal to the vehicle;
transmitting data from said steerable antenna in the direction of the vehicle moving relative to the antenna; and
applying pre-trained learning machine models and modifies the pre-trained models for a selected task requested by the vehicle.
| 25. The method of claim 24, comprising requesting a portion of a network for a group of devices, checking for available resources to satisfy the request and assigning a network slice deployment layout satisfying the requested portion of the network including antenna level layout, and managing resources at the antenna level as part of the requested portion of the network to provide communication for the group.
| 26. The method of claim 24, comprising a high power active antenna array mounted on a cell tower, balloon, or a drone; and a plurality of medium power active antenna arrays wirelessly coupled to the high power active antenna, wherein the medium power antenna array relays data transmission between the high power active antenna array and the UE to reduce RF exposure on a person. | The method involves receiving a signal from the UE coupled to the steerable antenna beams from antennas at a communication station. The location direction of the UE is determined using signal. The digital beam forming coefficients to transmit from one of steerable antenna beams are generated in location direction of the UE. The data is transmitted from communication station to UE within one transmit steerable antenna beam. The location direction of UE is tracked as communication station and UE movement relative to other UEs. The beam forming coefficients associated with one transmit steerable antenna beam is adjusted in response to the tracking step to maintain one transmit steerable antenna beam in the location direction of UE. The beam forming coefficients associated with one transmit steerable antenna beam are adjusted to improve a signal quality of communication signal received at communication station. INDEPENDENT CLAIMS are included for the following:a system for communicating data with UE; anda communication method. Method for communicating data with user equipment (UE) e.g. mobile phone in cellular system. The receive and transmits digital beam former (DBF) coefficients are adjusted to help maintain an improved or maximum signal quality, to help reduce or minimize in-band interference and to help maximize receive power level. The aesthetics of the site can be improved and wind load reduced, resulting in lower leasing and installation costs. The neural network control of multiple input multiple output (MIMO) system facilitate testing of MIMO base stations, reduce power consumption during MIMO communications, allow for flexibility in capacity, allow for flexibility in MIMO signal estimation, allow routing around defective processing elements or antennas, etc. The local neural network performs late down-sampling and filter count reduction, to get high performance at a low parameter count to reduce size of the neural networks for edge learning when maintaining accuracy. The drawing shows a schematic diagram of exemplary city light small cell environment with crime or pollution sniffing capabilities.10 Computing unit 11Street device 15Road 18Database 19 User interface |
Please summarize the input | Cellular systemA system includes a housing with one or more edge processors to handle processing on behalf of a mobile target or to provide local data to the mobile target or to provide artificial intelligence for the mobile target; one or more antennas coupled to the housing; and a processor to control a directionality of the antennas in communication with the mobile target using 5G or 6G protocols.What is claimed is:
| 1. A system, comprising:
a housing with one or more edge processors to handle processing on behalf of a mobile target or to provide local data to the mobile target or to provide artificial intelligence for the mobile target;
one or more antennas coupled to the housing; and
a processor to control a directionality of the antennas in communication with the mobile target using 5G or 6G protocols; and
a cloud trained neural network whose network parameters are reduced before transferring to the edge neural network.
| 2. The system of claim 1, wherein the processor calibrates a radio link between a transceiver in the housing and a client device.
| 3. The system of claim 1, wherein the processor process images from one or more mobile target cameras for location identification, ridesharing pick-up, or delivery.
| 4. The system of claim 1, wherein the one or more edge processors detect real time hazard detection or road signs.
| 5. The system of claim 1, wherein the processor moves actuators coupled to the antennas.
| 6. The system of claim 1, wherein the one or more edge processors handle local data, weather or location data.
| 7. The system of claim 1, wherein the one or more edge processors handle video content, healthcare, robotics, autonomous vehicle, augmented reality, virtual reality, extended reality, factory automation, gaming, asset tracking, or surveillance.
| 8. The system of claim 1, wherein the mobile target comprises plant or manufacturing equipment.
| 9. The system of claim 1, wherein the one or more edge processors provide traffic, transit, search, routing, telematics, weather, tracking, positioning, high definition map, or geo-enrichment data.
| 10. The system of claim 1, wherein the processor focuses 5G signals to the target with iterative changes in electrical or mechanical orientation of the one or more antennas.
| 11. The system of claim 1, wherein neural networks comprises parameters trained with remote processors.
| 12. The system of claim 1, comprising one or more cameras and sensors in the housing to capture security information.
| 13. The system of claim 1, wherein the one or more edge processors perform predictive analytics, consumer targeting, fraud detection, or demand forecast.
| 14. The system of claim 1, comprising a camera and a processor for individual identity identification.
| 15. The system of claim 1, wherein the one or more edge processors applies artificial intelligence to location data.
| 16. The system of claim 1, comprising an edge learning machine in the housing to provide local edge processing for one or more Internet-of-Things (IOT) sensors.
| 17. The system of claim 16, wherein the edge learning machine uses pre-trained models and modifies the pre-trained models for a selected task.
| 18. The system of claim 1, comprising a cellular device for a person crossing a street near the city light or street light, the cellular device emitting a person to vehicle (P2V) or a vehicle to person (V2P) safety message. | The system has a housing with one or more edge processors that handles processing on behalf of a mobile target or provides local data to the mobile target or provides artificial intelligence for the mobile target. One or more antennas (11) are coupled to the housing and a processor controls a directionality of the antennas in communication with the mobile target using fifth generation (5G) or 6G protocols. A cloud trained neural network whose network parameters are reduced before transferring to the edge neural network. Cellular system. The beamforming architecture can control the resulting interference pattern, in order to realize a steerable main lobe that provides high beam gain in a particular direction. The neural network control of the multiple-input multiple-output (MIMO) system can facilitate testing of MIMO base stations, reduce power consumption during MIMO communication, allow for flexibility in capacity, allow for flexibility in MIMO signal estimation, allow routing around defective processing elements or antennas, etc. The receive and transmit digital beam former (DBF) coefficients are adjusted to help maintain an improved or maximum signal quality, to help reduce or minimize in-band interference and to help maximize receive power level. The drawing shows a schematic view of 5G network architecture. 11Antenna102Digital beamformer104Base station108Computing device128Spoke |
Please summarize the input | Cellular systemA system includes a cellular transceiver to communicate with a predetermined target; one or more antennas coupled to the 5G transceiver each electrically or mechanically steerable to the predetermined target; a processor to control a directionality of the one or more antennas in communication with the predetermined target; and an edge processing module coupled to the processor and the one or more antennas to provide low-latency computation for the predetermined target.What is claimed is:
| 1. A system, comprising:
a 5G cellular transceiver to communicate with a predetermined target;
one or more antennas coupled to the 5G cellular transceiver each electrically or mechanically steerable to the predetermined target;
a processor to control a directionality of the one or more antennas in communication with the predetermined target; and
an edge processing module coupled to the processor and the one or more antennas to provide low-latency computation on a request or data generated by the predetermined target, wherein the edge processing module shares workload with a core processing module located at a head-end or a cloud module located at a cloud data center, each processing module having increased latency and each having a processor, a graphical processing unit (GPU), a neural network, a statistical engine, or a programmable logic device (PLD).
| 2. The system of claim 1, wherein the processor calibrates a radio link between a transceiver in the housing and a client device.
| 3. The system of claim 1, wherein the processor is coupled to fiber optics cable to communicate with a cloud-based radio access network (RAN) or a remote RAN.
| 4. The system of claim 1, wherein the edge processing module comprises at least a processor, a graphical processing unit (GPU), a neural network, a statistical engine, or a programmable logic device (PLD).
| 5. The system of claim 1, wherein the edge processing module and the antenna comprise one unit.
| 6. The system of claim 5, wherein the unit comprises a pole, a building, or a light.
| 7. The system of claim 1, comprising a neural network coupled to a control plane, a management plane, or a data plane to optimize 5G parameters.
| 8. The system of claim 1, comprising one or more cameras and sensors in the housing to capture security information.
| 9. The system of claim 1, comprising edge sensors including LIDAR and RADAR.
| 10. The system of claim 1, comprising a camera for automated identity identification.
| 11. The system of claim 1, wherein the edge processing module streams data processed at the head end or the cloud data center to the predetermined target to minimize a processing load for the target.
| 12. The system of claim 1, comprising a wearable device to render data processed by the head end or the cloud data center and wireless sent to the wearable device to minimize processing at the wearable device.
| 13. The system of claim 1, comprising an edge learning machine in a housing to provide local edge processing for Internet-of-Things (IOT) sensors.
| 14. The system of claim 13, wherein the edge learning machine uses pre-trained models and modifies the pre-trained models for a selected task.
| 15. The system of claim 1, comprising a cellular device for a person crossing a street near a city light or street light, the cellular device emitting a person to vehicle (P2V) or a vehicle to person (V2P) safety message.
| 16. A system, comprising:
a 5G cellular transceiver to communicate with a predetermined target;
one or more antennas coupled to the 5G cellular transceiver each electrically or mechanically steerable to the predetermined target;
a processor to control a directionality of the one or more antennas in communication with the predetermined target; and
an edge processing module coupled to the processor and the one or more antennas to provide low-latency computation on a request or data generated by the predetermined target, wherein the processor calibrates a connection by analyzing RSSI and TSSI and the one or more antennas is moved until predetermined cellular parameters are reached.
| 17. A system, comprising:
a 5G cellular transceiver to communicate with a predetermined target;
one or more antennas coupled to the 5G cellular transceiver each electrically or mechanically steerable to the predetermined target;
a processor to control a directionality of the one or more antennas in communication with the predetermined target; and
an edge processing module coupled to the processor and the one or more antennas to provide low-latency computation on a request or data generated by the predetermined target, wherein the edge processing module stores video content close to users.
| 18. A system, comprising:
a 5G cellular transceiver to communicate with a predetermined target;
one or more antennas coupled to the 5G cellular transceiver each electrically or mechanically steerable to the predetermined target;
a processor to control a directionality of the one or more antennas in communication with the predetermined target; and
an edge processing module coupled to the processor and the one or more antennas to provide low-latency computation on a request or data generated by the predetermined target, wherein the processor coordinates beam sweeping by the one or more antennas with radio nodes or user equipment (UE) devices based upon service level agreement, performance requirement, traffic distribution data, networking requirements or prior beam sweeping history.
| 19. The system of claim 18, wherein the beam sweeping is directed at a group of autonomous vehicles or a group of virtual reality devices.
| 20. A system, comprising:
a 5G cellular transceiver to communicate with a predetermined target;
one or more antennas coupled to the 5G cellular transceiver;
a processor to control one or more antennas in communication with the predetermined target;
an edge processing module coupled to the processor and the one or more antennas to provide low-latency computation based on data generated by or a request from the predetermined target; and
a cloud trained edge neural network whose network parameters are down-sampled and filter count reduced before transferring to the edge neural network. | The system (1) has a fifth generation (5G) cellular transceiver that is provided to communicate with a predetermined target. Antennas are coupled to the 5G cellular transceiver and electrically or mechanically steerable to the predetermined target. A processor is provided to control a directionality of the antennas in communication with the predetermined target. An edge processing module is coupled to the processor and the antennas to provide a low-latency computation on a request or data generated by the predetermined target. The edge processing module shares a workload with a core processing module located at a head-end or a cloud module located at a cloud data center. The processing module having increased latency and including a processor, a GPU, a neural network, a statistical engine or a PLD. Cellular system for optimizing data flow in 5G network. The receive and transmits digital beam former (DBF) coefficients are adjusted to maintain the improved or maximum signal quality, to reduce or minimize the in-band interference and to maximize the receive power level. The benefits of AAS include the site footprint reduction, distribution of radio functions within the antenna results in built-in redundancy and improved thermal performance and distributed transceivers can support the host of advanced electronic beam-tilt features that enables the improvements in network capacity and coverage. The aesthetics of the site are improved and the wind load is reduced by integrating the remote radio head functionality into the antenna, thus, resulting in lower leasing and installation costs. The tasks are divided up across the network in the intelligent and meaningful way that facilitates the efficient use of resources and/or the improved user experience. The local neural network performs the late down-sampling and filter count reduction to get high performance at the low parameter count to reduce size of the neural networks for edge learning while maintaining accuracy. The drawing shows a schematic view of a city light small cell environment with crime/pollution sniffing capabilities. 1Cellular system10Computing unit11Street device15Road18Database |
Please summarize the input | Systems and methods for selecting among different driving modes for autonomous driving of a vehicleSystems and methods for selecting among different driving modes for autonomous driving of a vehicle may: generate output signals; determine the vehicle proximity information that indicates whether one or more vehicles are within the particular proximity of the vehicle; determine the internal passenger presence information that indicates whether one or more passengers are present in the vehicle; select a first driving mode or a second driving mode based on one or more determinations; and control the vehicle autonomously in accordance with the selection of either the first driving mode or the second driving mode.What is claimed is:
| 1. A vehicle configured to select among different driving modes for autonomous driving of the vehicle, the vehicle comprising:
a set of sensors sensing vehicle proximity information indicating whether one or more vehicles are within a threshold distance from the vehicle;
one or more hardware processors configured by machine-readable instructions to:
select a first driving mode, wherein selecting the first driving mode is responsive to the vehicle proximity information indicating no vehicles are within the threshold distance from the vehicle;
select a second driving mode responsive to the vehicle proximity information indicating one or more vehicles are within the threshold distance from the vehicle, wherein the first driving mode is at least one of more energy-efficient than the second driving mode and results in shortened driving time compared to that resulting from selection of the second driving mode; and
control the vehicle autonomously in accordance with selection of either the first driving mode or the second driving mode.
| 2. The vehicle of claim 1, wherein the set of sensors is further configured to generate output signals conveying internal passenger presence information, wherein the internal passenger presence information indicates whether one or more passengers are present in the vehicle, and wherein the one or more hardware processors are further configured by machine-readable instructions to:
determine the internal passenger presence information that indicates whether one or more passengers are present in the vehicle, wherein determination of the internal passenger presence information is based on the output signals;
wherein selecting the first driving mode is further responsive to the internal passenger presence information indicating no passengers are present in the vehicle;
wherein selecting the second driving mode is responsive to the internal passenger presence information indicating one or more passengers are present in the vehicle.
| 3. The vehicle of claim 1, wherein the one or more hardware processors are further configured by machine-readable instructions to:
obtain external passenger presence information that indicates whether any passengers are present in one or more vehicles within the threshold distance from the vehicle, responsive to the vehicle proximity information indicating the one or more vehicles are within the threshold distance from the vehicle;
select the first driving mode, wherein selecting the first driving mode is responsive to the external passenger presence information indicating no passengers are present in the one or more vehicles within the threshold distance from the vehicle; and
select the second driving mode, wherein selecting the second driving mode is responsive to the external passenger presence information indicating passengers are present in the one or more vehicles within the threshold distance from the vehicle.
| 4. The vehicle of claim 1, wherein the one or more hardware processors are further configured by machine-readable instructions to:
obtain external control information that indicates whether one or more vehicles within the particular proximity of the vehicle are currently under autonomous control, responsive to the vehicle proximity information indicating the one or more vehicles are within the threshold distance from the vehicle;
select the first driving mode, wherein selecting the first driving mode is responsive to the external control information indicating the one or more vehicles within the threshold distance from the vehicle are currently under autonomous control; and
select the second driving mode, wherein selecting the second driving mode responsive to the external control information indicating at least one of the one or more vehicles within the threshold distance from the vehicle are currently not under autonomous control.
| 5. The vehicle of claim 1, wherein the first driving mode allows a higher level of acceleration than a maximum level of acceleration allowed while the vehicle is operating in the second driving mode.
| 6. The vehicle of claim 1, wherein the first driving mode allows a higher level of deceleration than a maximum level of deceleration allowed while the vehicle is operating in the second driving mode.
| 7. The vehicle of claim 3, wherein the first driving mode allows a smaller following distance to another vehicle than a minimum following distance allowed while the vehicle is operating in the second driving mode.
| 8. The vehicle of claim 3, wherein obtaining the external passenger presence information is accomplished through vehicle-to-vehicle communication.
| 9. The vehicle of claim 3, wherein the output signals further convey visual information regarding an exterior of the vehicle, and wherein obtaining the external passenger presence information is accomplished through analysis of the visual information.
| 10. The vehicle of claim 1, wherein the one or more physical computer processors are further configured by computer-readable instructions to:
facilitate user input from one or more passengers, wherein the user input represents a request for selection of the first driving mode;
select the first driving mode, wherein selection is based on the user input.
| 11. A method for selecting among different driving modes for autonomous driving of a vehicle, the method comprising:
generating output signals conveying vehicle proximity information indicating whether one or more vehicles are within a threshold distance from the vehicle;
selecting a first driving mode responsive to the vehicle proximity information indicating no vehicles are within the threshold distance from;
selecting a second driving mode responsive to the vehicle proximity information indicating one or more vehicles are within the threshold distance from the vehicle, wherein the first driving mode is at least one of more energy-efficient than the second driving mode and results in shortened driving time compared to that resulting from selection of the second driving mode; and
controlling the vehicle autonomously in accordance with selecting either the first driving mode or the second driving mode.
| 12. The method of claim 11, wherein the output signals further convey internal passenger presence information, wherein the internal passenger presence information indicates whether one or more passengers are present in the vehicle, the method further comprising:
determining the internal passenger presence information that indicates whether one or more passengers are present in the vehicle, wherein determination of the internal passenger presence information is based on the output signals;
wherein selecting the first driving mode is further responsive to the internal passenger presence information indicating no passengers are present in the vehicle; and
wherein selecting the second driving mode is responsive to the internal passenger presence information indicating one or more passengers are present in the vehicle.
| 13. The method of claim 11, further comprising:
obtaining external passenger presence information that indicates whether any passengers are present in one or more vehicles within the particular proximity of the vehicle, responsive to the vehicle proximity information indicating the one or more vehicles are within the threshold distance from the vehicle;
selecting the first driving mode, wherein selecting the first driving mode is responsive to the external passenger presence information indicating no passengers are present in the one or more vehicles within the threshold distance from the vehicle; and
selecting the second driving mode, wherein selecting the second driving mode is responsive to the external passenger presence information indicating passengers are present in the one or more vehicles within the threshold distance from the vehicle.
| 14. The method of claim 11, further comprising:
obtaining external control information that indicates whether one or more vehicles within the threshold distance from the vehicle are currently under autonomous control, responsive to the vehicle proximity information indicating the one or more vehicles are within the threshold distance from the vehicle;
selecting the first driving mode, wherein selecting the first driving mode is responsive to the external control information indicating the one or more vehicles within the threshold distance from the vehicle are currently under autonomous control; and
selecting the second driving mode, wherein selecting the second driving mode responsive to the external control information indicating at least one of the one or more vehicles within the particular proximity of the vehicle are currently not under autonomous control.
| 15. The method of claim 11, wherein the first driving mode allows a higher level of acceleration than a maximum level of acceleration allowed while the vehicle is operating in the second driving mode.
| 16. The method of claim 11, wherein the first driving mode allows a higher level of deceleration than a maximum level of deceleration allowed while the vehicle is operating in the second driving mode.
| 17. The method of claim 13, wherein the first driving mode allows a smaller following distance to another vehicle than a minimum following distance allowed while the vehicle is operating in the second driving mode.
| 18. The method of claim 13, wherein obtaining the external passenger presence information is accomplished through vehicle-to-vehicle communication.
| 19. The method of claim 13, wherein the output signals further convey visual information regarding an exterior of the vehicle, and wherein obtaining the external passenger presence information is accomplished through analysis of the visual information.
| 20. The method of claim 11, further comprising:
facilitating user input from one or more passengers;
selecting the first driving mode, wherein selection is based on the user input. | The vehicle has sensors to generate output signals, and hardware processors configured by machine-readable instructions to perform operations. The operation includes determine (206) the vehicle proximity information that indicates whether vehicles are within the particular proximity of the vehicle, select (208) a first driving mode, select (210) a second driving mode, control (212) the vehicle autonomously in accordance with selecting either the first driving mode or the second driving mode. An INDEPENDENT CLAIM is included for a method for selecting different driving modes for autonomous driving of vehicle. Vehicle with different driving modes. Enables to control the vehicle autonomously based on selection of either the first driving mode or second driving mode. The drawing shows the flowchart of the method for selecting different driving modes for autonomous driving of vehicle. 202Generate output signals204Determine vehicle proximity information206Determine internal passenger presence information208Select first driving mode210Select second driving mode212Control vehicle autonomously |
Please summarize the input | VEHICLE-TO-HUMAN COMMUNICATION IN AN AUTONOMOUS VEHICLE OPERATIONA device and method for autonomous vehicle-to-human communications are disclosed. Upon detecting a human traffic participant being proximal to a traffic yield condition of a vehicle planned route, generating a message for broadcast to the human traffic participant and sensing whether the human traffic participant acknowledges a receipt of the message. When sensing that the human traffic participant acknowledges receipt of the message, generating a vehicle acknowledgment message for broadcast to the pedestrian.|1. A method for vehicle-to-human communication operable for autonomous vehicle operation, the method comprising:
detecting a human traffic participant being proximal to a traffic yield condition of a vehicle planned route;
generating an action message indicating a vehicle intent for broadcast to the human traffic participant;
sensing, by a vehicle sensor device, whether the human traffic participant acknowledges receipt of the action message by at least one of:
determining, by at least one processor, human traffic participant attention data indicating that the human traffic participant visually observes the action message; and
determining, by the at least one processor, human traffic participant movement data to be responsive to the action message; and
when the human traffic participant acknowledges the receipt of the action message, generating a vehicle acknowledgment message for broadcast to the human traffic participant.
| 2. The method of claim 1, further comprising:
when either of the human traffic participant attention data and the human traffic participant movement data are contrary to the action message, generating a counter-action message responsive to the either of the human traffic participant attention data and the human traffic participant movement data.
| 3. The method of claim 1, wherein the generating the action message comprises at least one of:
a graphic-based message for display by an external display of the vehicle; and
an audible message for playback from the vehicle.
| 4. The method of claim 1, wherein the human traffic participant attention data comprises at least one of:
a human traffic participant gaze directed towards a direction of the vehicle;
a human traffic participant gesture directed towards the direction of the vehicle; and
a facial recognition indicating the human traffic participant is facing the direction of the vehicle.
| 5. The method of claim 1, wherein the human traffic participant movement data comprises at least one of:
the action message conveying a human traffic participant velocity vector component slowing to a pedestrian travel rate that operates to avoid interception of the vehicle planned route; and
modifying a directional component to one that operates to avoid intercepting the vehicle planned route.
| 6. The method of claim 1, wherein the vehicle acknowledgement message comprises at least one of:
a graphic user interface acknowledgment message for display via the external display; and
an audible acknowledgment message for directional announcement by a speaker of the vehicle.
| 7. The method of claim 1, wherein the traffic yield condition may be defined by at least one of:
Route Network Description File (RNDF) data;
traffic yield condition data; and
object recognition data relating to the traffic yield condition for the vehicle planned route.
| 8. A method in a vehicle control unit for autonomous operation of a vehicle, the method comprising:
detecting a human traffic participant being proximal to a traffic yield condition of a vehicle planned route;
generating an action message indicating a vehicle intent for broadcast to the human traffic participant;
sensing, by a vehicle sensor device, whether the human traffic participant acknowledges receipt of the action message by:
determining, by at least one processor, human traffic participant attention data indicating that the human traffic participant visually observes the action message; and
determining, by the at least one processor, human traffic participant movement data to be responsive to the action message; and
when the human traffic participant acknowledges the receipt of the action message, generating a vehicle acknowledgment message for broadcast to the human traffic participant.
| 9. The method of claim 8, further comprising:
when either of the human traffic participant attention data and the human traffic participant movement data are contrary to the action message, generating a counter-action message responsive to the either of the human traffic participant attention data and the human traffic participant movement data.
| 10. The method of claim 8, wherein the generating the action message comprises at least one of:
a graphic-based message for display by an external display of the vehicle; and
an audible message for playback from the vehicle.
| 11. The method of claim 8, wherein the human traffic participant attention data comprises at least one of:
a human traffic participant gaze directed towards a direction of the vehicle;
a human traffic participant gesture directed towards the direction of the vehicle; and
a facial recognition indicating the human traffic participant is facing the direction of the vehicle.
| 12. The method of claim 8, wherein the human traffic participant movement data comprises at least one of:
the action message conveying a human traffic participant velocity vector component slowing to a pedestrian travel rate that operates to avoid interception of the vehicle planned route; and
modifying a directional component to one that operates to avoid intercepting the vehicle planned route.
| 13. The method of claim 8, wherein the vehicle acknowledgement message comprises at least one of:
a graphic user interface acknowledgment message for display via the external display; and
an audible acknowledgment message for directional announcement by a speaker of the vehicle.
| 14. The method of claim 8, wherein the traffic yield condition may be defined by at least one of:
Route Network Description File (RNDF) data;
traffic yield condition data received from a vehicle-to-infrastructure device; and
object recognition data prompting the traffic yield condition for the vehicle planned route.
| 15. A vehicle control unit for providing vehicle-to-human communications in an autonomous vehicle operation, the vehicle control unit comprising:
a processor;
memory communicably coupled to the processor and to a plurality of vehicle sensor devices, the memory storing:
a vehicular operations module including instructions that when executed cause the processor to generate vehicle location data including a traffic yield condition from vehicle planned route data and sensor data;
a traffic yield condition module including instructions that when executed cause the processor to:
receive the vehicle location data and human traffic participant data, based on at least some of the plurality of vehicle sensor devices, to detect a human traffic participant being proximal to the traffic yield condition;
when the human traffic participant is proximal to the traffic yield condition, generate message data indicating a vehicle intent for delivery to the human traffic participant; and
an acknowledgment confirmation module including instructions that when executed cause the processor to sense, based on the at least some of the plurality of vehicle sensor devices, whether the human traffic participant acknowledges a receipt of the message by at least one of:
determining human traffic participant attention data indicating whether the human traffic participant comprehends the message; and
determining human traffic participant conduct data responsive to the message;
wherein the acknowledgement confirmation module includes further instructions to, upon sensing that the human traffic participant acknowledges the receipt of the message, generate a vehicle acknowledgment message for delivery to the human traffic participant.
| 16. The vehicle control unit of claim 15, wherein the message comprises at least one of:
a graphic-based message for an external display of the vehicle; and
an audible message for announcement by the vehicle.
| 17. The vehicle control unit of claim 15, wherein the acknowledgment message comprises at least one of:
a graphic-based acknowledgment message for display by an external display of the vehicle; and
an audible acknowledgment message for announcement by an audio system of the vehicle.
| 18. The vehicle control unit of claim 15, wherein the human traffic participant attention data comprises at least one of:
a human traffic participant gaze directed towards a direction of the vehicle;
a human traffic participant gesture directed towards the direction of the vehicle; and
a facial recognition indicating that the human traffic participant faces a direction of the vehicle.
| 19. The vehicle control unit of claim 15, wherein the human traffic participant conduct comprises at least one of:
a human traffic participant velocity vector component slowing to a pedestrian travel rate that yields to the vehicle planned route; and
modifying a human traffic participant vector directional component to one that operates to avoid the vehicle planned route.
| 20. The vehicle control unit of claim 15, wherein the traffic yield condition may be defined by at least one of:
Route Network Description File (RNDF) data;
traffic yield condition data; and
object recognition data relating to the traffic yield condition for the vehicle planned route. | The method (600) involves generating action message indicating vehicle intent for broadcast to a human traffic participant (606). A determination is made (608) to check whether the human traffic participant acknowledges receipt of the action message by a vehicle sensor device by determining human traffic participant attention data indicating that the human traffic participant visually observes the action message by a processor, and by determining human traffic participant movement data to be responsive to the action message. Vehicle acknowledgment message for broadcast to the human traffic participant is generated (616) when the human traffic participant acknowledges the receipt of the action message. An INDEPENDENT CLAIM is also included for a vehicle control unit for providing vehicle-to-human communications for facilitating autonomous operation of a vehicle. Method for providing vehicle-to-human communication for facilitating autonomous operation of a vehicle i.e. car (from drawings). Can also be used for utility vehicles such as lorries and construction vehicles. The method enables allowing vehicle sensor devices to be communicatively coupled to a number of servers by a network cloud, so that a vehicle planned route can be dynamically adjusted based on driving conditions. The method enables operating a human traffic participant detection module to track the human traffic participant for sufficient time duration to determine movement speed and movement direction of the detected human traffic participant in an accurate manner. The drawing shows a flowchart illustrating a method for providing vehicle-to-human communication for facilitating autonomous operation of a vehicle. 600Method for providing vehicle-to-human communication for facilitating autonomous operation of vehicle606Step for generating action message indicating vehicle intent for broadcast to human traffic participant608Step for determining whether human traffic participant acknowledges receipt of action message by vehicle sensor device616Step for generating vehicle acknowledgment message for broadcast to human traffic participant when human traffic participant acknowledges receipt of action message618Step for prompting alternate vehicle action |
Please summarize the input | Systems and methods for controlling the operation of an autonomous vehicle using multiple traffic light detectorsSystems and methods for controlling the operation of an autonomous vehicle are disclosed herein. One embodiment performs traffic light detection at an intersection using a sensor-based traffic light detector to produce a sensor-based detection output, the sensor-based detection output having an associated first confidence level; performs traffic light detection at the intersection using a vehicle-to-infrastructure-based (V2I-based) traffic light detector to produce a V2I-based detection output, the V2I-based detection output having an associated second confidence level; performs one of (1) selecting as a final traffic-light-detection output whichever of the sensor-based detection output and the V2I-based detection output has a higher associated confidence level and (2) generating the final traffic-light-detection output by fusing the sensor-based detection output and the V2I-based detection output using a first learning-based classifier; and controls the operation of the autonomous vehicle based, at least in part, on the final traffic-light-detection output.What is claimed is:
| 1. A system for controlling operation of an autonomous vehicle, the system comprising:
one or more processors; and
a memory communicably coupled to the one or more processors and storing:
a sensor-based traffic light detection module including instructions that when executed by the one or more processors cause the one or more processors to perform sensor-based traffic light detection at an intersection to produce a sensor-based detection output, the sensor-based detection output having an associated first confidence level;
a vehicle-to-infrastructure-based (V2I-based) traffic light detection module including instructions that when executed by the one or more processors cause the one or more processors to perform V2I-based traffic light detection at the intersection to produce a V2I-based detection output, the V2I-based detection output having an associated second confidence level;
a fusion module including instructions that when executed by the one or more processors cause the one or more processors to select as a final traffic-light-detection output whichever of the sensor-based detection output and the V2I-based detection output has a higher associated confidence level subject to an override based on consideration of a risk associated with a transition through the intersection that the autonomous vehicle plans to execute; and
a control module including instructions that when executed by the one or more processors cause the one or more processors to control the operation of the autonomous vehicle based, at least in part, on the final traffic-light-detection output.
| 2. The system of claim 1, wherein the sensor-based traffic light detection module includes instructions to produce the sensor-based detection output by analyzing image data associated with the intersection.
| 3. The system of claim 1, wherein the V2I-based traffic light detection module includes instructions to compute the associated second confidence level, at least in part, by comparing V2I signals received from an information system of the intersection with environmental sensor data associated with the intersection using a learning-based classifier.
| 4. The system of claim 1, wherein the V2I-based traffic light detection module includes instructions to compute the associated second confidence level, at least in part, by processing past sensor-based detection output data and past V2I-based detection output data using a learning-based classifier.
| 5. The system of claim 1, wherein the instructions in the control module to control the operation of the autonomous vehicle based, at least in part, on the final traffic-light-detection output include instructions to control one or more of steering, acceleration, and braking.
| 6. The system of claim 1, wherein the final traffic-light-detection output includes one or more transitions, the one or more transitions corresponding to different possible paths through the intersection, and an estimated traffic light state for each of the one or more transitions.
| 7. The system of claim 6, wherein the final traffic-light-detection output further includes state-timing information for at least one traffic light at the intersection and an overall confidence level for the final traffic-light-detection output.
| 8. A non-transitory computer-readable medium for controlling operation of an autonomous vehicle and storing instructions that when executed by one or more processors cause the one or more processors to:
perform sensor-based traffic light detection at an intersection to produce a sensor-based detection output, the sensor-based detection output having an associated first confidence level;
perform vehicle-to-infrastructure-based (V2I-based) traffic light detection at the intersection to produce a V2I-based detection output, the V2I-based detection output having an associated second confidence level;
select as a final traffic-light-detection output whichever of the sensor-based detection output and the V2I-based detection output has a higher associated confidence level subject to an override based on consideration of a risk associated with a transition through the intersection that the autonomous vehicle plans to execute; and
control the operation of the autonomous vehicle based, at least in part, on the final traffic-light-detection output.
| 9. The non-transitory computer-readable medium of claim 8, wherein the instructions include instructions to compute the associated second confidence level, at least in part, by comparing V2I signals received from an information system of the intersection with environmental sensor data associated with the intersection using a learning-based classifier.
| 10. The non-transitory computer-readable medium of claim 8, wherein the instructions include instructions to compute the associated second confidence level, at least in part, by processing past sensor-based detection output data and past V2I-based detection output data using a learning-based classifier.
| 11. The non-transitory computer-readable medium of claim 8, wherein the final traffic-light-detection output includes one or more transitions, the one or more transitions corresponding to different possible paths through the intersection, and an estimated traffic light state for each of the one or more transitions.
| 12. The non-transitory computer-readable medium of claim 11, wherein the final traffic-light-detection output further includes state-timing information for at least one traffic light at the intersection and an overall confidence level for the final traffic-light-detection output.
| 13. A method of controlling operation of an autonomous vehicle, the method comprising:
performing traffic light detection at an intersection using a sensor-based traffic light detector to produce a sensor-based detection output, the sensor-based detection output having an associated first confidence level;
performing traffic light detection at the intersection using a vehicle-to-infrastructure-based (V2I-based) traffic light detector to produce a V2I-based detection output, the V2I-based detection output having an associated second confidence level;
selecting as a final traffic-light-detection output whichever of the sensor-based detection output and the V2I-based detection output has a higher associated confidence level subject to an override based on consideration of a risk associated with a transition through the intersection that the autonomous vehicle plans to execute; and
controlling the operation of the autonomous vehicle based, at least in part, on the final traffic-light-detection output.
| 14. The method of claim 13, wherein the associated second confidence level is computed, at least in part, by comparing V2I signals received from an information system of the intersection with environmental sensor data associated with the intersection using a learning-based classifier.
| 15. The method of claim 13, wherein the associated second confidence level is computed, at least in part, by processing past sensor-based detection output data and past V2I-based detection output data using a learning-based classifier.
| 16. The method of claim 13, wherein the final traffic-light-detection output includes one or more transitions, the one or more transitions corresponding to different possible paths through the intersection, and an estimated traffic light state for each of the one or more transitions.
| 17. The method of claim 16, wherein the final traffic-light-detection output further includes timing information for at least one traffic light at the intersection and an overall confidence level for the final traffic-light-detection output. | The system (170) has a fusion module (325) having instructions that when executed by processors (110) cause the processors to perform one of selecting as a final traffic-light-detection output whichever of a sensor-based detection output and a vehicle-to-infrastructure-based (V2I-based) detection output has a higher associated confidence level. The fusion module generates the final detection output by fusing the detection outputs using a learning-based classifier. A control module (330) consists of instructions that cause the processor to control an operation of an autonomous vehicle based on the detection output. INDEPENDENT CLAIMS are included for:1) a non-transitory computer-readable medium for controlling operation of an autonomous vehicle; and2) a method for controlling operation of an autonomous vehicle. System for controlling operation of autonomous vehicle using multiple traffic light detectors. The autonomous vehicle can be able to detect traffic lights and their current states to decide whether to stop or proceed or whether it is permissible to turn at a particular time and in a particular direction. The vehicle-to-infrastructure-based (V2I-based) traffic light detection module can be used to improve the accuracy of the final traffic-light-detection output. The fusion module can improve the performance of the autonomous vehicle by fusing the sensor-based detection output and the V2I based detection output using a first learning-based classifier. The drawing shows a block diagram of the traffic light detection system.110Processor 170Traffic light detection system 310Memory 325Fusion module 330Control module |
Please summarize the input | SYSTEMS AND METHODS FOR OPERATING A VEHICLE ON A ROADWAYA host vehicle can detect two or more markers positioned on respective locations of a surrounding vehicle. The two or more markers can include data corresponding to the respective locations of each of the two or more markers. The host vehicle can determine the location of the two or more markers based on the data. Based on the location of the two or more markers, the host vehicle can determine the orientation of the surrounding vehicle. The host vehicle can determine a path to follow based on the determined orientation of the surrounding vehicle. The host vehicle can then follow the determined path.What is claimed is:
| 1. A system for operating a host vehicle on a roadway, the system comprising:
a camera positioned to capture an image of an external environment of the host vehicle;
one or more processors communicably coupled to the camera;
a memory communicably coupled to the one or more processors and storing:
a marker location determination module including instructions that, when executed by the one or more processors, cause the one or more processors to detect, using the image captured by the camera, a first marker and at least a second marker positioned on one or more surfaces of a vehicle surrounding the host vehicle, and determine at least a location of the first and second markers on the surrounding vehicle based on data encoded each of the markers;
an orientation determination module including instructions that, when executed by the one or more processors, cause the one or more processors to determine an orientation of the surrounding vehicle based on the location of the first and second markers; and
an autonomous driving module including instructions that, when executed by the one or more processors, cause the one or more processors to determine a path for the host vehicle based on the orientation of the surrounding vehicle, and to control the host vehicle to follow the determined path by sending one or more control signals to one or more vehicle components to cause the host vehicle to travel along the determined path.
| 2. The system of claim 1, wherein the first and second markers are non-visible markers, and wherein the camera is a hyperspectral camera configured to detect the non-visible first and second markers.
| 3. The system of claim 1, further comprising:
a communications system configured to receive data generated by the surrounding vehicle that is associated with at least one of the first and second markers; and wherein the memory further stores
a feature determination module including instructions that, when executed by the one or more processors, cause the one or more processors to receive, via the communications system, the data generated by the surrounding vehicle, the data indicating a feature related to an area of the surrounding vehicle located proximate at least one of the markers, and determine, based on the retrieved data, the feature corresponding to the area of the surrounding vehicle located proximate to the at least one marker.
| 4. The system of claim 3, wherein the autonomous driving module further includes instructions that cause the one or more processors to monitor a likelihood of a collision with the surrounding vehicle based on the orientation of the surrounding vehicle with respect to the determined path, and, responsive to determining a likely collision with the surrounding vehicle, determine a collision path for the host vehicle based on the orientation of the surrounding vehicle determined by the orientation determination module and the feature for the surrounding vehicle determined by the feature determination module.
| 5. A method of operating for a host vehicle according to an orientation of a surrounding vehicle, the method comprising:
detecting, via a camera on the host vehicle, a first marker positioned on a surface of a surrounding vehicle, the first marker including data corresponding to the first marker's location on the surrounding vehicle;
determining the location of the first marker on the surrounding vehicle based on the data;
detecting, via the camera, a second marker positioned on another surface of the surrounding vehicle, the second marker including data corresponding to the second marker's location on the surrounding vehicle;
determining the location of the second marker on the surrounding vehicle based on the data;
determining an orientation of the surrounding vehicle based on the locations of the first and second markers;
determining a path for the host vehicle based on the orientation of the surrounding vehicle; and
causing the host vehicle to follow the determined path.
| 6. The method of claim 5 further comprising:
receiving data associated with the first marker from the surrounding vehicle, the data representative of one or more features related to an area of the surrounding vehicle proximate the location of the first marker; and
determining a likelihood of collision with the surrounding vehicle.
| 7. The method of claim 6, wherein the received data is representative of one or more features including a fuel level of the surrounding vehicle, and wherein the determined path is a collision path based at least in part on the fuel level of the surrounding vehicle.
| 8. The method of claim 6, wherein the received data is representative of one or more features including an occupant presence in the area of the surrounding vehicle proximate the location the first marker, and wherein the determined path is a collision path based at least in part on the occupant presence in the area of the surrounding vehicle proximate the location of the first marker.
| 9. The method of claim 8, wherein the occupant presence includes a vulnerability assessment for any occupants present in the area of the surrounding vehicle proximate the location of the first marker, and wherein the collision path is further based at least in part on the vulnerability assessment.
| 10. The method of claim 6, wherein the received data is representative of one or more features including a hazardous cargo assessment for the area of the surrounding vehicle proximate the location of the first marker, the hazardous cargo assessment being based on any cargo present in the area of the surrounding vehicle proximate the location of the first marker, and wherein the determined path is a collision path based at least in part on the hazardous cargo assessment for the area of the surrounding vehicle proximate the location of the first marker.
| 11. The method of claim 5, further comprising:
determining a location of the surrounding vehicle relative to the host vehicle, and wherein determining the path for the host vehicle is further based on the determined location of the surrounding vehicle.
| 12. A system for providing information from a first vehicle to at least a second vehicle related to one or more features of the first vehicle, the system comprising:
a marker positioned on an external surface of the first vehicle, the marker including data corresponding to the marker's location on the first vehicle;
a communications system;
one or more processors;
a memory communicably coupled to the one or more processors and storing:
a feature detection module including instructions that, when executed by the one or more processors, cause the one or more processors to detect one or more features corresponding to an area of the first vehicle proximate the location of the marker; and
a data generation module including instructions that, when executed by the one or more processors, cause the one or more processors to generate data for the marker representing the determined one or more features, and to transmit, via the communications system, the data for the marker corresponding to the detected one or more features, the data being accessible by the second vehicle.
| 13. The system of claim 12, wherein the marker positioned on the external surface of the first vehicle is a non-visible marker.
| 14. The system of claim 12, wherein the communications system is a vehicle-to-vehicle communications system, and wherein the instructions included on data generation module cause the one or more processors to transmit, via the vehicle-to-vehicle communications system, the data for the marker corresponding to the detected one or more features to the second vehicle.
| 15. The system of claim 12, wherein the communication system is in communication with a database accessible by the second vehicle, and wherein the instructions included on data generation module cause the one or more processors to transmit, via the communication system, the data for the marker corresponding to the detected one or more features to the database, the database being accessible by the second vehicle.
| 16. The system of claim 12, wherein the marker is positioned on a fuel door of the first vehicle, and wherein the data generated by the data generation module includes an energy level for the first vehicle.
| 17. The system of claim 12, wherein the marker is positioned proximate to a cargo area of the first vehicle, and wherein the data generated by the data generation module includes a cargo risk assessment associated with the cargo area.
| 18. The system of claim 12, wherein the marker is positioned on an occupant door for the first vehicle, and wherein the data generated by the data generation module indicates presence of an occupant in the area of the first vehicle proximate the location of the marker.
| 19. The system of claim 18 wherein the data generated by the data generation module further includes a vulnerability of any occupants present in the area of the first vehicle proximate the location of the marker on the first vehicle. | The system has a camera that is positioned to capture an image of an external environment of a host vehicle (200). The processors are communicably coupled to the camera. A marker location determination module detects the first and second markers (110) positioned on surfaces of a vehicle surrounding the host vehicle, and determines a location of the first and second markers on a surrounding vehicle (100a-100c) based on data encoded each of the markers using the image captured by the camera. An orientation determination module determines an orientation of the surrounding vehicle based on the location of the first and second markers. An autonomous driving module determines a path for the host vehicle based on the orientation of the surrounding vehicle and controls the host vehicle to follow the determined path by sending control signals to vehicle components to cause the host vehicle to travel along the determined path. INDEPENDENT CLAIMS are included for the following:a method of operating for a host vehicle according to an orientation of a surrounding vehicle; anda system for providing information from a first vehicle to at least a second vehicle related to one or more features of the first vehicle. System for operating host vehicle such as conventional vehicle and autonomous vehicle on roadway. The redundancies can permit data to be extrapolated from the steganographic pattern, even if the steganographic pattern is damaged. The determined path can be a path that avoids a collision with the surrounding vehicle. The autonomous driving modules can determine paths for the host vehicle so as to avoid particular areas of the surrounding vehicle in the event of a likely collision with the surrounding vehicle. The drawing shows a perspective view of host vehicle on a roadway including multiple surrounding vehicles. 100a-100cSurrounding vehicles110Markers200Host vehicle215Sensor system |
Please summarize the input | Dynamic speed limit for vehicles and autonomous vehiclesSystems and methods are provided for operating a vehicle using dynamic speed limits. A vehicle can monitor current road conditions by capturing real-time data that is indicative of a driving environment associated with a roadway being traversed by the vehicle. Using the captured real-time data, the vehicle can predict a dynamic speed limit, wherein the dynamic speed limit is a driving speed for the vehicle that is adapted for the monitored current road conditions. Additionally, the vehicle can automatically perform a driving operation for the vehicle in accordance with the dynamic speed limit, wherein the driving operation causes the vehicle to move at a driving speed that is approximately equal to the predicted dynamic speed limit.What is claimed is:
| 1. A method comprising:
monitoring, by a vehicle, current road conditions using captured real-time data indicative of a driving environment associated with a roadway being traversed by the vehicle, wherein monitoring the current road conditions comprises:
capturing the real-time data from one or more vehicle sensors of the vehicle, wherein the captured real-time data is from one or more vehicle sensors sensing the current road conditions associated with the roadway being traversed by the vehicle; and
collecting federated real-time data from multiple communication points communicatively connected to the vehicle and a plurality of dynamic speed limits predicted by other vehicles, the federated real-time data indicating the driving environment associated with the roadway being traversed by the vehicle, and wherein collecting the federated real-time data and the plurality of dynamic speed limits predicted by other vehicles comprises receiving data via vehicle-to-vehicle (V2V) communication between the vehicle and one or more other vehicles on the roadway;
predicting, by the vehicle, a dynamic speed limit based on the captured real-time data from the one or more vehicle sensors of the vehicle, wherein the predicted dynamic speed limit comprises a driving speed for the vehicle that is adapted for the monitored current road conditions;
optimizing the predicted dynamic speed limit by applying the federated real-time data and the plurality of dynamic speed limits predicted by other vehicles to analysis of the dynamic speed limit to generate an optimized dynamic speed limit when the predicted dynamic speed limit is not verified; and
automatically performing a driving operation for the vehicle in accordance with the optimized dynamic speed limit, wherein the driving operation causes the vehicle to move at the driving speed that is approximately equal to the optimized dynamic speed limit when the predicted dynamic speed limit is not verified.
| 2. The method of claim 1, wherein predicting the dynamic speed limit comprises applying the captured real-time data to one or more machine learning models.
| 3. The method of claim 2, further comprising:
determining whether the predicted dynamic speed limit is verified based on the federated real-time data, wherein determining that the predicted dynamic speed limit is verified comprises identifying a convergence between the federated real-time data and the captured real-time data and determining that the predicted dynamic speed limit is not verified comprises identifying a divergence between the federated real-time data and the captured real-time data;
upon determining that the predicted dynamic speed limit is not verified, generating the optimized dynamic speed limit, wherein the optimized dynamic speed limit is based on the predicted dynamic speed limit and optimally adapted to the monitored current road conditions in accordance with federated learning techniques applied to the one or more machine learning models; and
upon determining that the predicted dynamic speed limit is not verified, performing the driving operation for the vehicle in accordance with the optimized dynamic speed limit, wherein the driving operation causes the vehicle to move at the driving speed that is approximately equal to the optimized dynamic speed limit.
| 4. The method of claim 1, wherein performing the driving operation for the vehicle in accordance with the predicted dynamic speed limit is fully autonomous.
| 5. The method of claim 1, wherein performing the driving operation for the vehicle in accordance with the predicted dynamic speed limit is semi-autonomous.
| 6. The method of claim 5, wherein performing the driving operation for the vehicle in accordance with the predicted dynamic speed limit comprises automatically displaying the predicted dynamic speed limit in a dashboard display of the vehicle.
| 7. The method of claim 1, wherein the collecting federated real-time data from multiple communication points further comprises vehicle-to-infrastructure (V2I) communication between the vehicle and an infrastructure device.
| 8. The method of claim 3, further comprising:
upon determining that the predicted dynamic speed limit is verified, performing the driving operation for the vehicle in accordance with the predicted dynamic speed limit, wherein the driving operation causes the vehicle to move at the driving speed that is approximately equal to the predicted dynamic speed limit. | The method involves monitoring current road conditions by capturing real-time data indicative of a driving environment associated with a roadway traversed by the vehicle (120). A dynamic speed limit is predicted based on the captured real-time data by the vehicle. The dynamic speed limit includes a driving speed for the vehicle, that is adapted for the monitored current road conditions. A driving operation is automatically performed for the vehicle in accordance with the predicted dynamic speed limit. The driving operation causes the vehicle to move at a driving speed that is approximately equal to the predicted dynamic speed limit. The captured real-time data is applied to multiple machine learning models. An INDEPENDENT CLAIM is included for a system for controlling dynamic speed limits of vehicle. Method for controlling dynamic speed limits of vehicle, such as trucks, motorcycles and boats. Vehicle can automatically generate an alert notifying the driver of the dynamic speed limit. Machine learning aspects of dynamic speed limit module are enhanced. Reliability of the predicted dynamic speed limit is improved. The drawing shows a block diagram of vehicle-infrastructure system. 100Vehicle-infrastructure system102Road condition service105Static speed limit sign110Communication network120Vehicle121Camera125Dynamic speed limit module |
Please summarize the input | DYNAMIC SPEED LIMIT FOR VEHICLES AND AUTONOMOUS VEHICLESSystems and methods are provided for operating a vehicle using dynamic speed limits. A vehicle can monitor current road conditions by capturing real-time data that is indicative of a driving environment associated with a roadway being traversed by the vehicle. Using the captured real-time data, the vehicle can predict a dynamic speed limit, wherein the dynamic speed limit is a driving speed for the vehicle that is adapted for the monitored current road conditions. Additionally, the vehicle can automatically perform a driving operation for the vehicle in accordance with the dynamic speed limit, wherein the driving operation causes the vehicle to move at a driving speed that is approximately equal to the predicted dynamic speed limit.What is claimed is:
| 1. A system comprising:
a plurality of vehicles communicatively coupled to transmit real-time data that is captured by each of the plurality of vehicles, wherein the captured real-time data is indicative of a driving environment associated with a roadway being traversed by the plurality of vehicles;
an infrastructure device communicatively coupled to the plurality of vehicles for transmitting initial speed limit data; and
at least one of the plurality of vehicles comprising a control unit configured to:
receive the real-time data from the plurality of vehicles;
receive the initial speed limit data from the infrastructure device;
dynamically adjust the initial speed limit data based on a consensus of the real-time data received from the plurality of vehicles; and
automatically perform a driving operation for the at least one vehicle in accordance with the dynamically adjusted speed limit, wherein the driving operation causes the at least one vehicle to move at a driving speed that is approximately equal to the dynamically adjusted speed limit.
| 2. The system of claim 1, wherein the communicative coupling between the plurality of vehicles comprises vehicle-to-vehicle (V2V) communication.
| 3. The system of claim 3, wherein the plurality of vehicles transmits additional dynamically adjusted speed limits using the V2V communication, and the additional dynamically adjusted speed limits are based on the real-time data captured by the respective one or more plurality of vehicles.
| 4. The system of claim 4, wherein the at least one vehicle receives the additional dynamically adjusted speed limits transmitted by the plurality of vehicles, and dynamically adjusting the initial speed limit is further based on a consensus of the additional dynamically adjusted speed limits.
| 5. The system of claim 1, wherein the at least one vehicle receives real-time data relating to the driving environment associated with the roadway comprising at least one of: traffic data, weather data, hazard data, and location data.
| 6. The system of claim 1, wherein the communicative coupling between the plurality of vehicles and the infrastructure device comprises vehicle-to-infrastructure (V2I) communication.
| 7. A vehicle comprising:
a processor device, wherein the processor device:
captures real-time data from one or more vehicle sensors of the vehicle, wherein the captured real-time data is from one or more vehicle sensors sensing current road conditions associated with a roadway being traversed by the vehicle;
receives federated real-time data from multiple communication points communicatively connected to the vehicle and a plurality of predicted dynamic speed limits generated by a plurality of connected vehicles;
verifies a predicted dynamic speed limit by applying the federated real-time data and the plurality of predicted dynamic speed limits to the predicted dynamic speed limit; and
in response to verifying the predicted dynamic speed limit, automatically performing a driving operation for the vehicle in accordance with the verified predicted dynamic speed limit, wherein the driving operation causes the vehicle to move at a driving speed that is approximately equal to the verified predicted dynamic speed limit.
| 8. The vehicle of claim 7, wherein the processor device further:
generates the predicted dynamic speed limits based on the real-time data indicative of the current road conditions.
| 9. The vehicle of claim 7, wherein verifying the predicted dynamic speed limit comprises determining whether there is consensus between the real-time data captured from the vehicle sensors and the federated real-time data collected from the multiple communication points.
| 10. The vehicle of claim 9, wherein verifying the predicted dynamic speed limit comprises a convergence between the real-time data captured from the vehicle sensors and the federated real-time data collected from the multiple communication points.
| 11. The vehicle of claim 7, wherein verifying the predicted dynamic speed limit comprises determining whether there is consensus between the predicted dynamic speed limit and the plurality of predicted dynamic speed limits generated by the plurality of connected vehicles.
| 12. The vehicle of claim 7, wherein the processor device further:
in response to failing to verify the predicted dynamic speed limit, adjusts the predicted dynamic speed limit based on the plurality of predicted dynamic speed limits generated by the plurality of connected vehicles.
| 13. The vehicle of claim 12, wherein adjusting the predicted dynamic speed limit comprises applying the plurality of predicted dynamic speed limits generated by the plurality of connected vehicles to a machine learning model to optimize the predicted dynamic speed limit.
| 14. The vehicle of claim 7, further comprising a communication system communicatively connected to the multiple communication points and the plurality of connected vehicles.
| 15. The vehicle of claim 7, wherein the communicative connection comprises vehicle-to-infrastructure (V2I) communication or vehicle-to-vehicle (V2V) communication.
| 16. The vehicle of claim 7, wherein the multiple communication points comprise one or more of: vehicles, infrastructure devices, and road condition services. | The system (100) has a set of vehicles that are communicatively coupled to transmit real-time data that is captured by each of the vehicles, where the captured data is indicative of a driving environment associated with a roadway being traversed by the vehicles. An infrastructure device is coupled to the vehicles for transmitting initial speed limit data. The vehicles is provided with a control unit to receive the data and the initial data from an infrastructure device. The control unit automatically performs a driving operation for the vehicle in accordance with a dynamically adjusted speed limit, where driving operation causes the vehicle to move at a driving speed that is approximately equal to the dynamically adjusted limit. An INDEPENDENT CLAIM is included for a vehicle comprising: a processor device. System for dynamic cruise control of vehicle such as automobiles, trucks, motorcycles, boats, recreational vehicles. The system utilizes a vehicle computing system to employ dynamic speed limits using real-time traffic monitoring, and federated learning from multiple communication points, allows the vehicles to use dynamic speed limit and updated in tandem with the changing road conditions to enhance navigation and/or maneuver capabilities. The drawing shows an example of a vehicle-infrastructure system with which the dynamic speed limit systems.100Vehicle-infrastructure system 102Road conditions services 103Infrastructure device 105Static speed limit sign 110Communications network 120Autonomous vehicle 121Camera 125Dynamic speed limits module |
Please summarize the input | Vehicular headlight warning systemA method is provided for alerting a driver of a vehicle of an unsafe exterior lighting status. The method includes receiving, at a first vehicle, information sufficient to detect a presence of a second vehicle. Once a vehicle is detected, the method continues to identify a direction of travel of the second vehicle with respect to the first vehicle. The method includes measuring a level of ambient light, and determining the level of ambient light is below a threshold level. The method further identifies any presence of functioning exterior lighting of the second vehicle by detecting a light color and light intensity. When an unsafe exterior lighting status is determined, the method includes alerting a driver of the second vehicle using a vehicle-to-vehicle communication signal. The signal includes a notification suggesting usage of at least one of headlights, parking lights, and hazard lights.What is claimed is:
| 1. A method for alerting a vehicle driver of an exterior lighting operational status, the method comprising:
detecting, at a first vehicle, a presence of a second vehicle;
measuring a level of ambient light, and determining the level of ambient light is below a threshold level;
determining an operational status of a specific type of functioning exterior lighting of the second vehicle including detecting a light color, angle, and light intensity to identify a type of the exterior lighting; and
alerting a driver of the second vehicle using a vehicle-to-vehicle communication signal, the signal including a notification related to the operational status of the exterior lighting of the second vehicle and suggesting usage of exterior lighting different from the specific type identified.
| 2. The method according to claim 1, further comprising identifying a direction of travel of the second vehicle with respect to the first vehicle.
| 3. The method according to claim 1, further comprising identifying the second vehicle as an oncoming vehicle moving in a direction towards the first vehicle,
wherein determining the operational status of the exterior lighting results in a determination that headlights are not operating, and the signal includes a notification suggesting usage of headlights operated in a low beam mode.
| 4. The method according to claim 1, further comprising identifying the second vehicle as an oncoming vehicle moving in a direction towards the first vehicle,
wherein determining the operational status of the exterior lighting results in a determination that headlights are operating in a high beam mode, and the signal includes a notification suggesting usage of the headlights in a low beam mode.
| 5. The method according to claim 4, wherein, upon the first vehicle passing the second vehicle, the method further comprises sending a follow-up vehicle-to-vehicle communication signal to the second vehicle, the follow-up signal including a notification suggesting that usage of the headlights can safely revert back to the high beam mode.
| 6. The method according to claim 1, further comprising identifying the second vehicle as an oncoming vehicle moving in a direction towards the first vehicle,
wherein determining the operational status of the exterior lighting results in a determination that only parking lights are operating, and the signal includes a notification suggesting usage of headlights operated in a low beam mode.
| 7. The method according to claim 1, further comprising identifying the second vehicle as a stationary vehicle; and
determining the second vehicle is parked or temporarily standing adjacent an intended travel path of the first vehicle,
wherein determining the operational status of the exterior lighting results in a determination that the exterior lighting is off, and the signal includes a notification suggesting usage of parking lights or hazard lights.
| 8. The method according to claim 7, further comprising alerting a driver of the first vehicle to the presence and location of the second vehicle.
| 9. The method according to claim 1, further comprising monitoring for a presence of precipitation.
| 10. The method according to claim 9, further comprising detecting a presence of precipitation,
wherein determining the operational status of the exterior lighting results in a determination that headlights are not operating, and the signal includes a notification suggesting usage of headlights operated in a low beam mode.
| 11. The method according to claim 1, wherein at least one of the first vehicle and the second vehicle is an autonomous vehicle.
| 12. A method for alerting a vehicle driver of an unsafe exterior lighting operational status, the method comprising:
receiving and processing, at a first vehicle, information sufficient to detect a presence of a second vehicle;
identifying a direction of travel of the second vehicle with respect to the first vehicle;
measuring a level of ambient light, and determining the level of ambient light is below a threshold level;
identifying a specific type of functioning exterior lighting of the second vehicle by detecting a light color, light angle, and light intensity; and
alerting a driver of the second vehicle using a vehicle-to-vehicle communication signal, the signal including a notification suggesting usage of exterior lighting different from the specific type identified.
| 13. A non-transitory computer-readable medium having instructions embodied thereon that, when executed by a processor, perform operations in a first vehicle, the operations comprising:
detecting a presence of a second vehicle;
measuring a level of ambient light, and determining the level of ambient light is below a threshold level;
determining an operational status of a specific type of functioning exterior lighting of the second vehicle, including detecting a light color, light angle, and light intensity to identify a type of the exterior lighting; and
alerting a driver of the second vehicle using a vehicle-to-vehicle communication signal, the signal including a notification related to the operational status of the exterior lighting of the second vehicle, and suggesting usage of exterior lighting different from the specific type identified.
| 14. The non-transitory computer-readable medium as recited in claim 13, wherein the operations further comprise:
identifying a direction of travel of the second vehicle with respect to the first vehicle.
| 15. The non-transitory computer-readable medium as recited in claim 14, wherein the operations further comprise:
identifying the second vehicle as a stationary vehicle and determining the second vehicle is parked or temporarily standing adjacent an intended travel path of the first vehicle; and
alerting a driver of the first vehicle of the presence of the second vehicle,
wherein determining the operational status of the exterior lighting results in a determination that the exterior lighting is off, and the signal includes a notification suggesting usage of parking lights or hazard lights.
| 16. The non-transitory computer-readable medium as recited in claim 14, wherein the operations further comprise:
identifying the second vehicle as an oncoming vehicle moving in a direction towards the first vehicle,
wherein determining the operational status of the exterior lighting results in a determination that headlights are operating in a high beam mode, and the signal includes a notification suggesting the headlights be operated in a low beam mode.
| 17. The non-transitory computer-readable medium as recited in claim 14, wherein the operations further comprise:
identifying the second vehicle as an oncoming vehicle moving in a direction towards the first vehicle,
wherein determining the operational status of the exterior lighting results in a determination that only parking lights are operating, and the signal includes a notification suggesting usage of headlights operated in a low beam mode.
| 18. The non-transitory computer-readable medium as recited in claim 14, wherein the operations further comprise:
identifying the second vehicle as an oncoming vehicle moving in a direction towards the first vehicle,
wherein determining the operational status of the exterior lighting results in a determination that headlights are not operating, and the signal includes a notification suggesting usage of headlights operated in a low beam mode. | The method (48) involves detecting (60) presence of a second vehicle at a first vehicle, measuring (64) level of ambient light and determining if the level of ambient light is below a threshold level. An operational status of exterior lighting of the second vehicle is determined (66). The light color, angle and light intensity to identify type of the exterior lighting are determined (68). The driver of the second vehicle is alerted (70) using a vehicle-to-vehicle communication signal that includes a notification related to the operational status of the exterior lighting of the second vehicle. An INDEPENDENT CLAIM is included for a non-transitory computer-readable medium storing instructions to perform operations in a first vehicle. Method for alerting vehicle driver of exterior lighting operational status while driving vehicles such as passenger or commercial automobile, car, truck, motorcycle, off-road vehicle, bus, boat, airplane, helicopter, lawn mower, recreational vehicle, amusement park vehicle, farm vehicle, construction vehicle, tram, golf cart, train, or trolley, etc,. The vehicles are modified either visually or electronically so their operators can consider turning on appropriate exterior lighting for the driving conditions, once an unsafe condition is determined. The environmental conditions are monitored for presence of precipitation as rain, snow, ice, slush, fog, etc., and that impairs visibility such that certain exterior lighting should be in an operational mode. The drawing shows a flow diagram illustrating control process algorithm for alerting vehicle driver of exterior lighting operational status. 48Method for alerting vehicle driver of exterior lighting operational status60Step for detecting presence of second vehicle at first vehicle64Step for measuring level of ambient light66Step for determining operational status of exterior lighting of second vehicle68Step for determining light color and light intensity to identify type of exterior lighting70Step for alerting the driver of second vehicle using vehicle-vehicle communication signal |