A vehicle-to-everything (V2X) IMU calibration and testing system and method

Abstract

This invention discloses an IMU calibration and detection system and method based on vehicle-to-everything (V2X) technology, belonging to the field of autonomous driving and V2X technology. It includes: a roadside perception unit for real-time monitoring of the motion attitude of an autonomous vehicle; an edge computing unit for constructing a vehicle motion model using acceleration and angular velocity in the XYZ directions and calculating the true values ​​of the vehicle's actual motion parameters; an onboard unit including an onboard IMU and an onboard OBU; a cloud platform communicatively connected to both the edge computing unit and the onboard OBU, for receiving the true values ​​of the vehicle's actual motion parameters and data collected by the onboard IMU, comparing the deviations between the two, and generating control commands; and an intelligent driving controller deployed on the autonomous vehicle for receiving control commands from the cloud platform and performing location source switching, IMU self-calibration, or maintenance operations. This invention can improve the accuracy of IMU calibration and the safety of assisted driving vehicles.

Description

Technical Field

[0001] This invention belongs to the field of autonomous driving and vehicle networking technology, and particularly relates to an IMU calibration and detection system and method based on vehicle networking. Background Technology

[0002] With the continuous advancement and widespread adoption of driver assistance technologies, many vehicles are now equipped with varying degrees of autonomous driving capabilities. In these systems, the accuracy of the vehicle's positioning system calibration is crucial, directly determining whether the autonomous vehicle's trajectory precisely meets the expected requirements. Traditional inertial measurement units (IMUs) installed in autonomous vehicles often lack regular recalibration testing, leading to a potential decrease in accuracy over time. Once positioning deviations occur, the autonomous vehicle's control system may be unable to correctly adjust the driving path, resulting in trajectory deviations. In severe cases, this can even cause the vehicle to deviate from its lane, increasing the risk of collision and posing a potential threat to driving safety. Summary of the Invention

[0003] The purpose of this invention is to provide an IMU calibration and detection system and method based on vehicle networking, which can improve the accuracy of IMU calibration and assist the safety of driving vehicles.

[0004] To achieve the above-mentioned objectives, the first objective of this invention is to provide an IMU calibration and detection system based on vehicle networking, comprising:

[0005] The roadside sensing unit is used to monitor the motion attitude of autonomous vehicles in real time and collect the actual acceleration and angular velocity of the vehicle in the XYZ directions. The edge computing unit is connected to the roadside sensing unit. It uses the acceleration and angular velocity in the XYZ directions to construct a vehicle motion model and calculate the true values ​​of the actual motion parameters of the vehicle. The vehicle-mounted unit includes an on-board IMU and an on-board OBU. The on-board IMU is used to collect the vehicle's own acceleration and angular velocity data, and the on-board OBU is used to upload the acceleration and angular velocity data collected by the on-board IMU. The cloud platform is connected to the edge computing unit and the vehicle-mounted OBU respectively, and is used to receive the true value of the actual motion parameters of the vehicle and the data collected by the vehicle-mounted IMU, compare the deviation between the two and generate control commands. The intelligent driving controller is installed on the autonomous vehicle to receive control commands from the cloud platform and perform location source switching, IMU self-calibration, or maintenance operations.

[0006] Furthermore, the roadside sensing unit includes a camera and a lidar mounted on a roadside pole.

[0007] Furthermore, the edge computing unit first receives 2D information from the camera and 3D information from the lidar, fuses the 2D and 3D information, then constructs a vehicle motion model and calculates the true values ​​of the actual motion parameters of the vehicle.

[0008] Furthermore, the step of comparing the deviations between the two and generating control commands includes: The system determines whether the feedback value from the vehicle-mounted IMU meets the requirements based on the deviation between the two. If the IMU calibration parameters are determined to be non-compliant, the cloud platform will issue an instruction to the intelligent driving vehicle controller to suspend the use of the positioning information input by the IMU, receive the acceleration and angular velocity in the XYZ directions transmitted by the edge computing unit, and simultaneously start the IMU to perform dynamic self-calibration for driving.

[0009] Furthermore, if the feedback value input by the IMU still does not meet the requirements after three attempts, it will be reported to the service backend system, the IMU will be calibrated as faulty, and the vehicle will be controlled to drive into the service station for repair.

[0010] Furthermore, the deviation includes acceleration value deviation and angular velocity value deviation, and the comparison of the two deviations and generation of control commands includes: When the acceleration value deviation is ≤0.5 and the angular velocity value deviation is ≤0.3, it is determined that the IMU calibration parameters meet the requirements, and no command will be issued. When the acceleration value deviation is greater than 0.5 or the angular velocity value deviation is greater than 0.3, it is determined that the IMU calibration parameters do not meet the requirements. Instruction No. 1 will be issued, and the intelligent driving controller will suspend the use of the positioning information input by the IMU, receive the acceleration and angular velocity of the autonomous vehicle in the XYZ directions transmitted by the edge computing unit, and simultaneously start the IMU to perform driving dynamic self-calibration.

[0011] Furthermore, it also includes a GPS system, which is installed on the top of the vehicle to receive GPS signals and transmit them to the intelligent driving vehicle controller for the intelligent driving controller to fuse vehicle positioning information.

[0012] A second objective of this invention is to provide an IMU calibration and detection method based on vehicle-to-everything (V2X) communication, comprising: S1. Truth Value Construction: The edge computing unit integrates data from the roadside perception unit to calculate the actual acceleration and angular velocity of the autonomous vehicle in the XYZ directions, and sends them to the cloud platform as the truth values ​​of the vehicle's actual motion parameters. S2. Deviation detection: The cloud platform receives the acceleration and angular velocity values ​​in the XYZ directions uploaded by the vehicle-mounted IMU and compares them with the true values ​​of the actual vehicle motion parameters sent by the edge computing unit. S3. Command Issuance and Execution: Based on the comparison difference, it is determined whether the IMU calibration parameters meet the requirements. If they do not meet the requirements, the cloud platform issues Command No. 1. In response to Command 1, the intelligent driving controller suspends the use of onboard IMU data, switches to receiving the true values ​​of the actual vehicle motion parameters transmitted by the edge computing unit for positioning fusion, and simultaneously starts the onboard IMU for driving dynamic self-calibration. S4. Re-inspection and closed-loop: After calibration, the cloud platform compares the data again; if the acceleration value deviation is ≤0.5 and the angular velocity value deviation is ≤0.3, a second command is issued to restore the vehicle-mounted IMU to use. If the deviation still does not meet the requirements, issue Command No. 3 to control the vehicle to continue receiving data from the edge computing unit and restart the self-calibration; If the data still does not meet the requirements after three self-calibrations, the cloud platform reports to the service backend, which then controls the vehicle to be driven to the service station for repair.

[0013] Furthermore, in S3, when the acceleration value deviation is greater than 0.5 or the angular velocity value deviation is greater than 0.3, it is determined that the IMU calibration parameters do not meet the requirements, and the cloud platform issues Command No. 1.

[0014] Furthermore, the edge computing unit integrates data from the roadside perception unit by receiving 2D information from the camera and 3D information from the lidar, and then integrating the 2D and 3D information.

[0015] The advantages and positive effects of this application are: This invention, based on vehicle-to-everything (V2X) technology, utilizes roadside sensing devices, including cameras and LiDAR, to collect real-time motion posture data of autonomous vehicles, including acceleration and angular velocity in the X, Y, and Z directions. This data is transmitted to a cloud platform via an edge computing unit and compared with the acceleration and angular velocity information in the X, Y, and Z directions transmitted by the vehicle's onboard IMU. The cloud platform determines whether the feedback value from the onboard IMU meets the requirements. If the IMU calibration parameters are deemed unacceptable, the cloud platform issues a command to suspend the use of the IMU's positioning information, receive the acceleration and angular velocity data in the X, Y, and Z directions transmitted by the edge computing unit, and simultaneously restart the IMU for dynamic self-calibration. If this process is repeated three times and the IMU input information still does not meet the requirements, the issue is reported to the service backend system, indicating an IMU calibration fault. The vehicle is then directed to a service station for repair, thereby improving the safety of autonomous driving and reducing the risk of collisions due to deviation from the vehicle's intended path. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a logic block diagram of a preferred embodiment of the present invention; Figure 2 This is a system schematic diagram of a preferred embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] A vehicle-to-everything (V2X) IMU calibration and testing system mainly includes: The roadside sensing unit is used to monitor the motion attitude of autonomous vehicles in real time and collect the actual acceleration and angular velocity of the vehicle in the XYZ directions. The roadside sensing unit mainly includes cameras and lidar arranged on roadside poles.

[0020] The edge computing unit is connected to the roadside sensing unit. It uses the acceleration and angular velocity in the XYZ directions to construct a vehicle motion model and calculate the true values ​​of the actual motion parameters of the vehicle. The edge computing unit first receives 2D information from the camera and 3D information from the lidar, then fuses the 2D and 3D information to build a vehicle motion model and calculate the true values ​​of the vehicle's actual motion parameters.

[0021] The vehicle-mounted unit includes an on-board IMU and an on-board OBU. The on-board IMU is used to collect the vehicle's own acceleration and angular velocity data, and the on-board OBU is used to upload the acceleration and angular velocity data collected by the on-board IMU. The cloud platform is connected to the edge computing unit and the vehicle-mounted OBU respectively, and is used to receive the true value of the actual motion parameters of the vehicle and the data collected by the vehicle-mounted IMU, compare the deviation between the two and generate control commands. The process of comparing the deviations between the two and generating control commands includes: The system determines whether the feedback value from the vehicle-mounted IMU meets the requirements based on the deviation between the two. If the IMU calibration parameters are determined to be non-compliant, the cloud platform will issue an instruction to the intelligent driving vehicle controller to suspend the use of the positioning information input by the IMU, receive the acceleration and angular velocity in the XYZ directions transmitted by the edge computing unit, and simultaneously start the IMU to perform dynamic self-calibration for driving.

[0022] If the feedback value input by the IMU still does not meet the requirements after three attempts, the system will report the issue to the service backend system, the IMU will calibrate the fault, and the vehicle will be directed to the service station for repair.

[0023] The deviation includes acceleration value deviation and angular velocity value deviation, and the comparison of the two deviations and generation of control commands includes: When the acceleration value deviation is ≤0.5 and the angular velocity value deviation is ≤0.3, it is determined that the IMU calibration parameters meet the requirements, and no command will be issued. When the acceleration value deviation is greater than 0.5 or the angular velocity value deviation is greater than 0.3, it is determined that the IMU calibration parameters do not meet the requirements. Instruction No. 1 will be issued, and the intelligent driving controller will suspend the use of the positioning information input by the IMU, receive the acceleration and angular velocity of the autonomous vehicle in the XYZ directions transmitted by the edge computing unit, and simultaneously start the IMU to perform driving dynamic self-calibration.

[0024] The intelligent driving controller is installed on the autonomous vehicle to receive control commands from the cloud platform and perform location source switching, IMU self-calibration, or maintenance operations.

[0025] Please see Figures 1 to 2 In one specific embodiment, the system includes: The roadside camera 1 is installed above the roadside pole and uses a 10-megapixel wide dynamic range vision sensor to monitor the actual acceleration and angular velocity of the autonomous vehicle in the XYZ directions in real time and transmit them to the edge computing unit. It is mainly used for daytime scenarios and simultaneously transmits the collected information to the edge computing unit for visual fusion with the roadside LiDAR. The roadside LiDAR 2 is deployed above the roadside poles. It is a solid-state LiDAR that monitors the actual acceleration and angular velocity of the autonomous vehicle in the XYZ directions in real time and transmits them to the edge computing unit. It is mainly used for nighttime scenes. Simultaneously, the collected information is transmitted to the edge computing unit for visual fusion with the roadside camera. Edge computing unit 3 receives perception information transmitted from roadside cameras and roadside lidar, fuses the 2D information from the cameras and the 3D information from the lidar to construct a motion model of the autonomous vehicle, calculates the actual acceleration and angular velocity of the autonomous vehicle in the XYZ directions, and sends it to the cloud platform as a truth system for calibration reference of the on-board IMU and for instruction judgment of the cloud platform. The cloud platform 4 receives the acceleration and angular velocity values ​​in the XYZ directions transmitted by the vehicle-mounted IMU and compares them with the values ​​of the edge computing unit. The cloud platform will then issue instructions based on the comparison values. The acceleration and angular velocity values ​​in the XYZ directions received from the vehicle-mounted IMU are compared with the values ​​from the edge computing unit. If the acceleration value deviation is ≤0.5 and the angular velocity value deviation is ≤0.3, it is determined that the IMU calibration parameters meet the requirements, and no command will be issued. The system receives acceleration and angular velocity values ​​in the XYZ directions from the onboard IMU and compares them with the values ​​from the edge computing unit. If the acceleration value deviation is >0.5 or the angular velocity value deviation is >0.3, it is determined that the IMU calibration parameters do not meet the requirements. The system will then issue command number one, and the intelligent driving vehicle controller will suspend the use of the positioning information input by the IMU, receive the acceleration and angular velocity values ​​in the XYZ directions of the autonomous vehicle transmitted by the edge computing unit, and simultaneously start the IMU to perform driving dynamic self-calibration. The system receives the acceleration and angular velocity values ​​in the XYZ directions from the recalibrated IMU and compares them with the values ​​from the edge computing unit. If the acceleration value deviation is ≤0.5 and the angular velocity value deviation is ≤0.3, it is determined that the IMU calibration parameters meet the requirements. Then, it issues the second command, and the intelligent driving vehicle controller stops using the positioning information input by the edge computing unit, receives the acceleration and angular velocity values ​​in the XYZ directions transmitted by the on-board IMU, and performs positioning fusion. The system receives the recalibrated IMU's acceleration and angular velocity values ​​in the XYZ directions and compares them with the values ​​from the edge computing unit. If the acceleration value deviation is >0.5 or the angular velocity value deviation is >0.3, it is determined that the IMU calibration parameters do not meet the requirements. In this case, the system will issue command number three. The intelligent driving vehicle controller will continue to receive the acceleration and angular velocity values ​​in the XYZ directions of the autonomous vehicle transmitted by the edge computing unit and simultaneously restart the IMU to perform driving dynamic self-calibration. If this is repeated three times and the information input by the IMU still does not meet the requirements, the system will report the IMU calibration fault to the service backend system and control the vehicle to drive to the service station for repair. The roadside RSU5 is installed above the roadside poles. It receives the command information transmitted by the cloud platform and transmits the command to the on-board OBU via LTEV communication to control the autonomous driving controller. At the same time, it uploads the acceleration and angular velocity values ​​in the XYZ directions of the vehicle status fed back by the IMU of the autonomous vehicle transmitted by the on-board OBU for the cloud platform to make a comprehensive judgment. The onboard OBU6 is installed at the bottom of the autonomous vehicle. It receives command information transmitted by the roadside RSU via LTEV communication, and uploads the vehicle status information in the XYZ directions, acceleration and angular velocity values ​​fed back by the vehicle's IMU, and transmits it to the cloud platform. The intelligent driving controller 7 is located at the bottom of the cockpit of the autonomous vehicle. It receives instructions from the cloud platform transmitted by the on-board OBU, and performs IMU calibration and location fusion calculation using the positioning information of the edge computing unit and GPS positioning information according to the instructions from the cloud platform. Alternatively, it controls the autonomous vehicle to drive into the maintenance station for repair according to the instructions. The vehicle-mounted IMU8 is installed at the bottom of the autonomous vehicle to collect the acceleration and angular velocity values ​​of the autonomous vehicle in the XYZ directions in real time and transmit them to the OBU; The maintenance service platform 9 is connected to the cloud platform via wireless network. It receives the vehicle IMU calibration fault information reported by the cloud platform and will notify the service station in advance to prepare for vehicle maintenance. The vehicle driving system 10 is located at the bottom of the autonomous vehicle 12, receives instructions from the autonomous driving controller, and controls the vehicle's acceleration, deceleration, steering, and other motion. GPS system 11 is installed on the top of the autonomous vehicle to receive GPS signals and transmit them to the intelligent driving vehicle controller for the autonomous driving controller to fuse vehicle positioning information. A vehicle-to-everything (V2X) IMU calibration and detection method includes: S1. Truth Value Construction: The edge computing unit integrates data from the roadside perception unit to calculate the actual acceleration and angular velocity of the autonomous vehicle in the XYZ directions, and sends them to the cloud platform as the truth values ​​of the vehicle's actual motion parameters. S2. Deviation detection: The cloud platform receives the acceleration and angular velocity values ​​in the XYZ directions uploaded by the vehicle-mounted IMU and compares them with the true values ​​of the actual vehicle motion parameters sent by the edge computing unit. S3. Command Issuance and Execution: Based on the comparison difference, it is determined whether the IMU calibration parameters meet the requirements. If they do not meet the requirements, the cloud platform issues Command No. 1. Specifically, when the acceleration value deviation is >0.5 or the angular velocity value deviation is >0.3, it is determined that the IMU calibration parameters do not meet the requirements, and the cloud platform issues Command No. 1.

[0026] In response to Command 1, the intelligent driving controller suspends the use of onboard IMU data, switches to receiving the true values ​​of the actual vehicle motion parameters transmitted by the edge computing unit for positioning fusion, and simultaneously starts the onboard IMU for driving dynamic self-calibration. S4. Re-inspection and closed-loop: After calibration, the cloud platform compares the data again; if the acceleration value deviation is ≤0.5 and the angular velocity value deviation is ≤0.3, a second command is issued to restore the vehicle-mounted IMU to use. If the deviation still does not meet the requirements, issue Command No. 3 to control the vehicle to continue receiving data from the edge computing unit and restart the self-calibration; If the data still does not meet the requirements after three self-calibrations, the cloud platform reports to the service backend, which then controls the vehicle to be driven to the service station for repair.

[0027] Logical judgment of the vehicle-to-everything (V2X) IMU calibration and testing system: The cloud platform receives the acceleration and angular velocity values ​​in the XYZ directions transmitted by the vehicle-mounted IMU and compares them with the values ​​of the edge computing unit. The cloud platform will then issue instructions based on the comparison values. 1.1 The acceleration and angular velocity values ​​in the XYZ directions received from the vehicle-mounted IMU are compared with the values ​​from the edge computing unit. If the acceleration value deviation is ≤0.5 and the angular velocity value deviation is ≤0.3, it is determined that the IMU calibration parameters meet the requirements, and no command will be issued. 1.2 The acceleration and angular velocity values ​​in the XYZ directions received from the vehicle-mounted IMU are compared with the values ​​from the edge computing unit. If the acceleration value deviation is >0.5 or the angular velocity value deviation is >0.3, it is determined that the IMU calibration parameters do not meet the requirements. Then, the first command will be issued, the intelligent driving vehicle controller will suspend the use of the positioning information input by the IMU, receive the acceleration and angular velocity in the XYZ directions of the autonomous vehicle transmitted by the edge computing unit, and simultaneously start the IMU to perform driving dynamic self-calibration. 1.3 The acceleration and angular velocity values ​​in the XYZ directions of the recalibrated IMU are compared with the values ​​of the edge computing unit. If the acceleration value deviation is ≤0.5 and the angular velocity value deviation is ≤0.3, it is determined that the IMU calibration parameters meet the requirements. Then, the second command will be issued. The intelligent driving vehicle controller will stop using the positioning information input by the edge computing unit, receive the acceleration and angular velocity in the XYZ directions transmitted by the vehicle IMU, and perform positioning fusion. 1.4 Upon receiving the recalibrated IMU's acceleration and angular velocity values ​​in the XYZ directions, the system compares them with the values ​​from the edge computing unit. If the acceleration deviation is greater than 0.5 or the angular velocity deviation is greater than 0.3, the IMU calibration parameters are deemed not to meet the requirements. In this case, command number three is issued. The intelligent driving vehicle controller will continue to receive the acceleration and angular velocity values ​​in the XYZ directions from the edge computing unit and simultaneously restart the IMU for dynamic self-calibration. If this is repeated three times and the information input by the IMU still does not meet the requirements, the system will report the IMU calibration fault to the service backend system and control the vehicle to drive to the service station for repair.

[0028] The above description is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An Internet of Vehicles based IMU calibration detection system, characterized in that, include: The roadside sensing unit is used to monitor the motion attitude of autonomous vehicles in real time and collect the actual acceleration and angular velocity of the vehicle in the XYZ directions. The edge computing unit is connected to the roadside sensing unit. It uses the acceleration and angular velocity in the XYZ directions to construct a vehicle motion model and calculate the true values ​​of the actual motion parameters of the vehicle. The vehicle-mounted unit includes an on-board IMU and an on-board OBU. The on-board IMU is used to collect the vehicle's own acceleration and angular velocity data, and the on-board OBU is used to upload the acceleration and angular velocity data collected by the on-board IMU. The cloud platform is connected to the edge computing unit and the vehicle-mounted OBU respectively, and is used to receive the true value of the actual motion parameters of the vehicle and the data collected by the vehicle-mounted IMU, compare the deviation between the two and generate control commands. The intelligent driving controller is installed on the autonomous vehicle to receive control commands from the cloud platform and perform location source switching, IMU self-calibration, or maintenance operations. 2.The vehicle networking based IMU calibration detection system of claim 1, wherein, The roadside sensing unit includes cameras and lidar mounted on roadside poles. 3.The V2X-based IMU calibration detection system of claim 2, wherein, The edge computing unit first receives 2D information from the camera and 3D information from the lidar, fuses the 2D and 3D information, then constructs a vehicle motion model and calculates the true values ​​of the vehicle's actual motion parameters. 4.The vehicle networking based IMU calibration detection system of claim 1, wherein, The process of comparing the deviations between the two and generating control commands includes: The system determines whether the feedback value from the vehicle-mounted IMU meets the requirements based on the deviation between the two. If the IMU calibration parameters are determined to be non-compliant, the cloud platform will issue an instruction to the intelligent driving vehicle controller to suspend the use of the positioning information input by the IMU, receive the acceleration and angular velocity in the XYZ directions transmitted by the edge computing unit, and simultaneously start the IMU to perform dynamic self-calibration for driving. 5.The V2X-based IMU calibration detection system of claim 4, wherein, If the feedback value input by the IMU still does not meet the requirements after three attempts, the system will report the issue to the service backend system, the IMU will calibrate the fault, and the vehicle will be directed to the service station for repair. 6.The V2X-based IMU calibration and detection system of claim 4, wherein, The deviation includes acceleration value deviation and angular velocity value deviation, and the comparison of the two deviations and generation of control commands includes: When the acceleration value deviation is ≤0.5 and the angular velocity value deviation is ≤0.3, it is determined that the IMU calibration parameters meet the requirements, and no command will be issued. When the acceleration value deviation is greater than 0.5 or the angular velocity value deviation is greater than 0.3, it is determined that the IMU calibration parameters do not meet the requirements. Instruction No. 1 will be issued, and the intelligent driving controller will suspend the use of the positioning information input by the IMU, receive the acceleration and angular velocity of the autonomous vehicle in the XYZ directions transmitted by the edge computing unit, and simultaneously start the IMU to perform driving dynamic self-calibration.

7. The IMU calibration and testing system based on vehicle networking according to claim 1, characterized in that, It also includes a GPS system, which is installed on the top of the vehicle to receive GPS signals and transmit them to the intelligent driving vehicle controller for the intelligent driving controller to fuse vehicle positioning information.

8. A vehicle networking-based IMU calibration detection method, characterized in that, include: S1. Truth Value Construction: The edge computing unit integrates data from the roadside perception unit to calculate the actual acceleration and angular velocity of the autonomous vehicle in the XYZ directions, and sends them to the cloud platform as the truth values ​​of the vehicle's actual motion parameters. S2. Deviation detection: The cloud platform receives the acceleration and angular velocity values ​​in the XYZ directions uploaded by the vehicle-mounted IMU and compares them with the true values ​​of the actual vehicle motion parameters sent by the edge computing unit. S3. Command Issuance and Execution: Based on the comparison difference, it is determined whether the IMU calibration parameters meet the requirements. If they do not meet the requirements, the cloud platform issues Command No.

1. In response to Command 1, the intelligent driving controller suspends the use of onboard IMU data, switches to receiving the true values ​​of the actual vehicle motion parameters transmitted by the edge computing unit for positioning fusion, and simultaneously starts the onboard IMU for driving dynamic self-calibration. S4. Re-inspection and closed-loop: After calibration, the cloud platform compares the data again; if the acceleration value deviation is ≤0.5 and the angular velocity value deviation is ≤0.3, a second command is issued to restore the vehicle-mounted IMU to use. If the deviation still does not meet the requirements, issue Command No. 3 to control the vehicle to continue receiving data from the edge computing unit and restart the self-calibration; If the data still does not meet the requirements after three self-calibrations, the cloud platform reports to the service backend, which then controls the vehicle to be driven to the service station for repair. 9.The V2X-based IMU calibration detection method of claim 8, wherein, In S3, when the acceleration value deviation is greater than 0.5 or the angular velocity value deviation is greater than 0.3, it is determined that the IMU calibration parameters do not meet the requirements, and the cloud platform issues Command No.

1. 10.The V2X-based IMU calibration detection method according to claim 8, characterized in that, The edge computing unit integrates data from the roadside perception unit by receiving 2D information from cameras and 3D information from lidar, and then fusing the 2D and 3D information.

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