4D millimeter wave radar sensor simulation method for automatic driving simulation test

Abstract

The invention relates to the technical field of sensors, in particular to a 4D millimeter wave radar sensor simulation method for an automatic driving simulation test, and the method comprises the following steps: 1, constructing a 4D radar sensor model, and loading the 4D radar sensor model in a simulation platform; step 2, completing radar parameter information *. Json file configuration; step 3, setting an IP address and a port for receiving the target object list; 4, simulation is started after setting is completed, and in the simulation running period, the 4D radar sensor model extracts needed information from detected target object information; step 5, all kinds of required information marked in the step 4 are integrated and stored in a target object list at each time step, and then are sent in a JSON (JavaScript Object Notation) format through a UDP (User Datagram Protocol); according to the method, a 4D millimeter wave radar model can be constructed and is seamlessly compatible with various existing virtual simulation platforms, and a detailed target object information list is provided for a user in a JSON format through a UDP (User Datagram Protocol).

Description

Technical Field

[0001] The present invention relates to the field of sensor technology, and in particular to a 4D millimeter wave radar sensor simulation method for autonomous driving simulation testing. Background Art

[0002] At present, autonomous vehicles are equipped with various types of sensor equipment, including but not limited to cameras, lidar, millimeter-wave radar, etc. At present, OEMs and Tier 1 suppliers are focusing on 4D millimeter-wave radar, hereinafter referred to as "4D radar". In the long run, 4D radar may gradually replace lidar, or at least help reduce the installation ratio of lidar in vehicle sensors.

[0003] Autonomous driving and advanced driver assistance systems, hereinafter referred to as "AD / ADAS", mainly use millimeter-wave radars to provide information about the distance, direction and speed of targets. However, the emergence of 4D radars has brought new technical ideas to AD / ADAS, because it can provide information about the height of targets in addition to the original three dimensions of distance, direction and speed. In addition, 4D radars also have the capabilities of real-time target detection, mapping and tracking of moving targets.

[0004] For most Tier 1 suppliers, LiDAR does have the ability and advantage of providing more accurate environmental information and mapping compared to ordinary radar, but it also faces the disadvantages of high cost and susceptibility to rain and fog, so the choice of 4D radar becomes the best alternative to solve the above problems. However, looking at the various simulation platforms on the market, no practical 4D millimeter wave radar model has been launched. Summary of the invention

[0005] The purpose of the present invention is to provide a 4D millimeter-wave radar sensor simulation method for autonomous driving simulation testing to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a 4D millimeter wave radar sensor simulation method for autonomous driving simulation test, the method comprising the following steps: Step 1: First, construct a 4D radar sensor model, and then load the executable program of the 4D radar sensor model into the simulation platform; Step 2: Complete the configuration of the radar parameter information *.json file, which includes but is not limited to horizontal and vertical field of view angles and beam coverage; Step 3, set the IP address and port for receiving the target list, or use the default IP address and port; Step 4: Start the simulation after completing the settings. During the simulation, the 4D radar sensor model will extract the required information from the detected target information, including but not limited to the relative and absolute position, velocity, acceleration, and size of the target. Step 5. During the simulation, all the required information marked in step 4 above will be integrated and saved in the target list at each time step, and then sent to the IP address and port set in step 3 in JSON format via the UDP protocol. Finally, wait for the user to stop the simulation or stop the simulation on time.

[0007] Preferably, in step 1, the execution file of the 4D radar sensor model is installed in a corresponding folder according to the simulation environment used.

[0008] Preferably, in step 2, the parameter configuration file is in JSON format and is named "4Dradar_input.json", and the parameter information filled in includes the installation position of the radar on the test vehicle, the orientation angle of the radar on the test vehicle, the maximum and minimum horizontal field of view angles, the maximum and minimum vertical field of view angles, the beam coverage range, the type of detected target, and the module frequency.

[0009] Preferably, the orientation angle of the radar to the test vehicle includes three angles: pitch, yaw, and roll. The detection target type is based on the optional target type of the virtual simulation environment. After the parameter information is filled in and the JSON file is saved in the corresponding folder, it is confirmed that when the platform simulation is running, the 4D radar sensor model can correctly call the relevant parameters.

[0010] Preferably, the IP address and port are set after all 4D radar parameters are set and the corresponding *.json file and 4D radar sensor model execution file are added to the correct path.

[0011] Preferably, after starting the simulation, the JSON format file containing radar configuration information, the set IP address and port are read once by the 4D radar sensor model, and at each time step of the simulation process, the working method of the 4D radar sensor model includes the following steps: S1: Read the test vehicle information, including but not limited to the vehicle position, direction, and speed; S2: Read all target information, including but not limited to absolute position and type; S3: Read other information related to the size of the target object; S4: The 4D radar model will determine whether the target object is within the radar detection range set by the user and whether the target type belongs to the detectable target type defined by the user based on the information provided by S1, S2, and S3 above; S5: If the two conditions of S4 are met, the 4D radar simulation model starts to extract and calculate relevant information; S6: After completing the data collection of each target object, the data will be classified and reorganized according to the JSON format; S7: All target object related data are sequentially superimposed and accumulated and grouped into a JSON message; S8: As in step S7, the summarized JSON message will be sent via UDP to the IP address and port defined in step 3.

[0012] Preferably, the information extracted and calculated in step S5 includes data acquisition time, target object ID, target object type, target object position in the user radar reference coordinate system, target object position in the test vehicle reference coordinate system, target object absolute position, target object size, target object position in the user radar reference coordinate system, target object position in the test vehicle reference coordinate system, target object relative speed in the user radar reference coordinate system, target object relative speed in the test vehicle reference coordinate system, target object absolute speed, target object relative acceleration in the user radar reference coordinate system, target object relative acceleration in the test vehicle reference coordinate system, and target object absolute acceleration.

[0013] Preferably, the 4D radar sensor model includes a radar parameter configuration module, a simulation interface module, a target monitoring module, a data extraction and calculation module, a data formatting module, a user interface and configuration module, a simulation control module, a depth interaction module, and a code adaptation module; The radar parameter configuration module is used to configure and manage the parameter information of the 4D radar to ensure that the model can correctly call these parameters during simulation operation; The simulation interface module is used to ensure the compatibility between the 4D radar and different simulation platforms, and to achieve seamless connection with the simulation environment; The target detection module is used to read the information of the test vehicle and the target from the simulation environment, determine whether the target is within the detectable range through the detection algorithm, and identify the target type; The data extraction and calculation module is used to extract various information of the target object and perform necessary calculations; The data formatting module is used to structure the extracted data and output it in JSON format; The user interface and configuration module is used to provide users with the function of setting IP addresses and ports, simplify the operation process, and provide an interactive interface for starting and stopping simulation; The simulation control module is used to control the start and stop of the simulation and monitor the operation status and data capture of the radar module during the simulation process; The deep interaction module is used to achieve deep interaction with the simulation environment and ensure the accuracy of detection and calculation by reading information in real time; The code adaptation module is used to support the adjustment of source code according to the requirements of a specific simulation environment.

[0014] Preferably, the simulation interface module can provide specific API interfaces for different virtual simulation platforms, and the simulation interface module has an error capture and processing mechanism.

[0015] Preferably, the simulation control module is provided with a start-stop control module to realize the start, pause and termination operations of the simulation, and the simulation control module is provided with a log recording module that can save key information including the start time, end time and target object data during the simulation process.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention can construct an advanced 4D millimeter-wave radar model, which is seamlessly compatible with various virtual simulation platforms currently available on the market, and can also provide users with a detailed list of target information in JSON format via UDP.

[0017] 2. The present invention can perform a variety of calculations and provide users with information including but not limited to the location, speed, size, etc. of the detected target. The 4D radar model can be fine-tuned for different simulation platforms to ensure its compatibility and functionality. Seamless integration can be achieved by simply adjusting the required input information of different simulation platforms accordingly. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of the simulation method of the present invention.

[0019] Figure 2 This is an example diagram of the 4D radar configuration of the present invention.

[0020] Figure 3 It is a schematic diagram of the target object recording information output structure of the present invention.

[0021] Figure 4 Schematic diagram of the module of the 4D radar sensor model of the present invention.

[0022] Figure 5 The following is a schematic diagram of the working process of the radar parameter configuration module of the present invention.

[0023] Figure 6 Schematic diagram of the workflow of the target monitoring module of the present invention.

[0024] Figure 7 It is a functional schematic diagram of the data extraction and calculation module of the present invention.

[0025] Figure 8 Schematic diagram of the working process of the data formatting module of the present invention.

[0026] Fig. 9 It is a schematic diagram of the working process of the simulation control module of the present invention.

[0027] Fig.10 Schematic diagram of the workflow of the deep interaction module of the present invention. DETAILED DESCRIPTION

[0028] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods. It should be noted that, under the premise of no conflict, the various embodiments or technical features described below can be arbitrarily combined to form a new embodiment. It should be noted that the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0029] See also Figures 1 to 10 The present invention provides a technical solution: first, a 4D radar model that can adapt to multiple simulation platforms is constructed, and a target object information list can be presented to the user in JSON format through the UDP protocol.

[0030] The core advantage of the present invention is that it can perform a variety of calculations to provide users with information including but not limited to the position, speed, size, etc. of the detected target.

[0031] The 4D radar model can be fine-tuned for different simulation platforms to ensure its compatibility and functionality.

[0032] The working principle of the present invention is as follows: 1. Load the executable program of 4D radar in the simulation platform.

[0033] 2. Complete the configuration of the radar parameter information *.json file, which includes but is not limited to horizontal and vertical field of view angles, beam coverage, etc.

[0034] 3. Set the IP address and port for receiving the target list. If the user does not specify, the system will use the default IP address 127.0.0.1 and port 8888.

[0035] 4. During the simulation, the 4D radar module will extract information including but not limited to the relative and absolute position, velocity, acceleration, target size, etc. of the detected target from the target information.

[0036] 5. During the simulation, the required information marked in step 4 will be integrated and saved in the target list at each time step, and then sent to the IP address and port set in step 3 in JSON format via UDP protocol.

[0037] Steps to use the 4D radar sensor model: 1. Install the executable file of the 4D radar sensor model in the corresponding folder according to the simulation environment used.

[0038] 2. Fill in the 4D radar related parameters to complete the configuration. The parameter configuration file is in JSON format and named "4Dradar_input.json". The following information must be filled in, such as Figure 1 As shown: 2.1: Installation position of radar on the test vehicle (x, y, z) (unit: meter) 2.2: Radar orientation angle (pitch, yaw, roll) of the test vehicle (unit: degree) 2.3: Maximum and minimum horizontal field of view (unit: degree) 2.4: Maximum and minimum vertical field of view (unit: degree) 2.5: Beam coverage (unit: meter) 2.6: Detection target type (optional target type based on virtual simulation environment) 2.7: Module frequency (unit: Hz) 3. After completing the parameter information in step 2 and saving the JSON file in the corresponding folder, you need to confirm that the 4D radar sensor model can correctly call the relevant parameters when the platform simulation is running.

[0039] 4. After all 4D radar parameters are set and the corresponding *.json file and 4D radar sensor model execution file are added to the correct path, the user can set the default IP address (127.0.0.1) and port (8888), or customize the IP address and port based on the simulation platform. The location of the customized IP address and port module may be different in different simulation environments.

[0040] 5. After completing the above settings, you can start the simulation. After starting, the 4D radar reads the following information at one time: 5.1: A JSON file containing radar configuration information.

[0041] 5.2: User-defined (if any) IP address and port or use the default IP address and port.

[0042] 6. During the simulation, at each time step, the 4D radar sensor model will: 6.1: Read the test vehicle information, including but not limited to the vehicle position, direction, speed, etc.

[0043] 6.2: Read all target information, including but not limited to absolute position, type, etc.

[0044] 6.3: Read other information related to the target size.

[0045] 6.4: The 4D radar model will make the following judgments based on the information provided in 6.1, 6.2, and 6.3 above: 6.4.1: Is the target within the radar detection range set by the user? 6.4.2: Whether the target type belongs to the user-defined detectable target type 6.5: If the conditions in 6.4.1 and 6.4.2 are met, the 4D radar simulation model will begin to extract and / or calculate the following information: 6.5.1: Data acquisition time 6.5.2: Target ID 6.5.3: Target type 6.5.4: Target position in the user radar reference coordinate system 6.5.5: Test the position of the target object in the vehicle reference coordinate system 6.5.6: Absolute position of the target 6.5.7: Target size (length, width, height) 6.5.8: Target position in the user radar reference coordinate system (distance Dist, horizontal angle Alpha, vertical angle Beta) 6.5.9: Test the target position in the vehicle reference coordinate system (distance Dist, horizontal angle Alpha, vertical angle Beta) 6.5.10: Relative velocity of target in user radar reference coordinate system 6.5.11: Test the relative speed of the target object in the vehicle reference coordinate system 6.5.12: Absolute speed of target 6.5.13: Relative acceleration of the target in the user radar reference coordinate system 6.5.14: Relative acceleration of the target object in the test vehicle reference coordinate system 6.5.15: Absolute acceleration of the target 6.6: After completing the data collection of each target object, the data will be in JSON format (such as Figure 2 Classification and reorganization are performed.

[0046] 6.7: All target object related data will be accumulated and grouped in a JSON message.

[0047] 6.8: As in step 6.7, the summarized JSON message will be sent via UDP to the IP address and port defined in step 4.

[0048] 7. The user can stop the simulation at any time, or stop it according to the simulation end time set for the test case.

[0049] If you need to obtain certain missing information that the 4D radar sensor model cannot directly read and calculate, you need to adjust the source code to adapt to the specific simulation environment, so that you can read basic information such as test vehicle information and target object information through the API of the simulation platform used.

[0050] The 4D radar sensor model of the present invention can support the completion of the above-mentioned simulation method. The model includes a radar parameter configuration module, a simulation interface module, a target monitoring module, a data extraction and calculation module, a data formatting module, a user interface and configuration module, a simulation control module, a depth interaction module, and a code adaptation module.

[0051] The specific contents of each module are as follows: 1. Radar parameter configuration module This module is responsible for acquiring and storing various configuration parameters of the 4D radar sensor to ensure that the radar settings meet actual application requirements.

[0052] User input: Allows users to set the radar's installation position (x, y, z coordinates), orientation angle (pitch, yaw, roll), field of view (horizontal and vertical), beam coverage, detection target type, module frequency, etc. by filling in a JSON-formatted configuration file.

[0053] Verification mechanism: After the parameters are entered, the module can verify the input data to ensure legality and rationality (for example, the field of view angle should be within a specific range).

[0054] Parameter Update: Supports users to dynamically change parameters during simulation and apply new settings in real time for more flexible operation.

[0055] 2. Simulation interface module This module ensures the interaction between the 4D radar model and different simulation platforms and provides the infrastructure for data transmission.

[0056] Platform compatibility: Specific API interfaces are provided for different virtual simulation platforms to ensure that the radar model can be smoothly integrated and support each other with the platform functions.

[0057] UDP protocol implementation: Implement the sending and receiving mechanism of the UDP protocol to efficiently transmit radar detection data to ensure timeliness and reliability.

[0058] Error handling: It has an error capture and handling mechanism, which can provide corresponding feedback for users to actively handle when data transmission or interface call fails.

[0059] 3. Object Detection Module This module extracts vehicle and target information from the simulation environment and determines detectable targets through judgment algorithms.

[0060] Information extraction: Read the real-time status information of the test vehicle, including position, speed, direction, etc., and obtain information about all possible detection targets, such as size, type and motion status.

[0061] Detection logic: Implement complex detection logic, such as using a threshold algorithm to determine whether a target is within the set detection range, and filtering target types to ensure that only targets of interest to the user are extracted.

[0062] Status update: Update the status information of the target object in real time during the simulation to facilitate subsequent data extraction and calculation.

[0063] 4. Data extraction and calculation module Responsible for extracting key information from the information obtained by the target detection module and performing calculations.

[0064] Information processing flow: Calculations are performed on demand based on the target's absolute position, speed, acceleration, size and other information.

[0065] Relative calculation: Calculate the position, velocity, and acceleration of the target relative to the user radar and test vehicle coordinate system to generate real-time monitoring data.

[0066] Batch processing: supports batch extraction and calculation of information of multiple targets to improve processing efficiency.

[0067] 5. Data formatting module Format and organize the extracted and calculated data for subsequent use.

[0068] JSON structure design: Design a reasonable JSON structure to clearly display data, including timestamp, target ID, type, position, speed, acceleration and other fields.

[0069] Classification and grouping: At the end of each simulation time step, the information of different targets is classified and grouped into a unified output format.

[0070] Output Verification: Ensures that the collated data is verified before sending to avoid errors in data transmission.

[0071] 6. User interface and configuration module Provides a user setting interface to support parameter configuration and simulation control.

[0072] User-friendly interface: Design an intuitive user interface including input boxes, selection boxes, and buttons so that users can easily set the IP address, port, and other radar parameters.

[0073] The default configuration provides default IP address and port configuration options to simplify user operations while ensuring that users can customize settings at any time.

[0074] Feedback mechanism: Provides instant feedback when the user makes an operation, informs the success or failure of the configuration, and gives suggestions to optimize the configuration.

[0075] 7. Simulation control module Responsible for controlling the start and stop of the simulation and monitoring the simulation execution status.

[0076] Control logic: implements the start, pause and end operations of the simulation, and provides users with the option of selecting the end time of the simulation.

[0077] Status monitoring: monitor the working status of the 4D radar, record abnormal conditions during simulation operation, and provide corresponding alarm mechanisms.

[0078] Log Recording: Save key information during the simulation, including start time, end time, and target data during runtime, to facilitate subsequent analysis and evaluation.

[0079] 8. Deep Interaction Module Enable deep interaction with the simulation environment to enhance detection accuracy.

[0080] Information synchronization: Real-time data synchronization with vehicles and targets in the simulation environment to ensure that all information is up to date.

[0081] Self-check of detection standards: Self-check the detection results of the target object according to the set detection standards to ensure the accuracy and reliability of the data.

[0082] Feedback mechanism: allows the radar system to dynamically adjust its working state based on the detection results to improve detection performance and efficiency.

[0083] 9. Code Adaptation Module Supports adaptation of source code to specific simulation environments.

[0084] Customized development: According to user requirements or the characteristics of the specific simulation environment, necessary modifications are made to the source code to achieve specific functions or cooperate with specific access methods.

[0085] Document support: Provide detailed documentation guidance to help developers quickly understand the code structure and modification methods, and reduce confusion that may arise during the adaptation process.

[0086] Version management: Ensure module version management to facilitate tracking of code changes and maintain compatibility of multiple versions to support different simulation requirements.

[0087] The 4D radar sensor model of the present invention is specially designed for the field of autonomous driving and sensor simulation, and has the ability to work in collaboration with various simulation platforms.

[0088] Compatibility across simulation platforms: The 4D radar model has the ability to be seamlessly compatible with a variety of virtual simulation platforms, highlighting its outstanding performance in versatility and practicality.

[0089] UDP data transmission and JSON output format. This model adopts the UDP (User Datagram Protocol) transmission mechanism and outputs the detection target list in JSON format, which reflects the innovative characteristics of the present invention in data communication.

[0090] Accurately detect the vertical height of the target. Different from traditional millimeter-wave radar technology, this 4D radar can help obtain the real height data of the measured target. This function marks another breakthrough in sensor technology in the accuracy of detecting targets.

[0091] With the capability of simultaneous detection, mapping and tracking, this 4D radar can realize simultaneous detection, mapping and tracking of moving targets, providing key technical support for autonomous driving / advanced driver assistance systems.

[0092] Configuration flexibility. The present invention supports users to modify some parameters to adapt to different types of 4D radars. The modifiable parameter types include horizontal and vertical field of view angles, beam coverage, detection target type and module frequency.

[0093] Data extraction and calculation, this model supports the extraction and calculation of various information of the detected target, including key data such as the target's position, speed, size and acceleration.

[0094] Structured Output Design,The present invention defines a structured output design, which records target information at each simulation,time step in JSON format, providing orderly and efficient data support for,the simulation process and subsequent analysis.

[0095] IP address and port settings. This model supports user-defined IP addresses and ports for network data transmission, and provides default configurations to simplify user operations.

[0096] Deep interaction with the simulation environment,This model achieves deep interaction with the simulation environment by reading vehicle and target information,,self-checking detection standards, and calculating relevant data of each,detected target.

[0097] Customized adaptation to specific simulation environments,To ensure compatibility and functionality in a specific simulation environment, the source code of this model supports partial modification to fully adapt to the simulation environment.

[0098] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A 4D millimeter wave radar sensor simulation method for autonomous driving simulation testing, characterized in that: The method comprises the following steps: Step 1: First, construct a 4D radar sensor model, and then load the executable program of the 4D radar sensor model into the simulation platform; Step 2: Complete the configuration of the radar parameter information *.json file, which includes but is not limited to horizontal and vertical field of view angles and beam coverage; Step 3, set the IP address and port for receiving the target list, or use the default IP address and port; Step 4: Start the simulation after completing the settings. During the simulation, the 4D radar sensor model will extract the required information from the detected target information, including but not limited to the relative and absolute position, velocity, acceleration, and size of the target. Step 5. During the simulation, all the required information marked in step 4 above will be integrated and saved in the target list at each time step, and then sent to the IP address and port set in step 3 in JSON format via the UDP protocol. Finally, wait for the user to stop the simulation or stop the simulation on time.

2. The 4D millimeter wave radar sensor simulation method for autonomous driving simulation test according to claim 1, characterized in that: In the step 1, the execution file of the 4D radar sensor model is installed in a corresponding folder according to the simulation environment used.

3. The 4D millimeter wave radar sensor simulation method for autonomous driving simulation test according to claim 1, characterized in that: In step 2, the parameter configuration file is in JSON format and named "4Dradar_input.json". The parameter information filled in includes the installation position of the radar on the test vehicle, the orientation angle of the radar on the test vehicle, the maximum and minimum horizontal field of view angles, the maximum and minimum vertical field of view angles, the beam coverage range, the type of detection target, and the module frequency.

4. The 4D millimeter wave radar sensor simulation method for autonomous driving simulation test according to claim 3 is characterized in that: The orientation angles of the radar to the test vehicle include pitch, yaw and roll. The detection target type is based on the optional target type of the virtual simulation environment. After the parameter information is filled in and the JSON file is saved in the corresponding folder, it is confirmed that the 4D radar sensor model can correctly call the relevant parameters when the platform simulation is running.

5. The 4D millimeter wave radar sensor simulation method for autonomous driving simulation test according to claim 1, characterized in that: The IP address and port are set after all 4D radar parameters are set and the corresponding *.json file and 4D radar sensor model execution file are added to the correct path.

6. The 4D millimeter wave radar sensor simulation method for autonomous driving simulation test according to claim 1, characterized in that: After starting the simulation, the JSON format file containing radar configuration information, the set IP address and port are read at one time through the 4D radar sensor model. At each time step of the simulation process, the working method of the 4D radar sensor model includes the following steps: S1: Read the test vehicle information, including but not limited to the vehicle position, direction, and speed; S2: Read all target information, including but not limited to absolute position and type; S3: Read other information related to the size of the target object; S4: The 4D radar model will determine whether the target object is within the radar detection range set by the user and whether the target type belongs to the detectable target type defined by the user based on the information provided by S1, S2, and S3 above; S5: If the two conditions of S4 are met, the 4D radar simulation model starts to extract and calculate relevant information; S6: After completing the data collection of each target object, the data will be classified and reorganized according to the JSON format; S7: All target object related data are sequentially superimposed and accumulated and grouped into a JSON message; S8: As in step S7, the summarized JSON message will be sent via UDP to the IP address and port defined in step 3.

7. The 4D millimeter wave radar sensor simulation method for autonomous driving simulation test according to claim 6, characterized in that: The information extracted and calculated in step S5 includes data acquisition time, target object ID, target object type, target object position in the user radar reference coordinate system, target object position in the test vehicle reference coordinate system, target object absolute position, target object size, target object position in the user radar reference coordinate system, target object position in the test vehicle reference coordinate system, target object relative speed in the user radar reference coordinate system, target object relative speed in the test vehicle reference coordinate system, target object absolute speed, target object relative acceleration in the user radar reference coordinate system, target object relative acceleration in the test vehicle reference coordinate system, and target object absolute acceleration.

8. The 4D millimeter wave radar sensor simulation method for autonomous driving simulation test according to claim 1, characterized in that: The 4D radar sensor model includes a radar parameter configuration module, a simulation interface module, a target monitoring module, a data extraction and calculation module, a data formatting module, a user interface and configuration module, a simulation control module, a depth interaction module, and a code adaptation module; The radar parameter configuration module is used to configure and manage the parameter information of the 4D radar to ensure that the model can correctly call these parameters during simulation operation; The simulation interface module is used to ensure the compatibility between the 4D radar and different simulation platforms, and to achieve seamless connection with the simulation environment; The target detection module is used to read the information of the test vehicle and the target from the simulation environment, determine whether the target is within the detectable range through the detection algorithm, and identify the target type; The data extraction and calculation module is used to extract various information of the target object and perform necessary calculations; The data formatting module is used to structure the extracted data and output it in JSON format; The user interface and configuration module is used to provide users with the function of setting IP addresses and ports, simplify the operation process, and provide an interactive interface for starting and stopping simulation; The simulation control module is used to control the start and stop of the simulation and monitor the operation status and data capture of the radar module during the simulation process; The deep interaction module is used to achieve deep interaction with the simulation environment and ensure the accuracy of detection and calculation by reading information in real time; The code adaptation module is used to support the adjustment of source code according to the requirements of a specific simulation environment.

9. The 4D millimeter wave radar sensor simulation method for autonomous driving simulation test according to claim 8, characterized in that: The simulation interface module can provide specific API interfaces for different virtual simulation platforms, and the simulation interface module has an error capture and processing mechanism.

10. The 4D millimeter wave radar sensor simulation method for autonomous driving simulation test according to claim 8, characterized in that: The simulation control module is provided with a start-stop control module to realize the start, pause and termination operations of the simulation. The simulation control module is provided with a log recording module to save key information including the start time, end time and target object data during the simulation process.

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