A 4D millimeter-wave radar sensor simulation method for autonomous driving simulation testing

Abstract

The present invention relates to the technical field of sensors, and specifically to a simulation method for a 4D millimeter-wave radar sensor for autonomous driving simulation testing. The method includes the following steps: Step 1, first construct a 4D radar sensor model and load the 4D radar sensor model in the simulation platform; Step 2, complete the configuration of the radar parameter information *.json file; Step 3, set the IP address and port for receiving the target list; Step 4, start the simulation after the settings are completed. During the simulation run, the 4D radar sensor model will extract the required information from the detected target information; Step 5, various types of required information marked in the above Step 4 will be integrated and saved to the target list at each time step, and then sent in JSON format through the UDP protocol. The present invention can construct a 4D millimeter-wave radar model that is seamlessly compatible with various existing virtual simulation platforms and provide users with a detailed target information list in JSON format through UDP.

Description

Technical Field

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

[0002] Currently, autonomous driving vehicles are all equipped with various types of sensor devices, including but not limited to cameras, lidars, millimeter-wave radars, etc. At present, both original equipment manufacturers and first-tier suppliers are focusing on 4D millimeter-wave radars, hereinafter referred to as "4D radars". In the long run, 4D radars may gradually replace lidars, or at least help reduce the assembly ratio of lidars in vehicle sensors.

[0003] Autonomous driving and advanced driver assistance systems, hereinafter referred to as "AD / ADAS", related radar technologies are mainly based on millimeter-wave radars, which can provide the distance, azimuth, and speed of target objects. However, the emergence of 4D radars brings new technical ideas to AD / ADAS, because they can provide the height information of target objects on the basis of the original three dimensions of distance, azimuth, and speed. In addition, 4D radars also have the capabilities of real-time target detection, map drawing, and moving target tracking.

[0004] For most first-tier suppliers, compared with ordinary radars, lidars do have the capabilities and advantages of providing higher-precision environmental information and map drawing, but at the same time, they also need to face the high costs brought by them and the disadvantages of being vulnerable to rain and fog weather. Therefore, choosing to use 4D radars becomes the best alternative solution to solve the above problems. However, currently, looking at all the simulation platforms on the market, no practical 4D millimeter-wave radar models have been launched. Summary of the Invention

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

[0006] To achieve the above purpose, the present invention provides the following technical solution: A simulation method for a 4D millimeter-wave radar sensor in autonomous driving simulation testing, the method includes the following steps:

[0007] Step 1, first construct a 4D radar sensor model, and then load the executable program of the 4D radar sensor model in the simulation platform;

[0008] Step 2, complete the configuration of the radar parameter information *.json file, and the parameters include but are not limited to horizontal and vertical field of view angles, beam coverage range;

[0009] Step 3, set the IP address and port for receiving the target object list, or use the default IP address and port;

[0010] Step 4: After the settings are completed, start the simulation. During the simulation run, the 4D radar sensor model will extract the required information from the detected target information, which includes but is not limited to the relative and absolute positions, speed, acceleration, and target size of the target.

[0011] Step 5: During the simulation process, various types of required information marked in Step 4 above will be integrated and saved to 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.

[0012] Preferably, in Step 1, according to the simulation environment used, install the executable file of the 4D radar sensor model in the corresponding folder.

[0013] Preferably, 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 detected target, and the module frequency.

[0014] Preferably, the orientation angle of the radar on the test vehicle includes three angles: pitch, yaw, and roll. The type of detected target is based on the optional target types in the virtual simulation environment. After the parameter information is filled in and the JSON file is saved in the corresponding folder, confirm that the 4D radar sensor model can correctly call each relevant parameter during the platform simulation run.

[0015] Preferably, the settings of the IP address and port are carried out after all 4D radar parameters are set and the corresponding *.json file and the executable file of the 4D radar sensor model are added to the correct path.

[0016] Preferably, after starting the simulation, read the JSON format file containing the radar configuration information, the set IP address and port at one time through 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:

[0017] S1: Read the test vehicle information, including but not limited to the vehicle position, orientation, and speed.

[0018] S2: Read all target information, including but not limited to the absolute position and type.

[0019] S3: Read other information related to the target size.

[0020] S4: The 4D radar model will determine whether the target is within the radar detection range set by the user and whether the target type belongs to the detectable target types defined by the user based on the information provided in S1, S2, and S3 above;

[0021] S5: If the two conditions in S4 are met, the 4D radar simulation model will start to extract and calculate relevant information;

[0022] S6: After all data of each target are collected, this type of data will be classified and reorganized in JSON format;

[0023] S7: All the data related to the targets will be successively superimposed and accumulated and grouped in a single JSON message;

[0024] S8: As in step S7, the summarized JSON message will be sent via UDP to the IP address and port defined in step 3.

[0025] Preferably, the information extracted and calculated in step S5 includes data acquisition time, target ID, target type, target position in the user radar reference coordinate system, target position in the test vehicle reference coordinate system, absolute target position, target size, target position in the user radar reference coordinate system, target position in the test vehicle reference coordinate system, relative velocity of the target in the user radar reference coordinate system, relative velocity of the target in the test vehicle reference coordinate system, absolute velocity of the target, relative acceleration of the target in the user radar reference coordinate system, relative acceleration of the target in the test vehicle reference coordinate system, and absolute acceleration of the target.

[0026] 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;

[0027] The radar parameter configuration module is used to configure and manage the parameter information of the 4D radar to ensure that these parameters can be correctly called during the simulation run of the model;

[0028] The simulation interface module is used to ensure the compatibility between the 4D radar and different simulation platforms and achieve a seamless connection with the simulation environment;

[0029] 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;

[0030] The data extraction and calculation module is used to extract various information of the target and perform necessary calculations;

[0031] The data formatting module is used to structurally organize the extracted data and output it in JSON format;

[0032] 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 the simulation;

[0033] The simulation control module is used to control the start and stop of the simulation, and monitor the running status and data capture of the radar module during the simulation process;

[0034] The deep interaction module is used to achieve deep interaction with the simulation environment, and ensure the accuracy of detection and calculation by real-time reading of information;

[0035] The code adaptation module is used to support the adjustment of the source code according to the requirements of a specific simulation environment.

[0036] 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 handling mechanism.

[0037] Preferably, 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 record module, which can save key information including start time, end time and runtime target data during the simulation process.

[0038] Compared with the prior art, the beneficial effects of the present invention are:

[0039] 1. The present invention can build an advanced 4D millimeter-wave radar model, which can be seamlessly compatible with various existing virtual simulation platforms on the market, and can also provide users with a detailed list of target information in JSON format via UDP.

[0040] 2. The present invention can perform diverse calculations, providing users with information including but not limited to the position, speed, size, etc. of the detected target. For different simulation platforms, the 4D radar model can be fine-tuned to ensure its compatibility and functionality, and seamless integration can be achieved only by correspondingly adjusting the required input information for different simulation platforms. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flowchart of the simulation method of the present invention.

[0042] Figure 2 It is an example diagram of the 4D radar configuration of the present invention.

[0043] Figure 3 It is a schematic diagram of the output structure of the target record information of the present invention.

[0044] Figure 4 This is a schematic diagram of the modules of the 4D radar sensor model of the present invention.

[0045] Figure 5 This is a schematic diagram of the working process of the radar parameter configuration module of the present invention.

[0046] Figure 6 This is a schematic diagram of the working process of the target monitoring module of the present invention.

[0047] Figure 7 This is a schematic diagram of the functions of the data extraction and calculation module of the present invention.

[0048] Figure 8 This is a schematic diagram of the working process of the data formatting module of the present invention.

[0049] Figure 9 This is a schematic diagram of the working process of the simulation control module of the present invention.

[0050] Figure 10 This is a schematic diagram of the working process of the depth interaction module of the present invention. Detailed implementation manners

[0051] Next, in combination with the accompanying drawings and specific implementation manners, the present invention will be further described. It should be noted that, on the premise of no conflict, any combination of the following-described embodiments or technical features can form a new embodiment. It should be known that the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0052] Please refer to Figures 1 to 10 , the present invention provides a technical solution: First, construct a 4D radar model that can adapt to multiple simulation platforms and can present a list of target object information to the user in JSON format through the UDP protocol.

[0053] The core advantage of the present invention is that it can perform diverse calculations and provide the user with information including but not limited to the position, speed, size, etc. of the detected target object.

[0054] For different simulation platforms, the 4D radar model can be fine-tuned to ensure its compatibility and functionality.

[0055] The working principle of the present invention is as follows:

[0056] 1. Load the executable program of the 4D radar in the simulation platform.

[0057] 2. Complete the configuration of the radar parameter information *.json file, and the parameters include but are not limited to the horizontal and vertical field of view angles, beam coverage range, etc.

[0058] 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.

[0059] 4. During the simulation run, the 4D radar module will extract information including but not limited to the relative and absolute positions, velocities, accelerations, target sizes, etc. of the detected targets from the target information.

[0060] 5. During the simulation process, various types of required information marked in step 4 will be integrated and saved to 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.

[0061] Steps for using the 4D radar sensor model:

[0062] 1. Install the executable file of the 4D radar sensor model in the corresponding folder according to the simulation environment used.

[0063] 2. Fill in the relevant parameters of the 4D radar to complete the configuration. The parameter configuration file is in JSON format and named "4Dradar_input.json". The following information must be filled in, as Figure 1 shown:

[0064] 2.1: Installation position (x, y, z) of the radar on the test vehicle (unit: meter)

[0065] 2.2: Orientation angles (pitch, yaw, roll) of the radar on the test vehicle (unit: degree)

[0066] 2.3: Maximum and minimum horizontal field of view angles (unit: degree)

[0067] 2.4: Maximum and minimum vertical field of view angles (unit: degree)

[0068] 2.5: Beam coverage range (unit: meter)

[0069] 2.6: Types of detected targets (optional target types based on the virtual simulation environment)

[0070] 2.7: Module frequency (unit: hertz)

[0071] 3. After completing the filling of the parameter information in step 2 and saving the JSON file in the corresponding folder, it is necessary to confirm that the 4D radar sensor model can correctly call each relevant parameter during the platform simulation run.

[0072] 4. When all 4D radar parameters are set and the corresponding *.json file and the 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 positions of the IP address and port customization modules may vary in different simulation environments.

[0073] 5. After completing the above settings, the simulation can be started. After startup, the 4D radar reads the following information at once:

[0074] 5.1: A JSON format file containing radar configuration information.

[0075] 5.2: User-defined (if any) IP address and port or use the default IP address and port.

[0076] 6. During the simulation, at each time step, the 4D radar sensor model will:

[0077] 6.1: Read the test vehicle information, including but not limited to vehicle position, orientation, speed, etc.

[0078] 6.2: Read all target information, including but not limited to absolute position, type, etc.

[0079] 6.3: Read other information related to the target size.

[0080] 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:

[0081] 6.4.1: Whether the target is within the radar detection range set by the user

[0082] 6.4.2: Whether the target type belongs to the detectable target types defined by the user

[0083] 6.5: If the conditions of 6.4.1 and 6.4.2 are met, the 4D radar simulation model will start to extract and / or calculate the following information:

[0084] 6.5.1: Data acquisition time

[0085] 6.5.2: Target ID

[0086] 6.5.3: Target type

[0087] 6.5.4: Target position in the user's radar reference coordinate system

[0088] 6.5.5: Target position in the test vehicle reference coordinate system

[0089] 6.5.6: Absolute position of the target

[0090] 6.5.7: Object size (length, width, height)

[0091] 6.5.8: Object position in the user radar reference coordinate system (distance Dist, horizontal angle Alpha, vertical angle Beta)

[0092] 6.5.9: Object position in the test vehicle reference coordinate system (distance Dist, horizontal angle Alpha, vertical angle Beta)

[0093] 6.5.10: Object relative velocity in the user radar reference coordinate system

[0094] 6.5.11: Object relative velocity in the test vehicle reference coordinate system

[0095] 6.5.12: Object absolute velocity

[0096] 6.5.13: Object relative acceleration in the user radar reference coordinate system

[0097] 6.5.14: Object relative acceleration in the test vehicle reference coordinate system

[0098] 6.5.15: Object absolute acceleration

[0099] 6.6: After all data of each object is collected, this type of data will be classified and reorganized in JSON format (as shown Figure 2 shown).

[0100] 6.7: All data related to objects will be successively stacked and accumulated into one JSON message for grouping.

[0101] 6.8: As in step 6.7, the aggregated JSON message will be sent to the IP address and port defined in step 4 via UDP.

[0102] 7. The user can stop the simulation at any time, or stop it according to the simulation end time set in this test case.

[0103] If certain missing information that cannot be directly read and calculated by the 4D radar sensor model is required, the source code needs to be adjusted to adapt to the specific simulation environment, so as to implement the reading of basic information through the API of the used simulation platform, such as test vehicle information, object information, etc.

[0104] The 4D radar sensor model of the present invention can support the completion of the above simulation method. The model includes a radar parameter configuration module, a simulation interface module, an object 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.

[0105] The specific content of each module is as follows:

[0106] 1. Radar Parameter Configuration Module

[0107] This module is responsible for obtaining and storing various configuration parameters of the 4D radar sensor to ensure that the radar settings meet the actual application requirements.

[0108] User input: Allows users to set the installation location (x, y, z coordinates), orientation angles (pitch, yaw, roll), field of view angles (horizontal and vertical), beam coverage, detection target types, and module frequencies of the radar by filling in a JSON-format configuration file.

[0109] Verification mechanism: After the parameters are input, this module can verify the input data to ensure its legality and rationality (for example, the field of view angle should be within a specific range).

[0110] Parameter update: Supports users to dynamically change parameters during simulation and can apply new settings in real time to achieve more flexible operations.

[0111] 2. Simulation Interface Module

[0112] This module ensures the interaction between the 4D radar model and different simulation platforms and provides the infrastructure for data transmission.

[0113] Platform compatibility: Provides specific API interfaces for different virtual simulation platforms to ensure that the radar model can be successfully integrated and support the platform functions mutually.

[0114] UDP protocol implementation: Implements the sending and receiving mechanisms of the UDP protocol to efficiently transmit the data detected by the radar and ensure timeliness and reliability.

[0115] Error handling: Has an error capture and handling mechanism that can give corresponding feedback for users to actively handle when data transmission or interface call fails.

[0116] 3. Target Detection Module

[0117] This module extracts vehicle and target information from the simulation environment and determines detectable targets through a judgment algorithm.

[0118] Information extraction: Reads the real-time status information of the test vehicle, including position, speed, orientation, etc., and at the same time obtains the information of all possible detection targets, such as size, type, and motion state.

[0119] Detection logic: Implements complex detection logic, such as using a threshold algorithm to determine whether a target is within the set detection range, and filtering the target types at the same time to ensure that only the targets of interest to users are extracted.

[0120] Status update: During the simulation run, the status information of the target object is updated in real time for subsequent data extraction and calculation work.

[0121] 4. Data Extraction and Calculation Module

[0122] Responsible for extracting key information from the information obtained by the target detection module and performing calculations.

[0123] Information processing flow: Calculations are performed as needed based on information such as the absolute position, speed, acceleration, and size of the target object.

[0124] Relative calculation: Calculate the position, speed, and acceleration of the target object relative to the user's radar and the test vehicle coordinate system to facilitate the generation of real-time monitoring data.

[0125] Batch processing: Support batch extraction and calculation of information for multiple target objects to improve processing efficiency.

[0126] 5. Data Formatting Module

[0127] Format and organize the extracted and calculated data for subsequent use.

[0128] JSON structure design: Design a reasonable JSON structure to clearly display the data, including fields such as timestamp, target ID, type, position, speed, and acceleration.

[0129] Classification and grouping: At the end of each simulation time step, classify and group the information of different target objects to form a unified output format.

[0130] Output verification: Ensure that the organized data is verified before being sent to avoid errors during data transmission.

[0131] 6. User Interface and Configuration Module

[0132] Provide an interface for user settings, supporting parameter configuration and simulation control.

[0133] User-friendly interface: Design an intuitive user interface, including input boxes, selection boxes, and buttons, so that users can easily set IP addresses, ports, and other radar parameters.

[0134] Default configuration: Provide default IP address and port configuration options to simplify user operations while ensuring that users can perform personalized settings at any time.

[0135] Feedback mechanism: Provide instant feedback when users make operations, inform whether the configuration is successful, and give suggestions to optimize the configuration.

[0136] 7. Simulation Control Module

[0137] Responsible for controlling the start and stop of the simulation and monitoring the simulation execution status.

[0138] Control logic: Implements the start, pause, and termination operations of the simulation, and can provide users with the option to select the end time of the simulation.

[0139] Status monitoring: Monitors the working status of the 4D radar, records abnormal situations during the simulation operation, and provides corresponding alarm mechanisms.

[0140] Log recording: Saves key information during the simulation process, including start time, end time, and target object data during operation, facilitating subsequent analysis and evaluation.

[0141] 8. Deep Interaction Module

[0142] Achieves deep interaction with the simulation environment to enhance detection accuracy.

[0143] Information synchronization: Synchronizes data with vehicles and target objects in the simulation environment in real time to ensure that all information is in the latest state.

[0144] Detection standard self-check: Self-checks the detection results of target objects according to the set detection standards to ensure the accuracy and reliability of the data.

[0145] Feedback mechanism: Allows the radar system to dynamically adjust its working status according to the detection results to improve detection performance and efficiency.

[0146] 9. Code Adaptation Module

[0147] Supports adjusting the source code according to a specific simulation environment.

[0148] Customized development: Makes necessary modifications to the source code according to user requirements or the characteristics of a specific simulation environment to achieve specific functions or cooperate with specific access methods.

[0149] Documentation support: Provides detailed documentation guidance to help developers quickly understand the code structure and modification methods, reducing possible confusion during the adaptation process.

[0150] Version management: Ensures the version management of the module, facilitating the tracking of code changes and maintaining multi-version compatibility to support different simulation requirements.

[0151] The 4D radar sensor model of the present invention is specifically designed for the field of autonomous driving and sensor simulation, and has the ability to cooperate with various simulation platforms.

[0152] Cross-simulation platform compatibility. The 4D radar model has the ability to seamlessly compatible with a variety of virtual simulation platforms, highlighting its excellent performance in versatility and practicality.

[0153] UDP data transmission and JSON output format. This model adopts the UDP (User Datagram Protocol) transmission mechanism and outputs the list of detected targets in JSON format, reflecting the innovative features of the present invention in data communication.

[0154] Precise detection of the vertical height of the target. Different from traditional millimeter-wave radar technology, this 4D radar can help obtain the true height data of the detected target, and this function marks another breakthrough in the accuracy of sensor technology for detecting targets.

[0155] Synchronous detection, mapping, and tracking capabilities. This 4D radar can achieve synchronous detection, mapping, and tracking of moving targets, providing key technical support for autonomous driving / advanced driver assistance systems.

[0156] Configuration flexibility. The present invention supports users to modify some parameters to adapt to different types of 4D radars. The types of parameters that can be modified include horizontal and vertical field of view angles, beam coverage, detected target types, and module frequencies, etc.

[0157] Data extraction and calculation. This model supports the extraction and calculation of various information of the detected targets, including key data such as the position, speed, size, and acceleration of the targets.

[0158] Structured output design. The present invention defines a structured output design, records target information in JSON format at each simulation time step, and provides ordered and efficient data support for the simulation process and subsequent analysis.

[0159] IP address and port settings. This model supports users to customize the IP address and port for network transmission of data, and at the same time provides default configurations to simplify user operations.

[0160] Deep interaction with the simulation environment. This model realizes deep interaction with the simulation environment by reading vehicle and target information, self-checking detection criteria, and calculating relevant data of each detected target.

[0161] Customized adaptation to specific simulation environments. To ensure compatibility and functionality in specific simulation environments, the source code of this model supports partial modification to fully adapt to the simulation environment.

[0162] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. 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: After completing the settings, start the simulation. 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 of the target, the speed, acceleration, and size of the target. Step 5: During the simulation, all required information extracted 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. Finally, wait for the user to stop the simulation or stop the simulation on time. 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.

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 executable program 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 2, 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.

4. 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.

5. 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 sensor 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 sensor model starts to extract and calculate relevant information; S6: After completing the data collection of each target object, the collected 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 is sent via UDP to the IP address and port defined in step 3.

6. The 4D millimeter wave radar sensor simulation method for autonomous driving simulation test according to claim 5, 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.

7. 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.

8. The 4D millimeter wave radar sensor simulation method for autonomous driving simulation test according to claim 7, 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.

9. The 4D millimeter wave radar sensor simulation method for autonomous driving simulation test according to claim 7, 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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