WO2023207016A1 - Autonomous driving test system and method based on digital twin cloud control platform - Google Patents

Abstract

An autonomous driving test system and method based on a digital twin cloud control platform. A high-fidelity virtual scene, which needs to be developed and tested, is generated by means of simulation software, and a plurality of virtual sensor models are established in the simulation software; information of a target object that is detected by a virtual sensor is input into an autonomous driving controller on a real vehicle by means of a bus, and information fusion and control decision are performed; and the controller sends decided acceleration, braking and steering instructions to a real-vehicle actuator. The real vehicle feeds vehicle motion state information on a vehicle bus back to the virtual scene, so as to complete synchronization of vehicle positions, closed-loop real-time testing and verification of the entire system are realized, and a scene-related twin effect is displayed in real time by means of the digital twin cloud control platform. Therefore, an integration test related to virtuality and reality combined autonomous driving algorithm function verification, a scene simulation test and a vehicle is realized, and the defects of existing real-vehicle road tests and software simulation testing technology are overcome, such that the test efficiency is improved.

Description

An autonomous driving testing system and method based on digital twin cloud control platform Technical field

The invention relates to the field of automatic driving technology, and specifically relates to an automatic driving testing system and method based on a digital twin cloud control platform.

Background technique

With the clarification of market demand and the development of autonomous driving technology, many traditional vehicle manufacturing companies have transformed or established autonomous driving departments, and some autonomous driving start-ups have developed many autonomous driving-related controllers and sensors, and have deeply integrated integrated applications. In L2 to L4 smart cars. These systems empower smart cars, but system robustness and security to cope with various long-tail effects have become bottlenecks that restrict the use of high-level smart cars. Therefore, sufficient testing and verification of autonomous vehicles is required.

Affected by factors such as the difficulty of constructing complex scenarios, test costs, and safety risks, it is difficult to meet the research and testing needs of intelligent vehicles by relying solely on actual vehicle road tests. In addition to actual vehicle road testing, software simulation testing is also an important technical means for smart vehicle testing and verification. It has the advantages of low cost, short cycle, and high efficiency. However, its scene modeling and sensor simulation are mostly based on ideal conditions and do not take into account Noise problems caused by the sensor hardware or software itself, as well as rain, snow, dust and other environmental factors that interfere with radar sensing performance, make the confidence of simulation results have great limitations.

To this end, the present invention proposes an automatic driving testing system and method based on a digital twin cloud control platform.

Contents of the invention

In order to solve the above problems, the present invention proposes an automatic driving test system and method based on a digital twin cloud control platform, which realizes virtual and real combined automatic driving algorithm function verification, scene simulation testing and vehicle-related integrated testing, and overcomes the existing Real vehicle road testing and software simulation testing technology are insufficient to improve testing efficiency.

The present invention provides the following technical solutions.

An autonomous driving test system based on a digital twin cloud control platform, including:

Autonomous driving simulation software builds virtual dynamic test scenarios based on vehicle simulation model data and real vehicle test tasks;

The data center processes real vehicle test tasks and virtual dynamic test scenarios into point clouds, images and ADC sensing data; and converts real vehicle status and control data into vehicle simulation model data and sends it to the autonomous driving simulation software;

Autonomous driving algorithm platform, which includes:

Perception fusion algorithm platform, used to fuse point cloud, image and ADC perception data, and generate traffic target location and category information;

The decision-making planning algorithm platform outputs the traffic target trajectory based on the traffic target location and category information;

The motion control algorithm platform calculates vehicle control signals based on the traffic target trajectory;

The real vehicle actuator is installed on the test vehicle. According to the vehicle control signal, the test vehicle performs acceleration, braking and steering actions, and generates real vehicle status and control data;

as well as,

The dynamic twin platform renders and displays real-time dynamic twin effects based on virtual dynamic test scene data and real-time vehicle simulation model data.

Preferably, the construction of the virtual dynamic test scenario data includes:

The automatic driving simulation software generates virtual dynamic test scenarios required for development and testing, and establishes multiple virtual sensor models in the simulation software. The target object information detected by the virtual sensors constitutes virtual dynamic test scenario data, which is injected into the automatic driving system through the bus. in the driving algorithm platform.

Preferably, the real vehicle actuator and the data center perform wireless communication data transmission through a local area network;

The data center uses wired communication to convert the actual vehicle status and control data obtained by the test vehicle into vehicle simulation model data and transmits it back to the autonomous driving simulation software.

Preferably, the actual vehicle test tasks include actual vehicle collision avoidance warning and actual vehicle avoidance of pedestrians.

Preferably, the actual vehicle status and control data include pose data and GPS data.

An autonomous driving testing method based on a digital twin cloud control platform, including the following steps:

According to the real vehicle test task, OPENDRIVE data is collected and produced to obtain high-precision maps, simulate the traffic environment through simulation software, and create test cases; calibrate the mapping relationship between the real map and the virtual traffic environment, and calibrate the mapping between the tested vehicle and the simulated vehicle model. relation;

Map-related road scene and traffic scene information is collected through sensors installed on the roadside, and processed into point clouds, images and ADC sensing data;

According to the actual vehicle test task, perception fusion processes point cloud, image and ADC perception data to obtain perception results, generate full-area perception results, and generate traffic target location and category information;

Plan the traffic target trajectory according to the generated traffic target position and category information, process and calculate it through the motion control algorithm, and output the vehicle control signal;

According to the vehicle control signal, the test vehicle is controlled by the real vehicle actuator for testing, and the vehicle status information is obtained;

After receiving the vehicle status information, the built-in high-precision road network map and objective environmental scene model are loaded and rendered, and the real-time dynamic twin effect is presented in real time through the visual interface.

Preferably, the testing by controlling the test vehicle through a real vehicle actuator according to the vehicle control signal includes the following steps:

Send vehicle control signals to the test vehicle on the road at the test site to obtain vehicle status information;

After receiving the vehicle status information, the built-in high-precision road network map and objective environmental scene model are loaded and rendered, and the parallel twin of the test field is presented in real time through the visual interface.

Preferably, the testing by controlling the test vehicle through a real vehicle actuator according to the vehicle control signal includes the following steps:

The OPENDRIVE data is OPENDRIVE data adapted to scenarios related to the handling and stability square, and the vehicle control signal is sent to the test vehicle located in the handling and stability square to obtain vehicle status information;

After receiving the vehicle status information, the built-in high-precision road network map and objective environmental scene model are loaded and rendered, and the twin of the real intersection is presented in real time through the visual interface.

Preferably, it also includes:

Control the movement of the simulated vehicle model through vehicle control signals and obtain vehicle status information;

The motion information of the simulated vehicle model at each timestamp is used as the analysis basis for the performance test of the controller under test. After the case is completed, the algorithm is analyzed.

Beneficial effects of the present invention:

The present invention proposes an automatic driving test system and method based on a digital twin cloud control platform, which can automatically generate large batches of simulation sample data and intelligent true value calibration for various virtual experimental conditions and scenarios, and is efficient, fast, and Accurately generate training data samples required in the field of autonomous vehicle driving; it can integrate high-precision map data road mining equipment and combine the expression of key scene elements and automatic reconstruction methods to quickly build three-dimensional reconstruction scenes that meet the testing requirements of different dimensions to help users improve The use value of self-collected scene data; it can integrate simulation test tools, V2X communication equipment, real test vehicles and other functional units to carry out digital twin autonomous driving tests based on virtual simulation and real environments, and has the ability to carry out virtual complex scenarios under limited resource conditions Autonomous driving real vehicle test verification capabilities.

The invention not only has the advantages of real vehicle road testing, but also integrates the advantages of simulation technology by integrating real vehicles in the virtual traffic environment, realizing the integration of real test dynamics, traffic flow simulation and complex virtual traffic scenes. Organically combined, it can continuously adapt to the rapidly changing testing needs of autonomous driving technology.

Description of the drawings

Figure 1 is a flow chart of the digital twin autonomous driving test method according to the embodiment of the present invention;

Figure 2 is a rendering of the digital twin according to the embodiment of the present invention;

Figure 3 is the testing process of the Class A software-in-the-loop vehicle automatic driving algorithm according to the embodiment of the present invention;

Figure 4 is the virtual and real combined test and parallel twin process of Class B autonomous driving of the embodiment of the present invention;

Figure 5 is a class C vehicle automatic driving adaptability test and twin process according to the embodiment of the present invention;

Figure 6 shows test items and test scenarios according to the embodiment of the present invention;

Figure 7 is a simulation test road scene rendering according to the embodiment of the present invention;

Figure 8 is a layout diagram of an intersection and a stability plaza according to an embodiment of the present invention.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention and are not intended to limit the present invention.

Example

The invention provides an automatic driving testing system and method based on a digital twin cloud control platform. As shown in Figure 1-8. As shown in Figure 1, the present invention integrates two functional units of the digital twin cloud control platform and the test vehicle.

The digital twin cloud control platform includes dynamic twin platform, autonomous driving simulation software and data center. Autonomous driving simulation software constructs virtual dynamic test scene data based on real vehicle test tasks issued by testers (real vehicle collision avoidance warning and real vehicle avoidance of pedestrians); the data center processes data based on real vehicle test tasks and virtual dynamic test scenarios Sensing data for point clouds, images and ADC (Analog to digital converter); dynamic twin platform; through the data center, the real vehicle status and control data (including pose data and GPS data) generated by the real vehicle actuator are converted into The vehicle simulation model data is sent to the autonomous driving simulation software; the dynamic twin platform renders and displays the real-time dynamic twin effect based on the virtual dynamic test scene data and real-time vehicle simulation model data sent by the autonomous driving simulation software. The rendering effect is shown in Figure 2 .

The digital twin cloud control platform is the host computer module of the test management software. It provides multi-account management, authority management, import of requirements, preparation of test plans on the platform, and can realize the management of specific test scenarios through integrated simulation software. As well as test record playback, test analysis, report generation and other functions. Users can create and edit test scenarios on the digital twin cloud control platform. They can directly access the software-in-the-loop and model-in-the-loop methods for testing. They can also use wireless methods on the actual vehicle side to add data to the digital twin cloud on the digital twin cloud. The test plan and test scenarios formulated on the control platform are downloaded to the vehicle-mounted terminal for vehicle-in-the-loop testing.

The test vehicle side includes: an autonomous driving algorithm platform, which is installed on the test vehicle; the autonomous driving algorithm platform includes: a perception fusion algorithm platform, which is used to fuse point clouds, images and ADC perception data sent by the data processing platform, and generate traffic targets Position and category information; the decision planning algorithm platform outputs the traffic target trajectory based on the generated traffic target location and category information; the motion control algorithm platform calculates the vehicle control signal based on the output traffic target trajectory;

And, the real vehicle actuator is mounted on the test vehicle, receives vehicle control signals, the test vehicle performs acceleration, braking and steering actions, and generates real vehicle status and control data;

The test vehicle end and the digital twin cloud control platform end carry out wireless communication data transmission through the local area network, receive the test task configuration and complete the vehicle in-the-loop test. The data center uses wired communication to convert the actual vehicle status and control data (including pose data and GPS data) obtained by the test vehicle into vehicle simulation model data and transmits it back to the autonomous driving simulation software to achieve "from virtual to real" Virtual-real interactive closed-loop automated testing of sensor injection and “real-to-virtual” control feedback.

The main functions of the invention are:

The invention can automatically generate large batches of simulation sample data and intelligently calibrate true values for various virtual experimental working conditions and scenarios, and efficiently, quickly and accurately generate training data samples required in the field of automatic driving of automobiles;

This invention can integrate high-precision map data road collection equipment and combine the expression of key scene elements and the automatic reconstruction method to quickly construct a three-dimensional reconstruction scene that meets the test requirements of different dimensions, helping users improve the use value of self-collection scene data;

This invention can integrate functional units such as simulation test tools, V2X communication equipment, real test vehicles, etc., to carry out digital twin automatic driving tests based on virtual simulation and real environments, and has the ability to carry out automatic driving real vehicle test verification in virtual complex scenarios under limited resource conditions. ability.

Example 1

As shown in Figure 3, SIL software-in-the-loop autonomous driving algorithm testing and visualization:

Step 1: Collect and produce OPENDRIVE data. Obtain the data through the collection vehicle and produce it into a high-precision map. The simulation software simulates the traffic environment;

Step 2: The simulator imports the prepared map into the simulation software and creates a test case;

Step 3: In the test case, virtual cameras, lidar and other sensors are added to the roadside to collect detection data within the scanning range. The sensors obtain map-related road scene and traffic scene information and pass it to the ADAS/AD algorithm platform through ROS;

Step 4: According to the test requirements, access the autonomous driving algorithm under test. The perception fusion algorithm processes point clouds, images, true values and other information, performs perception fusion, calculates perception results, and generates global perception results. The output position, category and other information are passed The topic communication method is passed to the decision-making planning algorithm. The decision-making planning algorithm outputs the vehicle trajectory after calculation. It is passed to the motion control algorithm through the topic communication method. After processing and calculation by the motion control algorithm, the control signal is finally output;

Step 5: The vehicle model obtains the control signal output by the controller under test for the controller sensor signal through ROS. The vehicle model moves and returns vehicle status information such as GPS, vehicle position and attitude, and the simulation software obtains it through the API interface to proceed to the next moment. algorithm testing;

Step 6: The motion information of the simulated vehicle model at each timestamp is used as the analysis basis for the performance test of the controller under test. After the case is completed, the algorithm is analyzed.

Example 2

As shown in Figure 4, the virtual and real combined test and parallel twin of complete vehicle autonomous driving:

Step 1: According to the test requirements, collect and produce OPENDRIVE data. Obtain the data through the collection vehicle and produce it into a high-precision map. The simulation software simulates the simulated traffic environment, calibrates the mapping relationship between the real map and the virtual traffic environment, and calibrates the vehicle under test and the simulated vehicle. The mapping relationship of the model;

Step 2: The simulator imports the prepared map into the simulation software and creates a test case;

Step 3: The simulation software transfers the map-related road scene and traffic scene information to the ADAS/AD algorithm platform through ROS;

Step 4: According to the test requirements, access the autonomous driving algorithm under test. The perceptual fusion algorithm processes point clouds, images, true values and other information to perform perceptual fusion. The output position, category and other information are passed to the decision-making planning algorithm through topic communication, and the decision is made. The planning algorithm outputs the vehicle trajectory after calculation, and passes it to the motion control algorithm through topic communication. After processing and calculation by the motion control algorithm, the control signal is finally output;

Step 5: Send the control signal to the driverless bus located on the road of the test site. After receiving the control signal, the industrial computer of the actual vehicle moves and returns vehicle status information such as GPS, vehicle position and attitude, etc. The simulation software obtains it through the API interface and performs Algorithm testing at the next moment;

Step 6: After receiving vehicle status and other information, the simulation software loads the built-in high-precision road network map and objective environmental scene model, renders it through the cloud control platform, and presents the parallel twin of the test field in real time on the visual interface.

Example 3

As shown in Figure 5, the vehicle autonomous driving adaptability test and twin process:

Step 1: According to the test requirements, collect and produce OPENDRIVE data that adapts to the scenarios related to the handling and stability square. The data is obtained through the collection vehicle and produced into a high-precision map. The simulation software simulates the traffic environment and calibrates the mapping between the real map and the virtual traffic environment. Relationship, calibrate the mapping relationship between the vehicle under test and the simulated vehicle model;

Step 2: The simulator imports the prepared map into the simulation software and creates a test case;

Step 3: The simulation software transfers the map-related road scene and traffic scene information to the ADAS/AD algorithm platform through ROS;

Step 4: According to the test requirements, access the autonomous driving algorithm under test. The perceptual fusion algorithm processes point clouds, images, true values and other information to perform perceptual fusion. The output position, category and other information are passed to the decision-making planning algorithm through topic communication, and the decision is made. The planning algorithm outputs the vehicle trajectory after calculation, and passes it to the motion control algorithm through topic communication. After processing and calculation by the motion control algorithm, the control signal is finally output;

Step 5: Send the control signal to the driverless bus located in the Stability Square. After receiving the control signal, the real vehicle industrial computer moves and returns vehicle status information such as GPS, vehicle position and attitude, etc. The simulation software obtains it through the API interface. Carry out algorithm testing at the next moment;

Step 6: After receiving vehicle status and other information, the simulation software loads the built-in high-precision road network map and objective environmental scene model, renders it through the cloud control platform, and presents a twin of the real intersection on the visual interface.

Example 4

The present invention uses simulation software to generate virtual scenes required for development and testing, and establishes multiple virtual sensor models in the simulation software, and injects target object information detected by the virtual sensors into the automatic driving controller on the real vehicle through the bus for information processing. Fusion and control decision-making, the controller sends the decision-making acceleration, braking and steering instructions to the real vehicle actuator, and the real vehicle then feeds back the vehicle motion status information on the vehicle bus to the virtual scene through the roadside communication system to complete the vehicle Position synchronization, thereby achieving closed-loop real-time simulation of the entire system.

In order to realize the system requirements of the present invention, we build a digital twin test system based on the entire vehicle, fully combining the advantages of XIL in-the-loop simulation testing and real vehicle testing, generating virtual test scenes through simulation software, and injecting virtual scene information into the automatic driving algorithm. In the controller, the movement state of the actual vehicle under test is controlled.

Set up and generate the virtual scenes required for development and testing in the virtual simulation system, and deploy the required virtual sensor models in the simulation software (supporting cameras, millimeter wave radar, lidar, ultrasonic radar), and the virtual sensors detect in the simulation environment The obtained target object information is injected into the autonomous driving algorithm platform through the wireless network for information fusion and decision-making control. The autonomous driving algorithm platform sends the decision-making acceleration, braking, steering and other control signals to the real vehicle actuator. The actual vehicle operation At the same time, the vehicle information collection system collects real-time real vehicle motion status information and feeds it back to the virtual scene to complete the synchronization of vehicle positions in the virtual and real environments, thereby realizing closed-loop real-time simulation testing of the entire digital twin system. By providing a standard sensor data format interface and vehicle status information receiving interface, more general injection test requirements are achieved.

The virtual simulation platform can generate the original signal or target signal of the sensor according to the needs of the test scenario in the test task management system, and inject it into the autonomous driving prototype before the data perception and fusion module or the decision-making layer algorithm.

details as follows:

(1) Original signal injection: The virtual simulation platform generates original signals based on different models such as camera models, millimeter-wave radar models, lidar models, and V2X models, and injects various basic test data in the original signals into the vehicle controller. The original signal of the camera is the video stream, and the original signal of the lidar is the laser point cloud data;

(2) Target signal injection: Based on the idealized true value sensing model, the virtual simulation platform injects the following target signals detected within a certain range of the prototype vehicle into the vehicle controller through CAN or other suitable methods;

(3) Motor vehicles, pedestrians, non-motor vehicles, traffic signs, traffic lights and lane lines, etc.;

(4) The position, orientation, bounding box, speed, acceleration and angular velocity of the above-mentioned target.

In order to verify the effectiveness of the present invention, a classic intersection model is used to conduct intelligent vehicle testing for the test scenario. The test in this article will be based on the autonomous driving test site of Chang'an University, with the Stability Plaza as the physical basis, and a virtual and real smart vehicle test will be carried out based on urban intersection scenarios. A two-way eight-lane intersection in a certain area of Xi'an City is used as a model. The typical traffic conditions at intersections in urban traffic flow were extracted. Test samples were set up for starting, stopping, going straight, and turning scenarios in the intersection. At the same time, the situation of pedestrians crossing the road was taken into consideration, and corresponding scenarios were set up. Carry out the test and simulate the test road scene effect as shown in Figure 7.

The selected test items and test scenarios are shown in Figure 6. The present invention uses the minimum safe distance at the warning time and the TTC at the warning time as the judgment conditions for whether to issue a warning, and uses the minimum safe distance model between vehicles to illustrate the possibility of collision during vehicle braking. In specific application scenarios, the time when the application issues an early warning must ensure that there is sufficient time or distance between the test vehicle and the target vehicle for the smart vehicle to react and brake to a safe speed to avoid a collision.

Based on the road and handling stability square recognized by the Ministry of Transportation of Chang'an University as the "Autonomous Driving Closed Site Test Base", a parallel twin virtual scene was constructed at a certain intersection in Xi'an, and five test items were analyzed. Real smart vehicles were used as test objects. After accepting the virtual traffic flow scenario, testing and decision-making are carried out in the real scenario, and the combination of virtual and real smart vehicle testing can overcome the problems of existing virtual testing and field testing technology deficiencies and improve testing efficiency.

The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention shall be included in the protection scope of the present invention. Inside.

Claims (9)

An autonomous driving test system based on a digital twin cloud control platform, which is characterized by including: Autonomous driving simulation software builds virtual dynamic test scenarios based on vehicle simulation model data and real vehicle test tasks; The data center processes real vehicle test tasks and virtual dynamic test scenarios into point clouds, images and ADC sensing data; and converts real vehicle status and control data into vehicle simulation model data and sends it to the autonomous driving simulation software; Autonomous driving algorithm platform, which includes: Perception fusion algorithm platform, used to fuse point cloud, image and ADC perception data, and generate traffic target location and category information; The decision-making planning algorithm platform outputs the traffic target trajectory based on the traffic target location and category information; The motion control algorithm platform calculates vehicle control signals based on the traffic target trajectory; The real vehicle actuator is installed on the test vehicle. According to the vehicle control signal, the test vehicle performs acceleration, braking and steering actions, and generates real vehicle status and control data; as well as, The dynamic twin platform renders and displays real-time dynamic twin effects based on virtual dynamic test scene data and real-time vehicle simulation model data. The automatic driving test system based on the digital twin cloud control platform according to claim 1, characterized in that the construction of the virtual dynamic test scene data includes: The automatic driving simulation software generates virtual dynamic test scenarios required for development and testing, and establishes multiple virtual sensor models in the simulation software. The target object information detected by the virtual sensors constitutes virtual dynamic test scenario data, which is injected into the automatic driving system through the bus. in the driving algorithm platform. The automatic driving test system based on the digital twin cloud control platform according to claim 1, characterized in that the real vehicle actuator and the data center perform wireless communication data transmission through a local area network; The data center uses wired communication to convert the actual vehicle status and control data obtained by the test vehicle into vehicle simulation model data and transmits it back to the autonomous driving simulation software. The automatic driving test system based on the digital twin cloud control platform according to claim 1, characterized in that the real vehicle test tasks include real vehicle collision avoidance warning and real vehicle avoidance of pedestrians. The automatic driving test system based on the digital twin cloud control platform according to claim 1, wherein the actual vehicle status and control data include pose data and GPS data. An autonomous driving testing method based on a digital twin cloud control platform, which is characterized by including the following steps: According to the real vehicle test task, OPENDRIVE data is collected and produced to obtain high-precision maps, simulate the traffic environment through simulation software, and create test cases; calibrate the mapping relationship between the real map and the virtual traffic environment, and calibrate the mapping between the tested vehicle and the simulated vehicle model. relation; Map-related road scene and traffic scene information is collected through sensors installed on the roadside, and processed into point clouds, images and ADC sensing data; According to the actual vehicle test task, perception fusion processes point cloud, image and ADC perception data to obtain perception results, generate full-area perception results, and generate traffic target location and category information; Plan the traffic target trajectory according to the generated traffic target position and category information, process and calculate it through the motion control algorithm, and output the vehicle control signal; According to the vehicle control signal, the test vehicle is controlled by the real vehicle actuator for testing, and the vehicle status information is obtained; After receiving the vehicle status information, the built-in high-precision road network map and objective environmental scene model are loaded and rendered, and the real-time dynamic twin effect is presented in real time through the visual interface. The automatic driving test method based on the digital twin cloud control platform according to claim 6, characterized in that the testing by controlling the test vehicle through the real vehicle actuator according to the vehicle control signal includes the following steps: Send vehicle control signals to the test vehicle on the road at the test site to obtain vehicle status information; After receiving the vehicle status information, the built-in high-precision road network map and objective environmental scene model are loaded and rendered, and the parallel twin of the test field is presented in real time through the visual interface. The automatic driving test method based on the digital twin cloud control platform according to claim 6, characterized in that the testing by controlling the test vehicle through the real vehicle actuator according to the vehicle control signal includes the following steps: The OPENDRIVE data is OPENDRIVE data adapted to scenarios related to the handling and stability square, and the vehicle control signal is sent to the test vehicle located in the handling and stability square to obtain vehicle status information; After receiving the vehicle status information, the built-in high-precision road network map and objective environmental scene model are loaded and rendered, and the twin of the real intersection is presented in real time through the visual interface. The automatic driving testing method based on the digital twin cloud control platform according to claim 6, further comprising: Control the movement of the simulated vehicle model through vehicle control signals and obtain vehicle status information; The motion information of the simulated vehicle model at each timestamp is used as the analysis basis for the performance test of the controller under test. After the case is completed, the algorithm is analyzed.

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