An intelligent networked traffic platform suitable for digital twinning and a testing method

Abstract

The application relates to the technical field of traffic digital twinning, and discloses an intelligent networked traffic platform suitable for digital twinning and a testing method, which comprises the following steps: an operating system is connected with the initialization of a ROS mobile robot and V2X equipment through Wifi, the self-navigation function of the ROS mobile robot is edited, and the running state feedback of the equipment is edited; a ROS mobile robot sand table is used for fusing the ROS mobile robot system and 5G-CV2X technology, a vehicle model with a networked communication environment is constructed, an information processing module is used for data information uploading, and a measured digital twinning platform interface is used for data information transmission; the vehicle model is equipped with 5G-CV2X vehicle-mounted equipment, and is endowed with 5G communication capability. In the testing process, the vehicle model fully reproduces a real networked communication environment by simulating the communication environment between the generated vehicle BSM information and the digital twinning platform, so that the accuracy of the testing result is improved, and the practical application and deployment of the digital twinning intelligent networked traffic platform are accelerated.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of traffic digital twinning technology, more specifically, it relates to an intelligent networked traffic platform suitable for digital twinning and a testing method. BACKGROUND

[0002] In recent years, digital twinning networked traffic platforms have been researched and applied in intersection signal control, ramp coordination merging, channel management, intelligent highways and even travel services. It is inferred that integrating digital twinning technology into intelligent networking will be a hot topic in future research. At the same time, a large number of testing requirements will be generated during the research process, such as digital model driven testing, digital twinning platform optimization feedback testing, intelligent networked scenario testing, etc.

[0003] However, there are the following shortcomings in the functional testing of the digital twinning traffic platform, which makes it difficult to land in actual application, such as using pure software simulation for the testing method of the digital twinning traffic platform, and lacking real networked communication environment in the testing link; the digital twinning platform may contain multiple vehicle models, and the traditional simulation method has a single vehicle model, which cannot realize the driving characteristics of different vehicle types.

[0004] Therefore, the present application provides an intelligent networked traffic platform suitable for digital twinning and a testing method, which improves the above technical problems. SUMMARY

[0005] The present application aims to overcome the shortcomings of the prior art and provides an intelligent networked traffic platform suitable for digital twinning and a testing method. The present application designs a simulation method with ROS mobile robots and 5G-V2X vehicle-mounted devices as the core. This method not only overcomes the above problems, but also autonomously simulates the interactive behavior of vehicles in real traffic scenarios, and is similar to the dynamics model of real vehicles, making the test results more realistic.

[0006] The above technical purpose of the present application is realized by the following technical scheme: an intelligent networked traffic platform suitable for digital twinning, comprising: an operating system, a ROS mobile robot sand table, an information processing module, and a measured digital twinning platform interface.

[0007] The operating system includes a PC end of the ROS operating environment, which is used to connect the initialization of the ROS mobile robot and the V2X device, edit the autonomous navigation function of the ROS mobile robot, and feedback the running state of the device through Wifi.

[0008] The ROS mobile robot sand table is constructed by integrating the ROS mobile robot system and 5G-CV2X technology, and a vehicle model with networked communication environment is constructed, which supports the switching of vehicle models and specifications such as cars, trucks, and buses.

[0009] The information processing module is used for data information uploading and receiving, and the data information uploading and receiving adopts a multi-thread programming mode.

[0010] The measured digital twin platform interface is used for data information transmission.

[0011] As a preferred technical solution of the present application, the vehicle model supports the switching process of vehicle models and specifications such as cars, trucks, and buses, which is realized by adjusting the footprint_model, min_obstacle_dist, and inflation_layer parameters in the TEB local planner and path planning cost map during autonomous navigation.

[0012] As a preferred technical solution of the present application, the vehicle model includes a ROS robot, a data conversion module, a switch, a 5G-CV2X vehicle-mounted device, and a flat panel display.

[0013] As a preferred technical solution of the present application, the ROS robot perceives surrounding environment information through radar, performs map construction, positioning, and navigation, and realizes autonomous navigation to complete the required scenarios of the digital twin platform.

[0014] The data conversion module is used to map the ROS robot spatial coordinate system to simulated vehicle BSM information, and then connect with the 5G-CV2X vehicle-mounted device through TCP communication.

[0015] The 5G-CV2X vehicle-mounted device receives the above-mentioned simulated BSM data using TCP / IP and displays it in real time on the map of the vehicle-mounted flat panel display.

[0016] As a preferred technical solution of the present application, the working steps of the data conversion module are: four-element number to radian system, direction angle mapping, and coordinate translation.

[0017] As a preferred technical solution of the present application, the four-element number to radian system process is: in the ROS robot system, four-element numbers are usually used to represent different attitudes of the robot. According to the robot four-element number data format, the conversion to radian system formula is:

[0018] Since the direction angle range of the robot is ( ), the value range of arctan and arcsin is ( ), which cannot be fully satisfied, so the arctan in (1) is converted to atan2; In the formula, roll, pitch, and yaw are the rotation angles of the robot around the X, Y, and Z directions, respectively representing the roll angle, pitch angle, and heading angle. A four-element number for describing the pose information of a robot.

[0019] As a preferred technical solution of the present application, the direction angle mapping process is: since the ROS mobile robot only moves in the horizontal plane, there is only a heading angle yaw around the Z axis; the conversion process needs to consider the initial orientation of the robot, taking the initial orientation of the robot as north for example; the heading angle yaw in formula (2) is converted to an angle: According to the corresponding relationship of the four directions, formula (3) is brought in to obtain four conversion relationships:

[0020] In the formula, , , , respectively represent the mapping relationship between the positive direction in the robot coordinate system and the four directions of east, west, south and north in the real world.

[0021] As a preferred technical solution of the present application, the coordinate translation process is: the coordinate mapping needs to consider the real shape of the earth, and for position information that is not too far apart, the terrain influence is ignored; the relationship between the two coordinate systems is simplified to deduce the formula as follows:

[0022] According to the distance formula of two latitude and longitude points by the formula of the secant:

[0023] In the formula, Δ ϕ is the difference of latitude, Δλ is the difference of longitude, ϕ 1 and ϕ 2 are the latitudes of the two points respectively, and R is the radius of the earth.

[0024] Suppose there are two points on the earth plane, the latitude and longitude coordinates of point A are (𝜆1,𝜑1), and the latitude and longitude coordinates of point B are (𝜆2,𝜑2); it is assumed that when A and B are at the same latitude: Therefore, when the difference of longitude Δλ is 0.00001°, the distance d between points A and B is approximately equal to 1m; similarly, the distance between two points under other angle difference conditions can also be calculated.

[0025] A test method, comprising the following steps:

[0026] S1: First, start the ROS mobile robot and the vehicle-mounted 5G-CV2X device, and check whether the communication between them is normal to provide a reliable network environment for testing;

[0027] S2, start the robot autonomous navigation function and information processing module in the system operation platform, load the scene target point information for navigation; at the same time, use 5G communication to establish network communication with the digital twin platform at the measured digital twin platform interface, for testing the subsequent model driving, optimization algorithm, etc. in the digital twin platform;

[0028] S3, through the multi-robot automatic navigation function, according to the pre-set target point autonomous navigation, autonomously realize the V2X test scene such as simulated vehicle lane changing and green wave passing, and the information processing module receives the simulated vehicle BSM data in real time and sends it to the measured digital twin platform interface;

[0029] S4, the measured digital twin platform interface outputs the simulated data in real time, such as vehicle driving data and traffic light phase data, and simultaneously receives the feedback optimization information from the measured digital twin platform in real time;

[0030] S5, before the end of the robot autonomous test, the user can manually intervene in the system operation platform to take over the control right in case of sudden situation or too much deviation from the actual expectation.

[0031] In summary, the present application has the following advantages:

[0032] First, it has a real network communication environment;

[0033] In the present application, the vehicle model is equipped with 5G-CV2X on-board equipment, which is given 5G communication capability. In the test process, the vehicle model fully reproduces the real network communication environment through the communication environment between the simulated vehicle BSM information and the digital twin platform, thereby improving the accuracy of the test results and accelerating the practical application and deployment of the digital twin intelligent networked transportation platform.

[0034] Second, different vehicle models can be simulated

[0035] In the present application, the vehicle model supports parameter setting, which adjusts the related parameters in the ROS robot autonomous navigation to simulate the motion characteristics of different vehicle models such as cars, trucks and buses in the driving process, effectively dealing with the variability of vehicle models in the digital twin platform. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 A framework of an intelligent networked transportation platform suitable for digital twinning is provided for the embodiments of the present application;

[0037] Figure 2 A simulation vehicle model schematic diagram is provided for the embodiments of the present application;

[0038] Figure 3 A robot and vehicle direction angle mapping diagram is provided for the embodiments of the present application;

[0039] Figure 4 A robot and vehicle coordinate translation relationship schematic diagram provided for an embodiment of the present application;

[0040] Figure 5 A simulation vehicle model physical diagram provided for an embodiment of the present application;

[0041] Figure 6 A lane changing effect schematic diagram when a robot simulates different vehicle models provided for an embodiment of the present application;

[0042] Figure 7 A display interface diagram provided for an embodiment of the present application. DETAILED DESCRIPTION

[0043] The present application will be described in detail below with specific embodiments. The following embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any form. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made. These are within the scope of the present application.

[0044] In order to make the purpose, technical solutions and advantages of the present application more clear, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0045] It should be noted that if there is no conflict, each feature in the embodiments of the present application can be combined with each other, and all within the scope of the present application. In addition, although the functional modules are divided in the device schematic diagram, and the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than the modules in the device or the order in the flowchart. In addition, the "first", "second", "third" and the like used herein do not limit the data and execution order, but only distinguish the same items or similar items with basically the same function and effect.

[0046] Unless otherwise defined, all technical and scientific terms used in the specification have the same meaning as commonly understood by those skilled in the art to which the present application belongs. The terms used in the specification of the present application are only for the purpose of describing the specific embodiments and are not intended to limit the present application. The term "and / or" used in the specification includes any and all combinations of one or more related listed items.

[0047] In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as there is no conflict.

[0048] The embodiments of the present disclosure aim to solve the problems of lack of real network communication environment, single vehicle model, and difficulty in user-defined interface in the functional test of the traditional digital twin traffic platform. Therefore, the embodiments of the present disclosure propose an intelligent networked traffic platform and a test method suitable for digital twin. The platform designs a simulation method with ROS mobile robots and 5G-V2X vehicle-mounted devices as the core. This method overcomes the above problems while autonomously simulating the interactive behavior of vehicles in real traffic scenarios, and is similar to the dynamics model of real vehicles, making the test results more realistic.

[0049] Please refer to Figure 1 , Figure 1 The framework diagram of the intelligent networked traffic platform suitable for digital twin is shown. The platform includes an operating system, a ROS mobile robot sand table, an information processing module, and an interface of the tested digital twin platform.

[0050] Specifically, the operating system includes a PC end of the ROS operating environment, which is used to connect the initialization of the ROS mobile robot and the V2X device, edit the autonomous navigation function of the ROS mobile robot, and feed back the device running state, etc. through Wifi, so as to ensure that the operator can fully master the platform state and timely adjust and optimize it to enhance the coordinated operation of autonomous navigation and traffic devices.

[0051] The autonomous navigation function of the ROS mobile robot refers to the process in which the robot autonomously perceives the environment, plans the path, and moves to the target position without relying on human intervention. In this invention, multiple robots are configured in a master-slave mode, the navigation points of the slave robots are allocated by the master, and the target point navigation process of multiple robots can autonomously realize overtaking, lane changing, and intersection scenarios, providing vehicle driving data for digital twin platform testing.

[0052] The device running state feedback refers to the real-time running feedback of the robots and traffic lights in the ROS mobile robot sand table to the operator through the rviz and xshell tools on the PC end, such as the real-time feedback of the robot's travel position, travel route, and traffic light running data.

[0053] The ROS mobile robot sand table integrates the ROS robot system and 5G-CV2X technology to build a vehicle model with networked communication environment, and supports the switching of vehicle models and specifications such as cars, trucks, and buses. With the participation of this model, the test results of the digital twin platform are more realistic.

[0054] Further, the support for switching vehicle models and specifications of cars, trucks, buses, etc. is achieved by adjusting the footprint_model, min_obstacle_dist, inflation_layer parameters in the TEB local planner and path planning cost map during autonomous navigation, for example, according to the selected vehicle size, the motion characteristics of the vehicle model in autonomous navigation can be changed to achieve different navigation and obstacle avoidance effects, thereby simulating the driving characteristics of different vehicle models, such as Figure 6 As shown in the figure. The figure shows the lane changing effect of the robot when simulating small, medium and large vehicles. As the vehicle model becomes larger, the initial safety distance and the lane changing duration become significantly larger. After the BSM process is processed, the motion of the real vehicle can be basically consistent.

[0055] The ROS mobile robot is one of the core hardware of the entire simulation platform. The vehicle model mainly includes: ROS robot, data conversion module, switch, 5G-CV2X vehicle equipment, and flat panel display. The simulation vehicle system architecture with networked communication environment is as shown in Figure 2 As shown in the figure. Among them:

[0056] The ROS robot selects Raspberry Pi 4B as the industrial computer running the Ubuntu system, and the radar and STM32 bottom driver board are connected to the host computer through the USB interface. Through the radar, the surrounding environment information is perceived, the map is constructed, the positioning and navigation are performed, and the autonomous navigation is realized to complete the scene required by the digital twin platform.

[0057] The data conversion module is used to map the ROS robot space coordinate system to the simulated vehicle BSM information, and then connect with the 5G-CV2X vehicle equipment through TCP communication. The working steps of the data conversion module are as follows:

[0058] Step one: four-element number to radian system;

[0059] In the ROS robot system, quaternion is usually used to represent different attitudes of the robot. According to the robot four-element number data format, the conversion to radian system formula is: Since the direction angle range of the robot is ( ), the value range of arctan and arcsin is ( ), which cannot be fully satisfied, so the arctan in (1) is converted to atan2. In the formula, roll, pitch and yaw are the rotation angles of the robot around the X, Y and Z directions, respectively representing the roll angle, pitch angle and heading angle. is the four-element number describing the attitude information of the robot.

[0060] Step two: direction angle mapping;

[0061] Since the ROS mobile robot only moves in the horizontal plane, there is only a yaw angle of rotation around the Z axis. The conversion process needs to consider the initial orientation of the robot. For example, taking the initial orientation of the robot as north, the coordinate mapping relationship is as shown in Figure 3 .

[0062] Convert the yaw angle in formula (2) to the angle: According to the corresponding relationship of the four directions in Figure 4 , formula (3) is brought into four conversion relationships: In the formula, , , , , respectively, represent the mapping relationship between the positive direction in the robot coordinate system and the real world east, west, south, and north.

[0063] Step three: coordinate translation;

[0064] In the ROS system, the position positioning and autonomous navigation of the robot depend on the map (static global coordinate system) coordinate system. However, in the automotive industry, the vehicle position is usually described using the latitude and longitude coordinate system, and the GNSS latitude and longitude data type is positive. Its data effective bit is generally accurate to 6 decimal places. Therefore, the conversion relationship between different coordinate systems needs to be derived, and the two coordinate translation relationships are as shown in Figure 4 .

[0065] The coordinate mapping needs to consider the real shape of the earth. For position information that is not too far apart, the terrain influence is ignored. Simplify the analysis of the relationship between the two coordinate systems to derive the formula as follows:

[0066] According to the Haversine formula, the distance formula between two latitude and longitude points is: , in the formula, Δ ϕ is the difference in latitude, Δλ is the difference in longitude, ϕ 1 and ϕ 2 are the latitudes of the two points, and R is the radius of the earth.

[0067] Suppose there are two points on the earth's plane, A point latitude and longitude coordinates (𝜆1,𝜑1), B point latitude and longitude coordinates (𝜆2,𝜑2). Assume that when A and B are at the same latitude: Therefore, when the difference in longitude Δλ is 0.00001°, the distance d between points A and B is approximately equal to 1 m. Similarly, the distance between two points under other conditions can be calculated according to the angle difference. According to the current longitude range of the region, which is approximately between 117°53'~118°52', and the latitude range between 31°24'~32°02'. Thus, the corresponding relationship is obtained according to the calculation method in formula (5), as shown in Table 1. Table 1. Latitude and longitude calculation relationship table

[0068] Classification 1 2 3 4 Difference in longitude 0.00001 0.0001 0.001 0.01 Difference in distance (approx. m) when latitude is equal 1 10 100 1000 Difference in distance (approx. m) when longitude is equal 1.1 11 111 1113

[0069] Ignoring the influence of regional differences on the change of latitude and longitude: is the converted latitude and longitude, is the conversion ratio fine-tuning parameter, K is the selected conversion ratio, is the initial latitude and longitude, is the offset of the robot.

[0070] According to the conversion relationship in formula (4) and formula (5) in the above steps, the mutual conversion method of the two coordinate systems is divided into rotation amount and offset, and the mutual conversion method of the coordinate systems is obtained by combining the two.

[0071] 5G-V2X vehicle-mounted equipment as a key unit in the whole architecture is installed on the ROS mobile robot, and together with the robot and the tablet computer forms a complete vehicle model. The information such as the position, speed and direction angle of the robot in the map enters the data conversion processing module, and the BSM data of the actual driving car in the actual road is simulated. The vehicle-mounted equipment receives the above simulated BSM data using TCP / IP, and displays it in real time on the map displayed on the vehicle-mounted display. The physical structure of the vehicle model is shown in Figure 5 .

[0072] The information processing module has two functions of receiving upload, and the two functions use multi-thread programming mode and do not interfere with each other. The upload function extracts key technical parameters related to vehicle motion and phase control, such as vehicle position information, vehicle speed, traffic light phase, phase duration, etc., from the simulated BSM information and traffic light information sent by different simulation vehicle models in the ROS mobile robot sand table. The data will be sent to the measured digital twin platform through 5G communication for driving the digital twin platform twin model. The receiving function realizes the reception of feedback information of the digital twin platform, such as the phase information of the traffic light optimized by the digital twin traffic platform for the user, and the best driving suggestion matched for the user, which is fed back to the vehicle-mounted tablet display, as shown in Figure 7 . By using the receiving function, the core concept of the digital twin traffic platform "from reality to virtual, and from virtual to reality" is truly realized. ​

[0073] The measured digital twin platform interface, according to market research, the network communication protocols commonly used in the field of digital twin platform at present include TCP / IP, WebSocket, MQTT and other network communication protocols. Therefore, in order to better achieve the compatibility of different digital twin platforms, the above-mentioned communication protocols are fully supported in the uploading and receiving process. At the same time, in order to better ensure the real-time performance of the digital twin platform to the real mapping, the data transmission of the digital twin platform is further improved by using 5G communication and cJSON data format in the application.

[0074] Based on the above platform, the application further provides a test method, comprising the following steps:

[0075] S1: first start ROS mobile robot and vehicle-mounted 5G-CV2X equipment, and check whether the communication between them is normal, to provide a reliable network environment for testing.

[0076] S2, start the robot autonomous navigation function and information processing module in the system operation platform, load the scene target point information for navigation. At the same time, the measured digital twin platform interface uses 5G communication to establish network communication with the digital twin platform, which is used to test the subsequent model driving, optimization algorithm and other functions in the digital twin platform.

[0077] S3, through the multi-robot automatic navigation function, according to the pre-set target point autonomous navigation, autonomous realization of simulation vehicle lane changing, green wave passing and other V2X test scene, information processing module receives the simulated vehicle BSM data in real time, and sends to the measured digital twin platform interface.

[0078] S4, the measured digital twin platform interface outputs the simulated data in real time, such as vehicle driving data and traffic light phase data, and simultaneously receives the feedback optimization information from the measured digital twin platform.

[0079] S5, before the end of the robot autonomous test, the user can manually intervene in the system operation platform, and take over the control right in case of sudden situation or too much deviation from the actual expectation.

[0080] The above is only the preferred embodiment of the application, the protection scope of the application is not limited to the above-mentioned embodiments, any technical scheme belonging to the idea of the application is also within the protection scope of the application. It should be noted that for ordinary skilled in the art, some improvements and decorations without departing from the principle of the application, these improvements and decorations should also be considered as the protection scope of the application.

Claims

1. An intelligent connected vehicle platform suitable for digital twinning, characterized in that, The application relates to a ROS mobile robot sand table system. The operation system comprises a PC terminal of a ROS operation environment, is used for connecting the initialization of a ROS mobile robot and V2X equipment through Wifi, editing the autonomous navigation function of the ROS mobile robot and feeding back the running state of the equipment, and the operation system comprises the following components: The ROS mobile robot sand table is fused with a ROS mobile robot system and 5G-CV2X technology, a vehicle model with a networked communication environment is constructed, and the vehicle model supports the switching of truck and bus models and specifications. The information processing module is used for data information uploading and receiving, and a multi-thread programming mode is adopted. The measured digital twin platform interface is used for data information transmission. The switching process of the vehicle model supporting the switching of truck and bus models and specifications is that footprint_model (robot footprint model), min_obstacle_dist (minimum distance between robot center and obstacle) and inflation_layer (inflation layer) parameters in a TEB local planner and a path planning cost map in an autonomous navigation process are adjusted. The vehicle model comprises a ROS robot, a data conversion module, a switch, 5G-CV2X vehicle-mounted equipment and a flat display.

2. The intelligent connected vehicle platform for digital twinning of claim 1, wherein, The ROS robot perceives surrounding environment information through a radar, carries out map construction, positioning and navigation, realizes autonomous navigation and completes a required scene of a digital twin platform; 3. The intelligent connected vehicle platform for digital twinning of claim 2, wherein, The data conversion module is used for mapping a ROS robot space coordinate system into simulated vehicle BSM information, and then realizing connection with 5G-CV2X vehicle-mounted equipment through TCP communication; The 5G-CV2X vehicle-mounted equipment receives the simulated vehicle BSM information through TCP / IP and displays the information in real time on a map of a vehicle-mounted flat display. The working steps of the data conversion module are four-element number conversion into radian system, direction angle mapping and coordinate translation.

4. The intelligent connected vehicle platform for digital twinning of claim 2, wherein, The direction angle mapping process is that the ROS mobile robot only moves on a horizontal plane, so only a heading angle yaw around a Z axis exists; 5. The intelligent connected vehicle platform for digital twinning of claim 4, wherein, The process of converting quaternion to radian system is as follows: in ROS robot system, quaternions are usually used to represent different poses of the robot. According to the data format of the robot quaternion, the conversion formula to radian system is: (1) Since the range of the direction angle of the robot is ( ), the range of arctan and arcsin is ( ), which cannot be satisfied in full, so the arctan in formula (1) is converted to atan2; (2) where roll, pitch, and yaw are the rotation angles of the robot about the X, Y, and Z directions, representing the roll angle, pitch angle, and yaw angle, respectively; A four-element number that describes the pose information of a robot.

6. The intelligent connected vehicle platform for digital twinning according to claim 4, wherein, The conversion process needs to consider the initial orientation of the robot, and the initial orientation of the robot is north; According to the corresponding relationship of the four directions, formula (3) is brought into four conversion relationships: The heading angle yaw in equation (2) is converted to an angle degree: (3) The coordinate translation process is that coordinate mapping needs to consider the real shape of the earth and ignore the influence of the terrain; the relationship between two coordinate systems is simplified for formula derivation as follows: （4） In the formula, , , , , respectively represent the mapping relationship between the positive direction in the robot coordinate system and the four directions of east, west, north and south in the real world.

7. The intelligent connected vehicle platform for digital twinning of claim 4, wherein, According to a distance formula of two longitude and latitude points based on a secant formula: The application further relates to a ROS mobile robot sand table system test method. （5） wherein is the difference in latitude, Δλ is the difference in longitude, and are the latitudes of the two points, R is the radius of the earth; let there be two points on the plane of the earth, A with longitude and latitude coordinates , and B with longitude and latitude coordinates ; when A and B are at the same latitude: , so that when the difference in longitude Δλ is 0.00001°, the distance d between the two points A and B is approximately equal to 1 m.

8. A testing method based on the intelligent connected vehicle platform for digital twinning according to any one of claims 1-7, characterized in that, S1: first, start a ROS mobile robot and vehicle-mounted 5G-CV2X equipment, and check whether communication between the two is normal, so as to provide a reliable networked environment for testing; S2: start the robot autonomous navigation function and the information processing module in the system operation platform, load point information of a scene target point used for navigation; meanwhile, the measured digital twin platform interface establishes network communication with a digital twin platform through 5G communication, and is used for testing model driving and optimization algorithm in the subsequent digital twin platform. ​ S3, through the multi-robot automatic navigation function, according to the target point set in advance, the V2X test scene of autonomous realization of lane changing and green wave passing of the simulated vehicle is realized, the information processing module receives the simulated vehicle BSM data in real time and sends it to the measured digital twin platform interface; S4, the measured digital twin platform interface outputs the simulated data in real time, including vehicle driving data and traffic light phase data, and simultaneously receives feedback optimization information from the measured digital twin platform in real time; S5, before the end of the robot autonomous test, the user can manually intervene in the system operation platform, and can take over the control right for the sudden situation or deviation from the actual expectation.

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