Vehicle-road cooperation test method, vehicle-road cooperation test device, vehicle-road cooperation test system and computer readable storage medium

Abstract

The present disclosure relates to a vehicle-road cooperation test method, a vehicle-road cooperation test device, a vehicle-road cooperation test system and a computer readable storage medium, which can quickly expand test cases at low cost. The test method comprises: building a 3D twin scene based on a high-precision map generated according to traffic environment data collected in a closed test site, and outputting twin point cloud data, twin scene image data and simulated traffic control information; using the twin point cloud data to perform real-time enhancement on measured point cloud data obtained in real time by a roadside / vehicle end laser radar sensor, to generate enhanced point cloud data; using the twin scene image data to perform image real-time enhancement on measured image data obtained in real time by a roadside / vehicle end image acquisition unit, to generate enhanced image data; and sending the enhanced point cloud data, the enhanced image data and the simulated traffic control information to a test vehicle via a V2X communication system.

Description

TECHNICAL FIELD

[0001] The present disclosure relates generally to the field of emerging information technology (Car-Infrastructure Cooperation), and more particularly, to a Car-Infrastructure Cooperation testing method, a Car-Infrastructure Cooperation testing system, and a computer readable storage medium. BACKGROUND

[0002] In recent years, with the development of Internet of Vehicles technology, concepts such as intelligent vehicles, unmanned vehicles, and autonomous vehicles have entered the public view more and more. Before all intelligent vehicles officially go on the road, they must be strictly tested and evaluated. At the present stage, autonomous driving algorithms based on single-vehicle intelligence can be fully iteratively tested in a twin 3D scene, and the test restoration effect is acceptable, but at present, affected by factors such as network delay and signal coverage, Car-Infrastructure Cooperation testing combined with roadside communication equipment still has room for improvement in the software simulation scene.

[0003] On the other hand, Car-Infrastructure Cooperation simulation testing based on real road conditions can meet the testing needs of V2X applications, but there are problems such as high hardware construction cost, poor configurability, difficulty in restoring test cases, limited number of covered cases, and the like. SUMMARY

[0004] A brief summary of the present disclosure is presented in the following to provide a basic understanding of some aspects of the present disclosure. However, it should be understood that this summary is not an extensive overview of the present disclosure. It is not intended to identify key or critical elements of the present disclosure or to delineate the scope of the present disclosure. Its sole purpose is to present some concepts of the present disclosure in a simplified form as a prelude to the more detailed description presented later.

[0005] It is an object of the present disclosure to provide a Car-Infrastructure Cooperation testing method, a Car-Infrastructure Cooperation testing system, and a non-volatile computer readable storage medium that can quickly expand test cases at low cost.

[0006] According to one aspect of the present disclosure, a vehicle-road cooperation test method is provided, comprising: a twin simulation step of building a 3D twin scene based on a high-precision map generated according to traffic environment data collected at a closed test site, integrating a simulated camera sensor and a simulated laser radar module, a simulated traffic signal module in the 3D twin scene, and outputting twin point cloud data, twin scene image data, and simulated traffic control information; a point cloud data real-time enhancement step of using the twin point cloud data to perform real-time enhancement on measured point cloud data obtained in real time by a roadside / vehicle-end laser radar sensor, and generating enhanced point cloud data; an image real-time enhancement step of using the twin scene image data to perform image real-time enhancement on measured image data obtained in real time by a roadside / vehicle-end image acquisition unit, and generating enhanced image data; and a sending step of sending the enhanced point cloud data, the enhanced image data, and the simulated traffic control information to a test vehicle via a V2X communication system.

[0007] According to another aspect of the present disclosure, a vehicle-road cooperation test device is provided, comprising: a twin simulation unit that builds a 3D twin scene based on a high-precision map generated according to traffic environment data collected at a closed test site, integrates a simulated camera sensor and a simulated laser radar module, a simulated traffic signal module in the 3D twin scene, and outputs twin point cloud data, twin scene image data, and simulated traffic control information; a point cloud data real-time enhancement unit that uses the twin point cloud data to perform real-time enhancement on measured point cloud data obtained in real time by a roadside / vehicle-end laser radar sensor, and generates enhanced point cloud data; an image real-time enhancement unit that uses the twin scene image data to perform image real-time enhancement on measured image data obtained in real time by a roadside / vehicle-end image acquisition unit, and generates enhanced image data; and a sending unit that sends the enhanced point cloud data, the enhanced image data, and the simulated traffic control information to a test vehicle via a V2X communication system.

[0008] According to still another aspect of the present disclosure, a vehicle-road cooperation test system is provided, comprising: a closed test site; the vehicle-road cooperation test device of the above-mentioned another aspect arranged at the closed test site; a V2X communication system arranged at the closed test site; and a test vehicle that performs a test task in the closed test site according to the enhanced point cloud data, the enhanced image data, and the simulated traffic control information received from the vehicle-road cooperation test device via the V2X communication system.

[0009] According to still another aspect of the present disclosure, a computer-readable storage medium is provided, comprising computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform the vehicle-road cooperation test method according to the above-mentioned aspects of the present disclosure. Attached Figure Description

[0010] The accompanying drawings, which form part of this specification, illustrate embodiments of this disclosure and, together with the specification, serve to explain the principles of this disclosure.

[0011] This disclosure will be more clearly understood with reference to the accompanying drawings and the following detailed description, in which:

[0012] Figure 1 This is a schematic diagram illustrating the configuration of a vehicle-road cooperative testing system 100 according to an embodiment of the present disclosure;

[0013] Figure 2 This is a flowchart schematically illustrating a vehicle-road cooperative testing method according to an embodiment of the present disclosure;

[0014] Figure 3 This is a schematic diagram illustrating the visualization of the point cloud of a lidar sensor;

[0015] Figure 4 This is a schematic diagram illustrating the real-time image enhancement process in the vehicle-road cooperative testing method according to an embodiment of the present disclosure;

[0016] Figure 5 This is a schematic block diagram illustrating an exemplary configuration of a vehicle-road cooperative testing apparatus according to an embodiment of the present disclosure;

[0017] Figure 6 An exemplary configuration of a computing device that can implement embodiments of the present disclosure is illustrated schematically. Detailed Implementation

[0018] The following detailed description is based on the accompanying drawings and provides various exemplary embodiments of the present disclosure to aid in a comprehensive understanding. Various details are included in the following description to aid understanding; however, these details are considered exemplary only and not intended to limit the present disclosure, which is defined by the appended claims and their equivalents. The words and phrases used in the following description are intended only to provide a clear and consistent understanding of the present disclosure. Additionally, descriptions of well-known structures, functions, and configurations may have been omitted for clarity and brevity. Those skilled in the art will recognize that various changes and modifications can be made to the examples described herein without departing from the spirit and scope of the present disclosure.

[0019] The following describes in detail, with reference to the accompanying drawings, exemplary embodiments of the vehicle-road cooperative testing technology of this disclosure.

[0020] Figure 1 This is a schematic diagram illustrating an example of the configuration of a vehicle-road cooperative testing system 100 according to an embodiment of the present disclosure. Figure 1The vehicle-road cooperative testing system 100, schematically shown in the diagram, includes: a closed testing area, at least one roadside unit (RSU) installed in the closed testing area, a V2X communication system, and test vehicles, etc. Additionally, Figure 1 Although not shown in the diagram, the actual test vehicle was equipped with an OBU (On Board Unit) terminal. The test vehicle received data from the roadside RSU via the V2X communication system through the OBU terminal to perform test tasks. Figure 1 In this context, roadside RSUs are installed on traffic signal poles, but it should be understood that, as needed, roadside RSUs can be installed in any location within a closed test area in various ways.

[0021] The closed test track provides a realistic hardware-in-the-loop environment for vehicle-road cooperative testing, mainly including static environmental elements, dynamic traffic elements, and roadside unit elements. Static elements are those whose state within the closed test track does not change over time, such as road guide lines, road signs, streetlights, buildings, and greenery. Dynamic traffic elements are elements with dynamic characteristics involved in traffic behavior, such as pedestrians, vehicles, and other moving bodies. Roadside unit elements include cameras, LiDAR, millimeter-wave radar, and V2X communication equipment. Furthermore, the closed test track is used to reproduce various real-world road environments, and sometimes weather factors also need to be considered. It should be noted that... Figure 1 For ease of understanding, only the elements involved in the test method of this disclosure are shown, while other elements not involved are omitted.

[0022] V2X (Vehicle-to-X) communication systems are a key technology for future intelligent transportation systems. They enable communication between vehicles, between vehicles and base stations, and between base stations. This allows for the acquisition of real-time traffic conditions, road information, pedestrian information, and other traffic data, thereby improving driving safety, reducing congestion, increasing traffic efficiency, and providing in-vehicle entertainment information. As crucial equipment for intelligent road upgrades in V2X scenarios, 4G / 5G base stations, RSUs (Roadside Units), and OBUs (On-Board Units) are used for data communication between vehicles and the road, transmitting various information to vehicles and cloud servers. This ensures the timely transmission of massive amounts of information such as traffic lights, traffic signs, parking locations, and vehicle status, enabling applications such as speed guidance, speed limit warnings, and congestion alerts in V2I (Vehicle-to-Infrastructure) scenarios, and providing services for assisted driving and autonomous driving.

[0023] Figure 2 A flowchart illustrating a vehicle-road cooperative testing method according to an embodiment of the present disclosure is shown. Figure 2The vehicle-road system testing methods shown can be performed, for example, by a V2X communication device (vehicle-road cooperative testing device) that is a type of roadside RSU. However, it should be understood that all or part of these processes can of course be completed by the V2X communication device in collaboration with a cloud server.

[0024] Before the test begins, a closed test site, V2X communication system, and simulation environment are set up according to the test content. Multiple rounds of on-site data collection are carried out in the set-up closed test site using data collection vehicles. Based on the collected traffic environment data, combined with semantic annotation information and road information drawn by road modeling tools, a twin high-precision map of the closed test site is constructed.

[0025] After generating a high-precision map of the closed test site, begin Figure 2 The vehicle-road cooperative testing method of this disclosure is shown in embodiment S1001. In step S1001, a 3D twin scene is constructed using virtual engine technology based on the generated twin high-precision map. In this disclosure, in addition to converting the real hardware-in-the-loop environment into a 3D twin scene based on the high-precision map and virtual engine technology, the generated 3D twin scene also integrates simulated camera sensors, simulated LiDAR modules, and simulated traffic signal modules. It outputs twin scene images, twin point cloud data, and simulated traffic control information (e.g., traffic lights, road signs, forward fault warnings, etc.) to expand the real-world traffic information and generate various test cases.

[0026] Then, in step S1002, the measured point cloud data obtained in real time by the roadside / vehicle-side lidar sensor is enhanced in real time using twin point cloud data to generate enhanced point cloud data.

[0027] LiDAR sensors detect targets by emitting laser beams and collect the beams reflected back by the targets to form point clouds, thus acquiring point cloud data. This point cloud data can be processed to generate accurate three-dimensional images. Figure 3 The diagram illustrates a visualization of the point cloud of a lidar sensor, primarily depicting whether there are obstacles obstructing the view ahead.

[0028] In this disclosure, enhanced point cloud data is generated by overlaying point cloud occlusion data of the real scene and the twin simulation scene. In some embodiments, after obtaining the twin point cloud data matrix X1 of the simulated LiDAR sensor in the twin scene output in step S1001 above, and the measured LiDAR sensor point cloud data matrix X2 collected in real time by the roadside / vehicle-side LiDAR sensor, coordinate system one is established, and then the measured LiDAR sensor point cloud data matrix X2 and the twin point cloud data matrix X1 are subjected to union processing.

[0029] As a specific embodiment, this disclosure simplifies the point cloud data matrix, using a 3*3 matrix to represent point cloud occlusion data, where "0" represents non-occlusion and "1" represents occlusion.

[0030] For example, let the occlusion point cloud acquired in real time in the real environment be... Twin point cloud occlusion data The enhanced point cloud occlusion data obtained by union processing is:

[0031]

[0032] return Figure 2 As explained in step S1003, the twin scene image data output in step S1001 is used to perform real-time image enhancement on the measured image data obtained in real time through the roadside / vehicle-end image acquisition unit, thereby generating enhanced image data.

[0033] In some embodiments, twin scene image data may include information about opposing vehicles, pedestrians, or other moving objects, and their trajectories. Figure 4 As shown on the left, firstly, models of vehicles, pedestrians, and other moving objects are constructed using 3D modeling software (such as 3ds Max). Then, an object detection algorithm is trained by labeling the twin positions and poses of vehicles, pedestrians, and other moving objects in the simulated twin world, and by training an obstacle detection model on the twin road using deep learning techniques such as YOLOv3 and R-CNN. Then, as... Figure 4 As shown in the middle, real video streams are output in real time by cameras mounted on the side of the vehicle / roadside. The position and posture information of the twin is extracted by the target detection algorithm based on the case scene of the twin scene simulation. The twin scene image data and the measured image data are superimposed using the texture fusion method to generate enhanced image data.

[0034] It should be noted that the execution order of steps S1002 and S1003 can be interchanged, i.e., image enhancement processing is performed first and point cloud data enhancement processing is performed later, or image enhancement processing and point cloud data enhancement processing can be performed in parallel.

[0035] After generating enhanced point cloud data and enhanced image data, in step S1004, the enhanced point cloud data, enhanced image data, and simulated traffic control information output in step S1001 are sent to the test vehicle via the V2X communication system. Then, the test vehicle drives in a closed test area based on the received enhanced point cloud data, enhanced image data, and simulated traffic control information to complete the test.

[0036] It should be noted that, to avoid conflicts with traffic control signals such as traffic lights in actual closed test tracks, the traffic control devices in the actual test tracks were turned off during the real-vehicle testing process disclosed in this disclosure. It should be understood that if the actual traffic control devices in the closed test tracks were turned on during the test, an arbitration mechanism would be needed to prevent conflicts between simulated traffic control signals and actual control signals.

[0037] In some embodiments, the V2X communication device can directly send enhanced point cloud data, enhanced image data, and simulated traffic control information to the test vehicle via wireless communication. Alternatively, the V2X communication device can also upload enhanced point cloud data, enhanced image data, and simulated traffic control information to a cloud server via a 4G / 5G base station, and then the cloud server can send them to the test vehicle.

[0038] The above description, as an example, illustrates the entire process of vehicle-to-infrastructure (V2I) testing performed by a V2X communication device acting as a roadside RSU. In some embodiments, a portion of the processing performed by the V2X communication device can also be performed by a cloud server. For example, the cloud server can acquire data collected from a closed test site and construct a twin high-precision map through semantic annotation. Furthermore, in some embodiments, the cloud server can build a 3D twin road network and integrate simulated camera sensors, simulated LiDAR modules, and simulated traffic signal modules. Additionally, in some embodiments, the V2X communication device can upload the measured point cloud data acquired in real-time by the roadside / vehicle-side LiDAR sensor and the measured image data acquired in real-time by the roadside / vehicle-side image acquisition unit to the cloud server, whereby the cloud server completes steps S1002 and S1003.

[0039] According to the vehicle-road cooperative testing method disclosed herein, a combination of real-world and virtual scenarios can be used to rapidly expand test cases and improve the security and reliability of the testing system. Furthermore, compared to software twin simulation, all test scenarios in this disclosure are based on real vehicle-road cooperative network environments, more faithfully reflecting vehicle-to-everything (V2X) scenarios affected by factors such as network latency and signal coverage, thus providing superior test support.

[0040] Furthermore, according to publicly available vehicle-road cooperative testing methods, simulations based on point cloud simulation and traffic twins offer greater configurability and lower hardware costs. The virtual-real hybrid testing method proposed in this disclosure can reduce the hardware costs of building traffic facilities for various typical cases. Simultaneously, since dynamic scene elements are configured within the software simulation, it offers advantages such as ease of operation and flexibility.

[0041] Furthermore, based on publicly available vehicle-road cooperative testing methods, the simulated traffic control signals and vehicle warning messages from twin simulations were integrated with the real test site, greatly enriching the traffic control test cases and further reducing hardware costs.

[0042] Figure 5 An exemplary configuration block diagram of a vehicle-road cooperative testing apparatus according to an embodiment of the present disclosure is shown schematically.

[0043] In some embodiments, the vehicle-to-infrastructure (V2I) testing apparatus 2000 may include processing circuitry 2010. The processing circuitry 2010 of the V2I testing apparatus 2000 provides various functions of the V2I testing apparatus 2000. In some embodiments, the processing circuitry 2010 of the V2I testing apparatus 2000 may be configured to execute a V2I testing method within the V2I testing apparatus 2000.

[0044] Processing circuitry 2010 can refer to various implementations of digital, analog, or mixed-signal (a combination of analog and digital) circuit systems that perform functions in a computing system.

[0045] Processing circuitry may include, for example, circuitry such as integrated circuits (ICs), application-specific integrated circuits (ASICs), portions or circuitry of a single processor core, an entire processor core, a single processor, programmable hardware devices such as field-programmable gate arrays (FPGAs), and / or systems comprising multiple processors.

[0046] In some embodiments, the processing circuit 2010 may include: a twin simulation unit 2020, a point cloud data real-time enhancement unit 2030, an image real-time enhancement unit 2040, and a transmission unit 2050. The twin simulation unit 2020 is configured to perform... Figure 2 In step S1001 of the flowchart, the point cloud data real-time enhancement unit 2030 is configured to execute Figure 2 In step S1002 of the flowchart, the real-time image enhancement unit 2040 is configured to perform... Figure 2 In step S1003 of the flowchart, the sending unit 2050 is configured to perform... Figure 2 Step S1004 in the flowchart.

[0047] In some embodiments, the vehicle-to-infrastructure (V2I) testing device 2000 may further include a memory (not shown). The memory of the V2I testing device 2000 may store information generated by the processing circuitry 2010, as well as programs and data for the operation of the V2I testing device 2000. The memory may be volatile memory and / or non-volatile memory. For example, the memory may include, but is not limited to, random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), read-only memory (ROM), and flash memory.

[0048] In addition, the vehicle-road cooperative testing device 2000 can be implemented at the chip level, or it can be implemented at the device level by including other external components.

[0049] It should be understood that the aforementioned twin simulation unit 2020, point cloud data real-time enhancement unit 2030, image real-time enhancement unit 2040, and transmission unit 2050 are merely logical modules divided according to their specific functions, and are not intended to limit the specific implementation method. In actual implementation, each of the above units can be implemented as an independent physical entity, or it can be implemented by a single entity (e.g., a processor (CPU or DSP, etc.), integrated circuit, etc.).

[0050] Figure 6 An exemplary configuration of a computing device 1200 capable of implementing embodiments of the present disclosure is shown.

[0051] Computing device 1200 is an example of a hardware device capable of applying the above aspects of this disclosure. Computing device 1200 can be any machine configured to perform processing and / or computation. Computing device 1200 can be, but is not limited to, a workstation, server, desktop computer, laptop computer, tablet computer, personal data assistant (PDA), smartphone, in-vehicle computer, or a combination thereof.

[0052] like Figure 6As shown, computing device 1200 may include one or more components that can be connected to or communicate with bus 1202 via one or more interfaces. Bus 2102 may include, but is not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Electronics for Imaging Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus. Computing device 1200 may include, for example, one or more processors 1204, one or more input devices 1206, and one or more output devices 1208. The one or more processors 1204 may be any type of processor and may include, but is not limited to, one or more general-purpose processors or dedicated processors (such as dedicated processing chips). Processor 1202 may, for example, correspond to... Figure 5 The processing circuit 2010 is configured to implement the functions of the vehicle-road cooperative testing device. Input device 1206 can be any type of input device capable of inputting information to the computing device, and may include, but is not limited to, a mouse, keyboard, touchscreen, microphone, and / or remote controller. Output device 1208 can be any type of device capable of presenting information, and may include, but is not limited to, a monitor, speaker, image / audio output terminal, vibrator, and / or printer.

[0053] The computing device 1200 may also include or be connected to a non-transitory storage device 1214, which may be any non-transitory storage device capable of storing data, and may include, but is not limited to, disk drives, optical storage devices, solid-state storage, floppy disks, flexible disks, hard disks, magnetic tapes or any other magnetic media, compressed disks or any other optical media, cache memory and / or any other storage chip or module, and / or any other medium from which a computer may read data, instructions and / or code. The computing device 1200 may also include random access memory (RAM) 1210 and read-only memory (ROM) 1212. ROM 1212 may store executable programs, utilities, or processes in a non-volatile manner. RAM 1210 provides volatile data storage and stores instructions related to the operation of the computing device 1200. The computing device 1200 may also include a network / bus interface 1216 coupled to a data link 1218. Network / bus interface 1216 can be any kind of device or system capable of enabling communication with external devices and / or networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication devices and / or chipsets (such as Bluetooth). TM Equipment, 802.11 equipment, WiFi equipment, WiMax equipment, cellular communication facilities, etc.

[0054] This disclosure can be implemented as any combination of apparatus, system, integrated circuit, and computer program on a non-transitory computer-readable medium. One or more processors can be implemented as integrated circuits (ICs), application-specific integrated circuits (ASICs), or large-scale integrated circuits (LSIs), system LSIs, super LSIs, or ultra LSI components that perform some or all of the functions described in this disclosure.

[0055] This disclosure includes the use of software, application programs, computer programs, or algorithms. Software, application programs, computer programs, or algorithms may be stored on a non-transitory computer-readable medium to cause a computer, such as one or more processors, to perform the steps described above and in the accompanying drawings. For example, one or more memories may store the software or algorithm in executable instructions, and one or more processors may be associated with executing a set of instructions of the software or algorithm to provide various functionalities according to embodiments described in this disclosure.

[0056] Software and computer programs (also referred to as programs, software applications, applications, components, or code) include machine instructions for programmable processors and can be implemented in high-level procedural languages, object-oriented programming languages, functional programming languages, logic programming languages, assembly languages, or machine languages. The term "computer-readable medium" means any computer program product, apparatus, or device used to provide machine instructions or data to a programmable data processor, such as magnetic disks, optical disks, solid-state storage devices, memories, and programmable logic devices (PLDs), including computer-readable media that receive machine instructions as computer-readable signals.

[0057] For example, computer-readable media may include dynamic random access memory (DRAM), random access memory (RAM), read-only memory (ROM), electrically erasable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage devices, magnetic disk storage devices or other magnetic storage devices, or any other medium that can be used to carry or store required computer-readable program code in the form of instructions or data structures, and that can be accessed by a general-purpose or special-purpose computer or a general-purpose or special-purpose processor. As used herein, a disk or disc includes compact discs (CD), laser discs, optical discs, digital versatile discs (DVD), floppy disks, and Blu-ray discs, wherein a disk typically copies data magnetically, while a disc copies data optically using a laser. Combinations of the above are also included within the scope of computer-readable media.

[0058] The subject matter of this disclosure is provided as examples of apparatus, systems, methods, and programs for performing the features described herein. However, other features or variations are contemplated in addition to those described above. It is anticipated that the components and functions of this disclosure can be implemented using any emerging techniques that may replace any of the above-described implementations.

[0059] Furthermore, the above description provides examples and does not limit the scope, applicability, or configuration set forth in the claims. Changes may be made to the function and arrangement of the elements discussed without departing from the spirit and scope of this disclosure. Various processes or components may be appropriately omitted, substituted, or added in various embodiments. For example, features described with respect to certain embodiments may be combined in other embodiments.

[0060] Furthermore, in the description of this disclosure, the terms “first,” “second,” “third,” etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or order.

[0061] Similarly, although the operations are depicted in a specific order in the accompanying drawings, this should not be construed as requiring the operations to be performed in the specific order shown or in sequential order, or requiring the execution of all illustrated operations to achieve the desired result. In some cases, multitasking and parallel processing can be advantageous.

Claims

1. A vehicle-road cooperative testing method, comprising: The twin simulation step involves building a 3D twin scene based on a high-precision map generated from traffic environment data collected in a closed test site. The 3D twin scene integrates a simulated camera sensor, a simulated LiDAR module, and a simulated traffic signal module, and outputs twin point cloud data, twin scene image data, and simulated traffic control information. The twin scene image data includes motion trajectory information related to oncoming vehicles, pedestrians, and other vehicles. The real-time point cloud data enhancement step involves using the twin point cloud data to enhance the measured point cloud data acquired in real time by the roadside / vehicle-side lidar sensor, generating enhanced point cloud data. This includes performing a union calculation on the point cloud data matrix of the twin point cloud data and the point cloud data matrix of the measured point cloud data to generate the enhanced point cloud data. The real-time image enhancement step involves using the twin scene image data to perform real-time image enhancement on the measured image data acquired in real-time by the roadside / vehicle-side image acquisition unit, generating enhanced image data; and The transmission step involves sending the enhanced point cloud data, the enhanced image data, and the simulated traffic control information to the test vehicle via a V2X communication system.

2. The vehicle-road cooperative testing method according to claim 1, wherein, In the real-time image enhancement step, based on the high-precision map and the 3D twin scene, the twin scene image data and the measured image data are overlaid to generate the enhanced image data.

3. The vehicle-road cooperative testing method according to claim 1, wherein, In the real-time image enhancement step: The twin position and attitude information of the test vehicle are extracted through target detection processing, and the measured image data obtained in real time by the roadside / vehicle-side image acquisition unit is acquired. The enhanced image data is generated by overlaying the twin scene image data and the measured image data through reinforcement learning and texture fusion processing.

4. The vehicle-road cooperative testing method according to claim 1, wherein, The simulated traffic control information includes information related to at least one of traffic lights, road signs, and forward malfunction warnings.

5. A vehicle-road cooperative testing device, comprising: The twin simulation unit builds a 3D twin scene based on a high-precision map generated from traffic environment data collected in a closed test site. The 3D twin scene integrates a simulated camera sensor, a simulated lidar module, and a simulated traffic signal module, and outputs twin point cloud data, twin scene image data, and simulated traffic control information. The twin scene image data includes motion trajectory information related to oncoming vehicles, pedestrians, and other vehicles. The real-time point cloud data enhancement unit uses the twin point cloud data to enhance the measured point cloud data acquired in real time by the roadside / vehicle-side lidar sensor, generating enhanced point cloud data. This includes performing a union calculation on the point cloud data matrix of the twin point cloud data and the point cloud data matrix of the measured point cloud data to generate the enhanced point cloud data. A real-time image enhancement unit utilizes the twin scene image data to perform real-time image enhancement on the measured image data acquired in real-time by the roadside / vehicle-side image acquisition unit, generating enhanced image data; and The transmitting unit transmits the enhanced point cloud data, the enhanced image data, and the simulated traffic control information to the test vehicle via a V2X communication system.

6. The vehicle-road cooperative testing device according to claim 5, wherein, The real-time image enhancement unit, based on the high-precision map and the 3D twin scene, overlays the twin scene image data and the measured image data to generate the enhanced image data.

7. The vehicle-road cooperative testing device according to claim 5, wherein, In the real-time image enhancement unit: The twin position and attitude information of the test vehicle are extracted through target detection processing, and the measured image data obtained in real time by the roadside / vehicle-side image acquisition unit is acquired. The enhanced image data is generated by overlaying the twin scene image data and the measured image data through reinforcement learning and texture fusion processing.

8. The vehicle-road cooperative testing device according to claim 5, wherein, The simulated traffic control information includes information related to at least one of traffic lights, road signs, and forward malfunction warnings.

9. A vehicle-road cooperative testing system, comprising: A closed testing site; At least one vehicle-road cooperative testing device according to any one of claims 5 to 8, which is set in the closed testing site; A V2X communication system set up in the enclosed test site; and The test vehicle performs test tasks in the closed test site based on the enhanced point cloud data, the enhanced image data, and the simulated traffic control information received from the vehicle-road cooperative test device via the V2X communication system.

10. A computer-readable storage medium comprising computer-executable instructions, which, when executed by one or more processors, cause the one or more processors to perform the vehicle-road cooperative testing method according to any one of claims 1 to 4.

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