CN116266428A - Vehicle-road coordination testing method, vehicle-road coordination testing device, vehicle-road coordination testing system, and computer-readable storage medium - Google Patents

Abstract

The present disclosure relates to a vehicle-road cooperative test method, a vehicle-road cooperative test apparatus, a vehicle-road cooperative test system, and a computer-readable storage medium, capable of rapidly expanding test cases at low cost. The testing method comprises the following steps: constructing a 3D twin scene based on a high-precision map generated according to traffic environment data acquired at a closed test site, and outputting twin point cloud data, twin scene image data and simulated traffic control information; real-time enhancement is carried out on real-time point cloud data obtained in real time through a road side/vehicle end laser radar sensor by utilizing twin point cloud data, and enhanced point cloud data are generated; image real-time enhancement is carried out on measured image data obtained in real time through a road side/vehicle end image obtaining unit by utilizing twin scene image data, and enhanced image data is generated; and transmitting the enhanced point cloud data, the enhanced image data, and the simulated traffic control information to the test vehicle via the V2X communication system.

Description

Vehicle-road coordination test method, vehicle-road coordination test device, vehicle-road coordination test system, and and computer readable storage media

technical field

The present disclosure generally relates to the field of emerging information technology (vehicle-road coordination), and more specifically relates to a vehicle-road coordination testing method, a vehicle-road coordination testing system, and a computer-readable storage medium.

Background technique

In recent years, with the development of Internet of Vehicles technology, concepts such as smart cars, driverless cars, and self-driving cars have increasingly entered people's field of vision. Before all smart vehicles are officially launched on the road, they must undergo rigorous testing and evaluation. At this stage, the autonomous driving algorithm based on single-vehicle intelligence can be fully iteratively tested in the twin 3D scene, and the test restoration effect is acceptable, but currently affected by factors such as network delay and signal coverage, combined with the vehicle-road coordination of roadside communication equipment There is still room for improvement in testing in software simulation scenarios.

On the other hand, the vehicle-road co-simulation test based on real road conditions can meet the requirements of V2X application testing, but there are problems such as high hardware construction costs, poor configurability, difficulty in restoring test cases, and limited number of covered cases.

Contents of the invention

A brief overview of the present disclosure is given below in order to provide a basic understanding of some aspects of the present disclosure. It should be understood, however, that this summary is not an exhaustive summary of the disclosure. It is not intended to identify key or critical parts of the disclosure, nor is it intended to limit the scope of the disclosure. Its purpose is merely to present some concepts of the disclosure in a simplified form as a prelude to the more detailed description that is presented later.

An object of the present disclosure is to provide a vehicle-road collaborative testing method, a vehicle-road collaborative testing system, and a non-volatile computer-readable storage medium capable of quickly expanding test cases at low cost.

According to one aspect of the present disclosure, a vehicle-road collaborative testing method is provided, including: a twinning simulation step of building a 3D twinning scene based on a high-precision map generated from traffic environment data collected in a closed test site, in the 3D Integrate the simulated camera sensor, the simulated laser radar module, and the simulated traffic signal module in the twin scene, and output twin point cloud data, twin scene image data and simulated traffic control information; the real-time enhancement step of point cloud data uses the twin point cloud data to pass The measured point cloud data obtained by the roadside/vehicle lidar sensor in real time is enhanced in real time to generate enhanced point cloud data; the image real-time enhancement step is to use the twin scene image data to pass through the roadside/vehicle image acquisition unit in real time. The obtained measured image data is enhanced in real time to generate enhanced image data; and the sending step is to send the enhanced point cloud data, the enhanced image data and the simulated traffic control information to the test vehicle via the V2X communication system.

According to another aspect of the present disclosure, there is provided a vehicle-road collaborative testing device, including: a twin simulation unit, which builds a 3D twin scene based on a high-precision map generated from traffic environment data collected in a closed test site, in which The 3D twin scene integrates a simulated camera sensor, a simulated laser radar module, and a simulated traffic signal module, and outputs twin point cloud data, twin scene image data, and simulated traffic control information; a point cloud data real-time enhancement unit uses the twin point cloud The data is enhanced in real time to the measured point cloud data obtained by the roadside/vehicle lidar sensor in real time, and the enhanced point cloud data is generated; the image real-time enhancement unit uses the twin scene image data to compare the image passing through the roadside/vehicle end The measured image data acquired by the acquisition unit in real time is enhanced in real time to generate enhanced image data; and the sending unit sends the enhanced point cloud data, the enhanced image data and the simulated traffic control information to Test vehicle.

According to another aspect of the present disclosure, there is provided a vehicle-road collaborative testing system, including: a closed test site; A V2X communication system of a closed test site; and a test vehicle, which controls the vehicle according to the enhanced point cloud data, the enhanced image data and the simulated traffic received from the vehicle-road coordination test device via the V2X communication system information, and perform test tasks in the closed test site.

According to yet another aspect of the present disclosure, there is provided a computer-readable storage medium comprising computer-executable instructions that, when executed by one or more processors, cause the one or more processors to Execute the vehicle-road coordination testing method according to the above aspects of the present disclosure.

Description of drawings

The accompanying drawings, which constitute a part of this specification, illustrate the embodiments of the disclosure and together with the description serve to explain the principles of the disclosure.

The present disclosure can be more clearly understood from the following detailed description with reference to the accompanying drawings, in which:

FIG. 1 is a schematic diagram schematically showing the composition of a vehicle-road collaborative testing system 100 according to an embodiment of the present disclosure;

FIG. 2 is a flowchart schematically showing a vehicle-road coordination testing method according to an embodiment of the present disclosure;

Fig. 3 is a schematic diagram schematically showing the visualization of the lidar sensor point cloud;

Fig. 4 is a schematic diagram schematically showing the process of real-time image enhancement in the vehicle-road collaborative testing method according to an embodiment of the present disclosure;

FIG. 5 is a block diagram schematically showing an exemplary configuration of a vehicle-road coordination testing device according to an embodiment of the present disclosure;

FIG. 6 schematically illustrates an exemplary configuration of a computing device in which embodiments according to the present disclosure may be implemented.

Detailed ways

The following detailed description is made with reference to the accompanying drawings, and is provided to assist in a comprehensive understanding of various example embodiments of the present disclosure. The following description includes various details to aid in understanding, but these are to be regarded as examples only and not for the purpose of limiting the disclosure, which is defined by the appended claims and their equivalents. Words and phrases used in the following description are used only to enable a clear and consistent understanding of the present disclosure. In addition, descriptions of well-known structures, functions, and configurations may be omitted for clarity and conciseness. Those of ordinary skill in the art will recognize that various changes and modifications of the examples described herein can be made without departing from the spirit and scope of the disclosure.

Hereinafter, an exemplary embodiment of the vehicle-road collaborative testing technology of the present disclosure will be described in detail with reference to the accompanying drawings.

FIG. 1 schematically shows a schematic diagram of an example of the composition of a vehicle-road coordination testing system 100 according to an embodiment of the present disclosure. In FIG. 1 , a schematically shown vehicle-road coordination test system 100 includes: a closed test site, at least one roadside RSU (road side unit) set in the closed test site, a V2X communication system, and a test vehicle, etc. . In addition, although not shown in Fig. 1, the actual test vehicle is equipped with an OBU (On Board Unit) terminal, and the test vehicle receives data from the roadside RSU through the OBU terminal through the V2X communication system, and executes the test task. In Fig. 1, the roadside RSU is installed on the light pole of the traffic signal light, but it should be understood that, according to needs, the roadside RSU can be installed and arranged in any position of the closed test site in various ways.

The closed test site provides a real hardware-in-the-loop environment for vehicle-road collaborative testing, mainly including static environment elements, dynamic traffic elements and roadside unit elements. The so-called static elements are elements whose state in the closed test site does not change with time, such as road guide lines, road signs, street lamps, buildings, greenery, etc. The so-called dynamic traffic elements are elements with dynamic characteristics that participate in traffic behavior, such as pedestrians, vehicles, and other various moving bodies. The so-called roadside unit elements include cameras, lidar, millimeter wave radar, V2X communication equipment, etc. In addition, closed test sites are used to reproduce various actual road environments, and sometimes climate and weather elements need to be considered. It should be noted that, in FIG. 1 , for ease of understanding, only elements involved in the test method of the present disclosure are shown and other elements not involved are omitted.

V2X (Vehicle to X) communication system is the key technology of future intelligent transportation system. It enables communication between vehicles, vehicles and base stations, and base stations. In order to obtain a series of traffic information such as real-time road conditions, road information, and pedestrian information, so as to improve driving safety, reduce congestion, improve traffic efficiency, and provide in-vehicle entertainment information. As the key equipment for intelligent upgrade of roads in V2X scenarios, 4G/5G base stations, RSUs and OBUs are used for data communication between vehicles and roads, and transmit various information to vehicles and cloud servers to ensure traffic lights, traffic signs, parking Massive information such as location and vehicle status can be transmitted in a timely manner to realize applications such as vehicle speed guidance, speed limit warning, and congestion reminder in V2I scenarios, and provide services for assisted driving and automatic driving.

Fig. 2 schematically shows a flowchart of a vehicle-road coordination testing method according to an embodiment of the present disclosure. The vehicle-infrastructure system testing method shown in FIG. 2 can be executed by, for example, a V2X communication device (vehicle-infrastructure collaborative test device) as a roadside RSU, but it should be understood that all or part of these processes can also be performed by the V2X The communication device cooperates with the cloud server to complete.

Before the test starts, build a closed test site, V2X communication system and simulation environment according to the test content, use the collection vehicle to conduct multiple rounds of field collection in the built closed test site, based on the collected traffic environment data, combined with semantic annotation Information and road information drawn by road modeling tools to build a twin high-precision map of the closed test site.

After the high-precision map of the closed test site is generated, the vehicle-road collaborative testing method of the embodiment of the present disclosure shown in FIG. 2 starts. In step S1001, based on the generated high-precision twin map, a 3D twin scene is constructed using virtual engine technology. In this disclosure, in addition to converting the real hardware-in-the-loop environment into a 3D twin scene based on high-precision maps and virtual engine technology, an integrated simulation camera sensor, a simulated lidar module, and a simulated traffic simulation are also designed in the generated 3D twin scene. Signal module, and output twin scene images, twin point cloud data, and simulated traffic control information (for example, traffic lights, road signs, front fault warning, etc.), which are used to expand the traffic information of the real site and derive various test cases.

Then, in step S1002, the twin point cloud data is used to enhance the measured point cloud data acquired in real time by the roadside/vehicle lidar sensor in real time to generate enhanced point cloud data.

The lidar sensor detects the target by emitting a laser beam, and forms a point cloud by collecting the beam reflected by the target object to obtain point cloud data, which can be processed into an accurate three-dimensional image. Fig. 3 schematically shows a visualized schematic diagram of a laser radar sensor point cloud, which mainly describes whether there is an obstacle blocking ahead.

In the present disclosure, the enhanced point cloud data is generated by superimposing the 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 simulation lidar sensor in the twin scene output in the above step S1001 and the measured radar sensor point cloud collected in real time by the roadside/vehicle lidar sensor After the data matrix X ₂ , the first coordinate system is performed, and then the measured radar sensor point cloud data matrix X ₂ and the twin point cloud data matrix X ₁ are combined.

As a specific embodiment, the disclosure simplifies the point cloud data matrix, and represents the point cloud occlusion data in a 3*3 matrix, where "0" indicates non-occlusion, and "1" indicates occlusion.

孪生点云遮挡数据为

则并集处理得到的增强点云遮挡数据为：For example, suppose the occlusion point cloud obtained in real time in the real environment is

The twin point cloud occlusion data is

Then the enhanced point cloud occlusion data obtained by the union processing is:

Returning to the description of FIG. 2 , in step S1003 , use the twin scene image data output in step S1001 to perform real-time image enhancement on the measured image data acquired by the roadside/vehicle image acquisition unit in real time to generate enhanced image data.

In some embodiments, the twin scene image data includes, for example, rival vehicles, pedestrians or other moving objects and their trajectory information. As shown on the left side of Figure 4, first construct models of vehicles, pedestrians, and other moving objects based on 3D modeling software (such as 3Dmax). Then train the target detection algorithm, by marking the twin position and attitude information of vehicles, pedestrians and other moving objects in the simulated twin world, and train the obstacle target detection model on the twin road based on deep learning technologies such as yoloV3 and R-CNN. Then, as shown in the middle part of Figure 4, the real video stream is output in real time by the camera mounted on the car side/road side, and the twin position and attitude information is extracted through the target detection algorithm for the case scene simulated by the twin scene, and the information of the twin scene is extracted using the texture fusion method. Methods The twin scene image data and the measured image data are superimposed to generate enhanced image data.

It should be noted that the execution order of step S1002 and step S1003 can be exchanged, that is, the image enhancement processing is executed first, and then the point cloud data enhancement processing is executed, or the image enhancement processing and the point cloud data enhancement processing can be executed in parallel.

After the enhanced point cloud data and enhanced image data are generated, in step S1004, the enhanced point cloud data, enhanced image data and the 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 the closed test site based on the received enhanced point cloud data, enhanced image data and simulated traffic control information to complete the test.

It should be noted that, in order to avoid conflicts with traffic control information in the actual closed test site, such as control signals of traffic lights, the traffic control device in the actual test site was turned off during the actual vehicle test of the present disclosure. It should be understood that if the real traffic control device in the closed test site is turned on during the test, an arbitration mechanism for avoiding conflicts between the simulated traffic control signal and the real control signal needs to be added accordingly.

In some embodiments, the V2X communication device can directly send the enhanced point cloud data, enhanced image data, and simulated traffic control information to the test vehicle through wireless communication, or the V2X communication device can also send the enhanced point cloud data to the test vehicle via a 4G/5G base station. , Enhanced image data and simulated traffic control information are uploaded to the cloud server, and then sent to the test vehicle by the cloud server.

In the above, as an example, the entire processing of the vehicle-road collaborative test performed by the V2X communication device as a roadside RSU is described as an example. In some implementation examples, part of the processing performed by the above-mentioned V2X communication device may also be performed by the cloud server to execute. For example, the collected data of the closed test site can also be obtained by the cloud server, and a twin high-precision map can be constructed through semantic annotation. Furthermore, in some embodiments, a cloud server can also be used to build a 3D twin road network, and integrate a simulated camera sensor, a simulated laser radar module, and a simulated traffic signal module. In addition, in some embodiments, the V2X communication device can also upload the measured point cloud data obtained in real time through the roadside/vehicle lidar sensor and the measured image data obtained in real time through the roadside/vehicle image acquisition unit. Go to the cloud server, and the cloud server completes steps S1002 and S1003.

According to the vehicle-road collaborative testing method of the present disclosure, the combination of real site and virtual scene can be used to quickly expand test cases and improve the safety and reliability of the test system. Moreover, compared with the software twin simulation, all the case scenarios in this disclosure are based on the real vehicle-road collaborative network environment test, which is more loyal to the vehicle networking case scenario affected by factors such as network delay and signal coverage, and the test support is better.

In addition, according to the public vehicle-road collaborative test method, the simulation based on point cloud simulation and traffic twinning is more configurable and the cost of building hardware is lower. The virtual-real test method proposed in this disclosure can reduce the hardware cost of building various typical cases of traffic facilities, and at the same time, because the dynamic scene elements are all configured in the software simulation, it has the advantages of simple operation, flexibility and change.

Furthermore, according to the public vehicle-road collaborative test method, the simulated traffic control signals and vehicle warning messages of the twin simulation are integrated with the real test site, which greatly enriches the traffic control test cases and further reduces the hardware cost.

Fig. 5 schematically shows an exemplary configuration block diagram of a vehicle-road coordination testing device according to an embodiment of the present disclosure.

In some embodiments, the vehicle-road coordination testing device 2000 may include a processing circuit 2010. The processing circuit 2010 of the vehicle-road coordination testing device 2000 provides various functions of the vehicle-road coordination testing device 2000 . In some embodiments, the processing circuit 2010 of the vehicle-road coordination testing device 2000 may be configured to execute the vehicle-road coordination testing method in the vehicle-road coordination testing device 2000 .

Processing circuitry 2010 may refer to various implementations of digital circuitry, analog circuitry, or mixed-signal (combination of analog and digital) circuitry that performs functions in a computing system.

Processing circuitry may include, for example, circuits such as integrated circuits (ICs), application specific integrated circuits (ASICs), portions or circuits of individual processor cores, entire processor cores, individual processors, such as field programmable gate arrays (FPGAs), programmable hardware devices, and/or systems including multiple processors.

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 sending unit 2050 . Wherein, the twin simulation unit 2020 is configured to execute step S1001 in the flowchart of FIG. 2 , the point cloud data real-time enhancement unit 2030 is configured to execute step S1002 in the flowchart of FIG. In step S1003 in the flowchart of FIG. 2 , the sending unit 2050 is configured to execute step S1004 in the flowchart of FIG. 2 .

In some embodiments, the vehicle-road coordination testing device 2000 may further include a memory (not shown). The memory of the vehicle-road coordination testing device 2000 may store information generated by the processing circuit 2010 as well as programs and data for the operation of the vehicle-road coordination testing device 2000 . The memory can be volatile memory and/or non-volatile memory. For example, 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.

In addition, the vehicle-road coordination testing device 2000 may be implemented at the chip level, or may be implemented at the device level by including other external components.

It should be understood that the above-mentioned twin simulation unit 2020, point cloud data real-time enhancement unit 2030, image real-time enhancement unit 2040, and sending unit 2050 are only logical modules divided according to the specific functions they realize, and are not used to limit specific implementation methods . In actual implementation, each of the above units may be implemented as an independent physical entity, or may also be implemented by a single entity (for example, a processor (CPU or DSP, etc.), an integrated circuit, etc.).

FIG. 6 shows an exemplary configuration of a computing device 1200 capable of implementing embodiments according to the present disclosure.

Computing device 1200 is an example of a hardware device to which the above-described aspects of the present disclosure can be applied. Computing device 1200 may be any machine configured to perform processing and/or computation. Computing device 1200 may be, but is not limited to, a workstation, server, desktop computer, laptop computer, tablet computer, personal data assistant (PDA), smart phone, vehicle-mounted computer, or combinations thereof.

As shown in FIG. 6 , computing device 1200 may include one or more elements that may be connected to or communicate with bus 1202 via one or more interfaces. The bus 2102 may include, but is not limited to, an Industry Standard Architecture (Industry Standard Architecture, ISA) bus, a Micro Channel Architecture (Micro Channel Architecture, MCA) bus, an Enhanced ISA (EISA) bus, a Video Electronics Standards Association (VESA) local bus, and a peripheral bus. Set Component Interconnect (PCI) bus and so on. 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 . Processor(s) 1204 may be any kind of processor, and may include, but is not limited to, one or more general purpose processors or special purpose processors (such as dedicated processing chips). For example, the processor 1202 may correspond to the processing circuit 2010 in FIG. 5 , and is configured to realize the function of the vehicle-road coordination testing device. Input device 1206 may be any type of input device capable of entering information into a computing device, and may include, but is not limited to, a mouse, keyboard, touch screen, microphone, and/or remote controller. Output devices 1208 may be any type of device capable of presenting information, and may include, but are not limited to, displays, speakers, image/audio output terminals, vibrators, and/or printers.

The computing device 1200 may also include or be connected to a non-transitory storage device 1214, which may be any storage device that is non-transitory and capable of data storage, and may include, but is not limited to, a disk drive, optical storage device, solid state memory, floppy disk, flexible disk, hard disk, magnetic tape or any other magnetic medium, compact disk or any other optical medium, cache memory and/or any other memory chip or module from which data can be read by a computer , instructions and/or code in any other medium. Computing device 1200 may also include random access memory (RAM) 1210 and read only memory (ROM) 1212 . The ROM 1212 may store programs, utilities, or processes to be executed in a non-volatile manner. RAM 1210 may provide volatile data storage and store instructions related to the operation of computing device 1200 . Computing device 1200 may also include network/bus interface 1216 coupled to data link 1218 . Network/bus interface 1216 may 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 ^BluetoothTM devices, 802.11 devices, WiFi devices, WiMax devices, cellular communication facilities, etc.).

The present disclosure can be implemented as any combination of apparatuses, systems, integrated circuits and computer programs on a non-transitory computer readable medium. One or more processors may be implemented as an integrated circuit (IC), application specific integrated circuit (ASIC), or large scale integrated circuit (LSI), system LSI, super LSI, or super LSI that performs some or all of the functions described in this disclosure components.

The present disclosure includes the use of software, applications, computer programs or algorithms. Software, applications, 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 figures. For example, one or more memories store software or algorithms as executable instructions, and one or more processors can be associated with a set of instructions that execute the software or algorithms to provide various functions according to the embodiments described in this disclosure.

Software and computer programs (also called programs, software applications, applications, components, or code) include machine instructions for programmable processors and can be written in high-level procedural languages, object-oriented programming languages, functional programming languages , logic programming language or assembly language or machine language. The term "computer-readable medium" means any computer program product, means or device for providing machine instructions or data to a programmable data processor, such as a magnetic disk, optical disk, solid state storage device, memory and programmable logic device (PLD) , including a computer-readable medium for receiving machine instructions as computer-readable signals.

By way of example, a computer readable medium may include dynamic random access memory (DRAM), random access memory (RAM), read only memory (ROM), electrically erasable read only memory (EEPROM), compact disk read only memory ( CD-ROM) or other optical disk storage devices, magnetic disk storage devices or other magnetic storage devices, or can be used to carry or store required computer-readable program code in the form of instructions or data structures and can be read by general or special purpose computers or general-purpose or any other medium accessed by a dedicated processor. Disk or disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disc, and Blu-ray disc, where disks usually reproduce data magnetically, while discs reproduce data via laser Data is copied optically. Combinations of the above should also be included within the scope of computer-readable media.

The subject matter of the present disclosure is provided as examples of apparatuses, systems, methods and programs for implementing the features described in the present disclosure. However, other features or variations are contemplated in addition to the features described above. It is contemplated that implementations of the components and functions of the present disclosure may be accomplished with any emerging technology that may replace any of the above-described implementations.

Additionally, the above description provides examples, and does not limit the scope, applicability or configuration set forth in the claims. Changes may be made in the function and arrangement of elements discussed without departing from the spirit and scope of the disclosure. Various embodiments may omit, substitute, or add various procedures or components as appropriate. For example, features described with respect to certain embodiments may be combined in other embodiments.

In addition, in the description of the present disclosure, the terms "first", "second", "third" and the like are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance and order.

Similarly, while operations are depicted in the figures in a particular order, this should not be construed as requiring that such operations be performed in the particular order shown, or in sequential order, or that all illustrated operations be performed to achieve the desired the result of. In certain situations, multitasking and parallel processing can be advantageous.

Claims (12)

1. A vehicle-road cooperative test method comprises the following steps:

a twinning simulation step, namely constructing a 3D twinning scene based on a high-precision map generated according to traffic environment data acquired at a closed test site, integrating a simulation camera sensor, a simulation laser radar module and a simulation traffic signal module in the 3D twinning scene, and outputting twinning point cloud data, twinning scene image data and simulation traffic control information;

a point cloud data real-time enhancement step, namely utilizing the twin point cloud data to enhance real-time point cloud data obtained in real time through a road side/vehicle end laser radar sensor in real time, and generating enhanced point cloud data;

an image real-time enhancement step, namely performing image real-time enhancement on actual measurement image data acquired in real time through a road side/vehicle side image acquisition unit by utilizing the twin scene image data to generate enhanced image data; and

and a transmission step of transmitting the enhanced point cloud data, the enhanced image data, and the simulated traffic control information to a test vehicle via a V2X communication system.

2. The vehicle-road cooperative test method according to claim 1, wherein,

in the point cloud data real-time enhancement step, based on the high-precision map and the 3D twin scene, performing union computation on a point cloud data matrix of the twin point cloud data and a point cloud data matrix of the real point cloud data, and generating the enhanced point cloud data.

3. The vehicle-road cooperative test method according to claim 1, wherein,

in the image real-time enhancement step, based on the high-precision map and the 3D twin scene, overlapping the twin scene image data and the actually measured image data to generate the enhanced image data.

4. The vehicle-road cooperative test method according to claim 1, wherein,

in the image real-time enhancement step:

as the twin scene image data, at least motion trail information on at least one of a oncoming vehicle, a pedestrian, and other vehicles is designed,

extracting twin position and attitude information of the test vehicle through target detection processing, acquiring actual measurement image data acquired in real time through the road side/vehicle end image acquisition unit,

and superposing the twin scene image data and the measured image data through reinforcement learning and map fusion processing to generate the enhanced image data.

5. The vehicle-road cooperative test method according to claim 1, wherein,

the simulated traffic control information includes information related to at least any one of a traffic signal lamp, a road sign, and a front fault early warning.

6. A vehicle-road cooperative test device, comprising:

a twin simulation unit for constructing a 3D twin scene based on a high-precision map generated according to traffic environment data acquired at a closed test site, integrating a simulation camera sensor, a simulation laser radar module and a simulation traffic signal module in the 3D twin scene, and outputting twin point cloud data, twin scene image data and simulation traffic control information;

the point cloud data real-time enhancement unit is used for enhancing real-time point cloud data obtained in real time through the road side/vehicle end laser radar sensor by utilizing the twin point cloud data to generate enhanced point cloud data;

the image real-time enhancement unit is used for carrying out image real-time enhancement on the actually measured image data obtained in real time through the road side/vehicle end image obtaining unit by utilizing the twin scene image data to generate enhanced image data; and

and a transmission unit that transmits the enhanced point cloud data, the enhanced image data, and the simulated traffic control information to a test vehicle via a V2X communication system.

7. The vehicle course cooperative test device according to claim 6, wherein,

the point cloud data real-time enhancement unit performs union calculation on the point cloud data matrix of the twin point cloud data and the point cloud data matrix of the real point cloud data based on the high-precision map and the 3D twin scene, and generates the enhanced point cloud data.

8. The vehicle course cooperative test device according to claim 6, wherein,

the image real-time enhancement unit performs superposition processing on the twin scene image data and the actually measured image data based on the high-precision map and the 3D twin scene to generate the enhanced image data.

9. The vehicle course cooperative test device according to claim 6, wherein,

in the image real-time enhancement unit:

as the twin scene image data, at least motion trail information on at least one of a oncoming vehicle, a pedestrian, and other vehicles is designed,

extracting twin position and attitude information of the test vehicle through target detection processing, acquiring actual measurement image data acquired in real time through the road side/vehicle end image acquisition unit,

and superposing the twin scene image data and the measured image data through reinforcement learning and map fusion processing to generate the enhanced image data.

10. The vehicle course cooperative test device according to claim 1, wherein,

the simulated traffic control information includes information related to at least any one of a traffic signal lamp, a road sign, and a front fault early warning.

11. A vehicle-road cooperative test system, comprising:

a closed test field;

at least one vehicle road co-testing device according to any one of claims 6 to 10 arranged at the closed test site;

the V2X communication system is arranged on the closed test site; and

the test vehicle performs a test task in the enclosed test site according to the enhanced point cloud data, the enhanced image data, and the simulated traffic control information received from the road cooperative test device via the V2X communication system.

12. A computer-readable storage medium comprising computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform the vehicle road co-testing method of any of claims 1-5.

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