CN117806182A

CN117806182A - Vehicle-road cloud integrated joint simulation system, method, equipment and medium

Info

Publication number: CN117806182A
Application number: CN202311801246.4A
Authority: CN
Inventors: 李聪; 冀健; 黄阿琼; 程明; 张剑锋; 唐凤敏
Original assignee: Guoqi Beijing Intelligent Network Association Automotive Research Institute Co ltd
Current assignee: Guoqi Beijing Intelligent Network Association Automotive Research Institute Co ltd
Priority date: 2023-12-25
Filing date: 2023-12-25
Publication date: 2024-04-02

Abstract

The invention relates to the technical field of traffic simulation, and discloses a vehicle-road-cloud integrated joint simulation system, a method, equipment and a medium. The system comprises an application layer, an algorithm layer, a cloud control layer and a simulation layer configured with VISSIM simulation software. Generating road model data and scene model data; acquiring vehicle parameters and strategy parameters of a simulation layer in real time, generating a first control instruction and a second control instruction, and transmitting the first control instruction and the second control instruction to VISSIM simulation software of the simulation layer; and initializing a road cloud model based on the road model data, the scene model data, the preset initial vehicle parameters and the preset initial strategy parameters, executing the vehicle cloud model simulation test, updating the vehicle Lu Yun model in real time based on the first control instruction and the second control instruction, continuously executing the test, and performing accurate simulation based on the real road condition, so that the vehicle cloud model simulation test system is integrated into a collaborative joint simulation system, and the efficiency of the traffic simulation test is effectively improved.

Description

Vehicle-road cloud integrated joint simulation system, method, equipment and medium

Technical Field

The invention relates to the technical field of traffic simulation, in particular to a vehicle-road-cloud integrated joint simulation system, a method, equipment and a medium.

Background

Modern traffic systems are complex giant systems, and system components such as people, vehicles, roads, environments, information and the like are mutually related, highly coupled and open in boundary, so that a plurality of problems are difficult to analyze through on-site observation and theoretical modeling, but virtual simulation technology provides effective tools and means for analyzing and solving complex traffic problems. Road traffic systems are undergoing brand new transformation, show trends of intellectualization, networking and collaboration, and vehicle-road cloud integrated technology becomes a hotspot and a front edge of international intelligent traffic research and practice, and simulation test becomes an important means for technological attack in industry and academia.

At present, the software for carrying out traffic simulation at home and abroad is a lot of, however, a lot of defects still exist in meeting the requirements of automatic driving simulation, and the method is mainly characterized in the following aspects. First, there are two modes for the autopilot simulation technique, one is an integrated simulation and the other is a joint simulation. The integrated simulation means that huge enterprises use fund strength (self-grinding or parallel purchase) to break through an industrial chain, so as to form independent and comprehensive simulation capability as much as possible; the joint simulation is to closely cooperate among different simulation enterprises, and the simulation tasks are jointly realized through product butt joint. Because of the complexity of automobiles and traffic simulation, the requirements of traffic simulation under an automobile-road cloud integrated environment are difficult to directly meet by a simulation tool, algorithm generation and access, real-time traffic information extraction, vehicle speed path control and the like are difficult to realize, and models of different architectures are difficult to effectively cooperatively work or perform data exchange, so that information is difficult to share.

Disclosure of Invention

In view of the above, the invention provides a vehicle-road cloud integrated joint simulation system, a method, equipment and a medium, so as to solve the problem of how to improve the efficiency of traffic simulation test.

In a first aspect, an embodiment of the present invention provides a vehicle-road cloud integrated joint simulation system, where the system includes: an application layer, an algorithm layer, a cloud control layer and a simulation layer;

the application layer is used for generating road model data and scene model data based on an actual road and an actual scene, and importing the road model data and the scene model data into the simulation layer;

the algorithm layer comprises a plurality of automatic driving algorithms and is used for acquiring vehicle parameters of the simulation layer in real time based on the first interface; generating a first control instruction by using an automatic driving algorithm based on the vehicle parameters; transmitting the first control instruction to a simulation layer based on a first interface;

the cloud control layer comprises a plurality of traffic simulation strategies and is used for acquiring strategy parameters of the simulation layer in real time based on the second interface; generating a second control instruction by using a traffic simulation strategy based on the strategy parameters; transmitting the second control instruction to a simulation layer based on a second interface;

The simulation layer is used for carrying out initialization operation of the road cloud model based on the road model data, the scene model data, the preset initial vehicle parameters and the preset initial strategy parameters, and carrying out vehicle road cloud model simulation test based on the initialized vehicle road cloud model; updating the vehicle Lu Yun model in real time based on the first control instruction and the second control instruction in the vehicle road cloud model simulation test process, and continuously executing the vehicle road cloud model simulation test based on the updated vehicle road cloud model; transmitting vehicle parameters generated in real time in the vehicle road cloud simulation test process to an algorithm layer, and transmitting strategy parameters generated in real time in the vehicle road cloud simulation test to a cloud control layer; the vehicle parameter characterization simulation layer is used for representing the state parameters of each vehicle in the simulation test process; the strategy parameters represent control strategy parameters of traffic flow in the simulation test process of the simulation layer.

The system provided by the embodiment of the invention generates road model data and scene model data according to the actual road scene at the application layer, and introduces the road model data and the scene model data into VISSIM simulation software of the simulation layer, so that accurate simulation can be performed based on the actual road condition, and the actual traffic condition including road condition, vehicle position, speed and the like is reflected; the algorithm layer acquires vehicle parameters of the simulation layer in real time through the first interface, and generates a first control instruction by utilizing an automatic driving algorithm, so that an automatic driving vehicle can be allowed to make a real-time decision according to the actual traffic condition, and the road safety and the traffic efficiency are improved; the cloud control layer acquires strategy parameters of the simulation layer in real time through the second interface, generates a second control instruction by utilizing the traffic simulation strategy, and can monitor and adjust the control strategy of the traffic flow in real time and test different traffic strategies in the simulation, so that the traffic mobility is improved, and the congestion and traffic accidents are reduced; the simulation layer transmits the vehicle parameters generated in real time to the algorithm layer and transmits the strategy parameters generated in real time to the cloud control layer, so that the algorithm layer and the cloud control layer can feed back and adjust in real time according to the simulation result to optimize an automatic driving algorithm and a traffic simulation strategy, and the performance and the effect of the system are improved; the system provided by the invention can reduce the cost and risk of testing on an actual road by using the simulation layer simulation, and simultaneously allows large-scale testing under different conditions so as to accelerate the development of an automatic driving system.

In an alternative embodiment, the autopilot algorithm includes at least a sensing algorithm, a positioning and mapping algorithm, a path planning and decision algorithm, a control algorithm, a communication and networking algorithm; the traffic simulation strategy at least comprises a vehicle-road cooperation strategy, a safety traffic rule strategy, a communication working condition and scene strategy and a case series-parallel strategy.

According to the embodiment of the invention, through a sensing algorithm, an automatic driving system can accurately sense the surrounding environment, including identifying and tracking other vehicles, pedestrians, obstacles and the like, and the road safety can be improved; the positioning and map algorithm can accurately determine the position of the automatic driving vehicle, match the position with a map, accurately position and navigate, and ensure that the vehicle runs according to a preset path; the path planning and decision algorithm can generate an optimal driving path and decision strategy according to the current traffic situation and the current traffic situation, so that the traffic efficiency can be improved, and the congestion can be reduced; the control algorithm can accurately control actions such as acceleration, braking, steering and the like of the vehicle according to instructions generated by the path planning and decision algorithm, and can ensure the stable action of the vehicle; through communication and networking algorithms, the automatic driving vehicle can conduct real-time communication and data exchange with other vehicles, traffic infrastructures and cloud platforms, and communication interconnection between vehicles can be ensured. The vehicle-road cooperative strategy can improve traffic mobility and road utilization rate through information exchange and cooperation between vehicles and road infrastructure; the behavior of the automatic driving vehicle can be ensured to accord with the road traffic regulations based on the traffic regulations and the regulations through the safety traffic regulation policy; through the communication working conditions and the scene strategies, the simulation layer can simulate different traffic communication conditions, such as traffic flow, signal control, communication delay and the like; the simulation layer can simulate different traffic flow control strategies, such as vehicle series-parallel connection and the like through case series-parallel connection strategies.

In an alternative embodiment, the application layer includes: the point cloud acquisition unit is used for acquiring point cloud data of an actual road and an actual scene based on acquisition equipment; the model extraction unit is used for cutting and filtering the point cloud data to extract earth surface model information; the information labeling unit is used for labeling road element information, corresponding attributes and connection relations of the earth surface model information, labeling scene element information, corresponding attributes and connection relations of the earth surface model information, and converting the labeled earth surface model information into an application map when the vehicle is driven automatically; the model generating unit is used for importing the road element information, the corresponding attribute and the connection relation in the application map into a twin model, generating static road grids and traffic facility position information as road model data and generating a traffic facility entity structure as scene model data; and the data transmission unit is used for converting the road model data and the scene model data into readable formats and importing the road model data and the scene model data into the simulation layer.

According to the embodiment of the invention, the point cloud data of the actual road and the scene can be obtained through the point cloud acquisition unit and the model extraction unit, the geometric information of the surface model is extracted through preprocessing, and the attribute relationship of the road and the scene is respectively marked to obtain the marked application map; the road element information, the attributes and the connection relations in the application map are imported into the twin model, static road grids and traffic facility position information are generated to serve as road model data, a traffic facility entity structure is generated to serve as scene model data, and a vivid twin scene model can be constructed so that automatic driving performance test can be conducted in a simulation environment.

In an alternative embodiment, the algorithm layer includes: the vehicle parameter acquisition unit is used for acquiring vehicle parameters in real time based on the first interface; a vehicle instruction generation unit for generating a vehicle control instruction in real time based on an automatic driving algorithm and vehicle parameters; a vehicle command formatting unit, configured to package the vehicle control command into a first interface message transmission format as a first control command; and the vehicle instruction transmission unit is used for transmitting the first control instruction to the simulation layer through the first interface.

According to the embodiment of the invention, the vehicle parameters are acquired from VISSIM simulation software in real time through the vehicle parameter acquisition unit so as to generate vehicle control instructions, such as instructions of acceleration, steering angle and the like, and the behavior of the vehicle in a simulation environment is controlled; the generated vehicle control instructions are packaged into the first interface message transmission format, so that the instructions can be correctly transmitted in a standardized format, and the behavior of the automatic driving vehicle under different scenes can be effectively simulated.

In an alternative embodiment, the cloud control layer includes: the strategy parameter acquisition unit is used for acquiring strategy parameters in real time based on the second interface; the strategy parameter generation unit is used for generating strategy control instructions in real time based on the traffic simulation strategy and the strategy parameters; a policy parameter formatting unit, configured to package the policy control instruction into a second interface message transmission format as a second control instruction; and the strategy parameter transmission unit is used for transmitting the second control instruction to the simulation layer through a second interface.

The embodiment of the invention acquires strategy parameters from VISSIM simulation software in real time through a strategy parameter acquisition unit so as to generate strategy control instructions of traffic flow, such as instructions of phase and duration adjustment of traffic signals, a plurality of vehicle driving strategies and the like, and control the state of the traffic flow in a simulation environment; and the generated strategy control instructions of the traffic flows are packaged into the second interface message transmission format, so that the instructions can be correctly transmitted in a standardized format, and the states of different traffic flows are effectively simulated.

In an alternative embodiment, the simulation layer includes: the initialization unit is used for completing the initialization operation of the vehicle road cloud model based on the road model data, the scene model data, the preset initial vehicle parameters and the preset initial strategy parameters; the simulation execution unit is used for executing the vehicle road cloud model simulation test according to the initialized vehicle road cloud model; the simulation data generation unit is used for generating real-time vehicle behavior simulation data based on the first control instruction and real-time strategy simulation data based on the second control instruction; the simulation test updating unit is used for updating the model Lu Yun of the vehicle in real time based on the real-time vehicle behavior simulation data and the real-time strategy simulation data and continuously executing the simulation test of the vehicle road cloud model; the simulation data transmission unit is used for transmitting the vehicle parameters generated in the vehicle-road cloud simulation test process to the algorithm layer and transmitting the strategy parameters generated in the vehicle-road cloud simulation test process to the cloud control layer.

According to the embodiment of the invention, the road model data, the scene model data, the preset initial vehicle parameters and the preset initial strategy parameters are loaded through the initialization unit, so that a simulation environment is established, and preparation is made for a subsequent simulation test; the simulation execution unit simulates traffic flow and vehicle behavior in a real road environment; the simulation test updating unit can dynamically display the vehicle behavior and traffic flow in the simulation process and timely feed back the simulation result; the simulation data transmission unit transmits the generated strategy parameters to the cloud control layer and transmits the generated vehicle parameters to the algorithm layer so as to further optimize and update the cloud control layer or the algorithm layer, thereby effectively realizing the integrated closed-loop simulation test of the whole vehicle road cloud model and improving the simulation test efficiency.

In an alternative embodiment, the emulation layer further comprises: the simulation result feedback unit is used for obtaining a test result output after the simulation test of the Lu Yun model of the vehicle is finished; and the simulation evaluation unit is used for acquiring the comprehensive evaluation result of the vehicle Lu Yun model simulation test based on the test result and a preset evaluation index.

According to the embodiment of the invention, the simulation evaluation unit comprehensively evaluates the vehicle road cloud model simulation test based on the test result and the preset evaluation index by feeding back the test data through the simulation result feedback unit, so as to obtain the index and the evaluation result about the performance and the safety of the automatic driving system, comprehensively evaluate the performance of the automatic driving in the whole simulation system process, the realization capability and the execution effect of different strategies, and further provide decision-related information for the actual traffic strategies.

In an alternative embodiment, the emulation layer further comprises: and the simulation backtracking unit is used for acquiring a log backtracking file output after the simulation test of the vehicle Lu Yun model is finished, and the log backtracking file is used for backtracking the simulation test process of the vehicle road cloud model.

The simulation backtracking unit is used for acquiring a log backvisit file output after the simulation test of the vehicle Lu Yun model is finished by the VISSIM simulation software, and the file records detailed information in the simulation test process, such as vehicle behavior, traffic flow and the like; the problems in the simulation process can be solved through simulation backtracking, the problems of potential system faults, algorithm errors, strategy errors and the like are solved, and the reliability of automatic driving is further optimized.

In an optional implementation manner, the algorithm layer is further configured to generate an algorithm optimization requirement of an autopilot algorithm based on a preset algorithm performance index and a preset scene requirement, and transmit the algorithm optimization requirement to the cloud control layer; the cloud control layer is also used for generating algorithm optimization parameters based on the algorithm optimization requirements by utilizing a vehicle-road cooperation strategy, a safety traffic rule strategy, a communication working condition and a scene strategy, and transmitting the algorithm optimization parameters to the algorithm layer; the algorithm layer is also used for optimizing the automatic driving algorithm based on the algorithm optimization parameters.

According to the embodiment of the invention, the requirements to be optimized are determined in advance through the preset algorithm performance indexes and scene requirements, the cloud control layer generates algorithm optimization parameters by utilizing the vehicle-road cooperation strategy and the safety traffic rule strategy, the algorithm layer optimizes the automatic driving algorithm by utilizing the algorithm optimization parameters generated by the cloud control layer, and the performance and the calculation efficiency of the automatic driving algorithm are improved by adjusting the parameters of the algorithm and updating the logic and the strategy of the algorithm, so that the control level and the simulation efficiency of the whole simulation system are further improved.

In a second aspect, an embodiment of the present invention provides a vehicle-road cloud integrated joint simulation method, which is applied to the vehicle-road cloud integrated joint simulation system in the first aspect, where the method includes: generating road model data and scene model data based on an actual road and an actual scene, and importing the road model data and the scene model data into a simulation layer; acquiring vehicle parameters of the simulation layer in real time based on the first interface; generating a first control instruction by using an automatic driving algorithm based on the vehicle parameters; transmitting the first control instruction to a simulation layer based on a first interface; acquiring strategy parameters of the simulation layer in real time based on the second interface; generating a second control instruction by using a traffic simulation strategy based on the strategy parameters; transmitting the second control instruction to a simulation layer based on a second interface; carrying out initialization operation of a road cloud model based on the road model data, the scene model data, and preset initial vehicle parameters and initial strategy parameters, and executing a vehicle road cloud model simulation test based on the initialized vehicle road cloud model; updating the vehicle Lu Yun model in real time based on the first control instruction and the second control instruction in the vehicle road cloud model simulation test process, and continuously executing the vehicle road cloud model simulation test based on the updated vehicle road cloud model; and transmitting the vehicle parameters generated in real time in the vehicle-road cloud simulation test process to an algorithm layer, and transmitting the strategy parameters generated in real time in the vehicle-road cloud simulation test to a cloud control layer.

In a third aspect, an embodiment of the present invention provides a vehicle-road cloud integrated joint simulation device, including: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface are communicated with each other through the communication bus; the memory is configured to store at least one executable instruction, where the executable instruction causes the processor to perform operations of the vehicle-road cloud integrated joint simulation method according to the above embodiment.

In a fourth aspect, an embodiment of the present invention provides a computer readable storage medium, where at least one executable instruction is stored in the storage medium, where the executable instruction when executed on a vehicle-road cloud integrated joint simulation device/apparatus causes the vehicle-road cloud integrated joint simulation device/apparatus to perform an operation of the vehicle-road cloud integrated joint simulation method according to the foregoing embodiment.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.

FIG. 1 is a block diagram of a vehicle-road cloud integrated joint simulation system according to some embodiments of the present invention;

FIG. 2 is a block diagram of another vehicle-road cloud integrated joint simulation system in accordance with some embodiments of the present invention;

FIG. 3 is a block diagram of another vehicle-road cloud integrated joint simulation system in accordance with some embodiments of the present invention;

FIG. 4 is a block diagram of another vehicle-road cloud integrated joint simulation system in accordance with some embodiments of the present invention;

FIG. 5 is a block diagram of another vehicle-road cloud integrated joint simulation system in accordance with some embodiments of the present invention;

FIG. 6 is a flow diagram of a vehicle-road cloud integrated joint simulation method according to an embodiment of the present invention;

fig. 7 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present invention.

Detailed Description

Further advantages and effects of the present invention will become readily apparent to those skilled in the art from the disclosure herein, by referring to the accompanying drawings and the preferred embodiments. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be understood that the preferred embodiments are presented by way of illustration only and not by way of limitation.

The embodiment of the invention is suitable for traffic management or automatic driving scenes. The vehicle-road-cloud integrated joint simulation system can integrate VISSIM simulation software, digital twin technology, cloud control platform and automatic driving algorithm into a joint simulation system which works cooperatively, supports real-time data integration, digital twin modeling, system integration and real-time decision making, and can provide more accurate capability of traffic simulation and automatic driving system test; researchers, government agencies and vehicle manufacturers can be helped to understand the impact of autopilot technology on the overall traffic system, thereby developing better traffic management strategies, improving the design of autopilot systems to achieve safer, more efficient road traffic systems.

The simulation test in the prior art focuses on the test, optimization and verification of an automatic driving system, and does not relate to the simulation and influence of the whole traffic flow.

The embodiment of the invention provides a vehicle-road cloud integrated joint simulation system. FIG. 1 is a block diagram of a vehicle-road cloud integrated joint simulation system according to an embodiment of the present invention, as shown in FIG. 1, the system includes: an application layer 1, an algorithm layer 2, a cloud control layer 3 and a simulation layer 4 configured with VISSIM simulation software;

An application layer 1, configured to generate road model data and scene model data based on an actual road and an actual scene, and import the road model data and the scene model data into a simulation layer;

it should be noted that, in the embodiment of the present invention, the simulation layer is configured with a simulation tool, which can acquire various data transmitted from the application layer, the algorithm layer, and the cloud control layer, and perform a simulation operation. The road model data refers to the position shape of the road restored by the digital twin technology and the position information of traffic facilities, and simulation software used for transmitting the position information to a simulation layer builds a Lu Yun model of the vehicle; the scene model data is data used for representing the shapes and the traffic scene relations of road scene elements in the index twin technology, and is also used for establishing a road cloud model by simulation software transmitted to a simulation layer.

The simulation tool configured in the simulation layer in the embodiment of the invention is VISSIM simulation software; the VISSIM simulation software refers to a tool for microscopic traffic simulation, and the VISSIM can integrate various traffic modes, including cars, rail transit, buses, trucks, bicycles, mopeds, motorcycles, pedestrians and the like, and the vehicles are placed in the same tool for integrated simulation; providing a simulation environment conforming to the real-world traffic background by quantitatively describing the interactive relationship between vehicles and pedestrians; the VISSIM provides a following model, a lane changing model and a transverse overtaking behavior model, so that the driving behavior of the vehicle can be simulated, and a user can modify parameters through editing windows and input parameters such as distance and the like which accord with the local traffic driving condition, thereby accurately simulating the behavior of the vehicle; VISSIM also supports manual creation of a road network, which may also be imported from other PTV-series software (e.g., PTV VISUM and PTV visro). It also supports importing existing road network data via the OpenDRIVE format. The VISSIM can simulate the control mode of an intersection, including a speed limit sign, a yield sign, a stop sign, a speed reducing zone, an intersection signal control scheme, a variable information board and the like. Meanwhile, the VISSIM can simulate different types of vehicles and provide the setting of vehicle attributes. The VISSIM provides a large number of random factors, which can be reflected in the simulation operation, so that the simulation result is more real. In addition, the VISSIM also develops some randomly generated driving error phenomena for automatic driving research, such as a phenomenon that a driver misjudges traffic signs and signal control schemes, a phenomenon that a driver overspeeds, a phenomenon that the driver deviates from a central line during driving, a phenomenon that attention is not focused, and the like.

It will be appreciated that, although the VISSIM simulation software has wide application in the field of microscopic traffic simulation, it is often operated as a stand-alone tool, and if a large simulation test is to be implemented, a large number of manual operations are required or the VISSIM is developed separately, which easily results in waste of computing resources and redundancy of data, and it is highly likely that full automation operation cannot be implemented. In the embodiment of the invention, VISSIM software is configured in the simulation layer, but the VISSIM software is not an independent tool, and an algorithm layer, an application layer and a cloud control layer are also constructed, so that data interaction among a plurality of layers is realized, independent development of the VISSIM software is avoided, and the closed loop and automation of the whole system are ensured.

In some alternative embodiments, referring to fig. 2, the application layer 1 specifically includes:

the point cloud acquisition unit 11 is configured to acquire point cloud data of an actual road and an actual scene based on an acquisition device.

It should be noted that, the point cloud acquisition unit firstly uses acquisition equipment, such as a laser radar, a camera, a GPS and an inertial sensor, to perform multi-round road acquisition and measurement on the ground, and then acquires corresponding point cloud data based on acquisition and measurement information of actual roads and scenes.

The model extraction unit 12 is configured to label the road element information and the corresponding attribute and connection relation of the surface model information, label the scene element information and the corresponding attribute and connection relation of the surface model information, and convert the labeled surface model information into an application map when the vehicle is automatically driven.

The model extraction unit firstly performs cutting and filtering processing on the point cloud data to extract surface model information, where the surface model information refers to geometric shape and characteristic information of the surface extracted by the point cloud data, such as information of shape, height, inclination and the like of a road. In the embodiment, the cutting refers to dividing the point cloud data into smaller areas, so that the point cloud data can be better processed and analyzed; filtering refers to denoising through a filtering algorithm, so that the data quality is effectively improved.

The information labeling unit 13 is configured to label road element information and corresponding attributes and connection relationships for the surface model information, label scene element information and corresponding attributes and connection relationships for the surface model information, and convert the labeled surface model information into an application map for automatic driving of the vehicle.

The information labeling unit labels the surface model information, wherein the labeling is to associate specific information and attributes with the surface model for subsequent application and analysis. For example, the elements for marking the road include the position, shape, type, connection relation between the lane line and the road, etc. of the lane line; the elements of the scene include the position and type of the traffic signal lamp, the state of the signal lamp, the connection relationship between the signal lamp and the road, and the like.

The model generating unit 14 is configured to import road element information in the application map, and corresponding attributes and connection relations into the twin model, generate static road grids and traffic facility position information as road model data, and generate a traffic facility entity structure as scene model data.

The static road grid includes nodes and edges of the road, the nodes are intersection points, and the edges are sections or lanes of the road. The traffic facility location information indicates the location of traffic lights, road signs, signboards, etc. on the road, each of the traffic facilities having corresponding location coordinates, and the location of each of the coordinates in the static road grid. The twin model refers to a model generated by digital twin technology, is a digital representation of a real physical system, and is capable of simulating and reproducing the characteristics and behavior of physical entities in a virtual environment.

And a data transmission unit 15 for converting the road model data and the scene model data into readable formats and importing the same into a simulation layer.

It should be noted that, the road model data and the scene model data are converted into readable formats so that they can be properly parsed and used by the VISSIM software, and the readable formats may be file formats supported by the VISSIM, such as XML, CSV, and the like. The data is converted into a readable format through the data transmission unit, so that the accuracy and consistency of the data can be ensured, and the data can be conveniently imported and used in simulation software.

The algorithm layer 2 comprises a plurality of automatic driving algorithms and is used for acquiring vehicle parameters of the simulation layer in real time based on the first interface; generating a first control command based on vehicle parameters using an autopilot algorithm; the first control instruction is transmitted to the simulation layer based on the first interface.

In the embodiment of the invention, the automatic driving algorithm represents the brain of the automatic driving vehicle, so that the vehicle can sense the surrounding environment, make decisions and execute control actions so as to realize autonomous driving. The technical framework core of the automatic driving algorithm is divided into three parts of environment perception, decision planning and control execution. (1) an environment awareness class algorithm: the position and the state of the traffic participants in the vehicle and the surrounding environment are known through the sensing algorithm and the sensor to observe the environment, and the method mainly comprises a SLAM algorithm and an automatic driving sensing algorithm. (2) The decision planning algorithm is mainly used for guaranteeing safety by planning driving paths and other information through a decision algorithm and a computing platform after environment information is known, and comprises an automatic driving planning algorithm and an automatic driving decision algorithm. (3) The control execution type algorithm controls the vehicle to execute driving operation according to the planned path through the algorithm and the drive-by-wire system. In the embodiment of the invention, the automatic driving algorithm at least comprises a sensing algorithm, a positioning and map algorithm, a path planning and decision algorithm, a control algorithm, a communication and networking algorithm.

It can be appreciated that through the sensing algorithm, the automatic driving system can accurately sense the surrounding environment, including identifying and tracking other vehicles, pedestrians, obstacles and the like, and the road safety can be improved; the positioning and map algorithm can accurately determine the position of the automatic driving vehicle, match the position with a map, accurately position and navigate, and ensure that the vehicle runs according to a preset path; the path planning and decision algorithm can generate an optimal driving path and decision strategy according to the current traffic situation and the current traffic situation, so that the traffic efficiency can be improved, and the congestion can be reduced; the control algorithm can accurately control actions such as acceleration, braking, steering and the like of the vehicle according to instructions generated by the path planning and decision algorithm, and can ensure the stable action of the vehicle; through communication and networking algorithms, the automatic driving vehicle can conduct real-time communication and data exchange with other vehicles, traffic infrastructures and cloud platforms, and communication interconnection between vehicles can be ensured.

For example, the vehicle parameter information may include a position, a speed, a direction, etc. of the vehicle. For example, through data exchange with an interface of the simulation layer, the automatic driving algorithm can acquire information such as the position, the speed and the like of the vehicle in the current simulation environment; the automatic driving algorithm utilizes the acquired vehicle parameters, and combines a sensing algorithm, a positioning and map algorithm, a path planning and decision algorithm and the like to generate a first control instruction. The first control instruction includes a control operation for running of the vehicle, such as acceleration, deceleration, steering, and the like; the generated first control instruction is transmitted to VISSIM simulation software of the simulation layer through the first interface so as to be convenient for simulation by the simulation software.

In some alternative embodiments, referring to fig. 3, algorithm layer 2 specifically includes:

a vehicle parameter acquisition unit 21 for acquiring vehicle parameters in real time based on the first interface;

a vehicle instruction generation unit 22 for generating a vehicle control instruction in real time based on an automatic driving algorithm and vehicle parameters;

a vehicle instruction formatting unit 23 for packaging the vehicle control instruction into a first interface message transmission format as a first control instruction;

the vehicle command transmission unit 24 is configured to transmit the first control command to the simulation layer through the first interface.

The vehicle parameter characterization VISSIM simulation software is used for executing the self state parameters of each vehicle in the simulation test process.

It should be noted that, the interface provided by the VISSIM simulation software may implement joint simulation, and the interface includes a component object model interface (COM), an external driver model interface (EDM), and a Driving Simulator Interface (DSI).

The COM interface is used for interacting and controlling with the VISSIM to create, modify and run a traffic simulation model, acquire data, execute simulation experiments and automate various tasks. Through a COM interface, python can extract static environment information (such as a road network, traffic lights and the like) in a VISSIM simulation environment, and the extracted static environment information can be used as parameters to be input into a simulation model for tasks such as scene generation, vehicle-road collaborative strategy debugging and the like; python can solve the model in real time in the simulation process by calling the official package of the open source solver SCIP, and returns the solving result to the VISSIM road network through the Python environment and the COM interface; the control of VISSIM simulation software can be realized, so that the functions of scene generation, visual display and evaluation of simulation results and the like are supported.

The EDM interface is used for testing and developing an automatic driving system so as to ensure the safety and performance of the automatic driving vehicle in different traffic environments, the EDM interface is used for controlling the speed and transverse position of the vehicle to be tested, and the result is input to the VISSIM so as to control the next behavior of the tested vehicle in the simulation environment, including the speed, the transverse position and other attributes, and after updating the vehicle position, the VISSIM transmits the information of the vehicle in the simulation environment back to the input end of an automatic driving algorithm through the EDM interface so as to enable the algorithm to perform tasks such as sensing, decision making and planning.

The DSI interface is used to connect and interact with a driving simulator to simulate various driving scenarios. Through the DSI interface, the vehicle under test can be controlled by other simulation software or driving simulators; and each simulation step length, according to the obtained position and speed information, the VISSIM synchronously updates the position and other attributes of the tested vehicle at the frequency of the simulation step length, and returns the position, speed and other information of other vehicles in the environment to other simulation software or driving simulators of the joint simulation.

Illustratively, the first interface in the embodiment of the present invention is an EDM interface provided by the VISSIM simulation software. Parameters of the vehicle in the simulation environment, including information of position, speed and the like, are acquired in real time through the EDM interface, and a control instruction of the vehicle is generated in real time based on an automatic driving algorithm and the vehicle parameters acquired from the vehicle parameter acquisition unit 21, wherein the control instruction can include the speed, steering angle and the like of the vehicle; and packaging the control instruction of the vehicle into a first interface message transmission format, wherein the formatted message at least comprises an instruction type, a vehicle ID, real-time control parameters and the like so as to ensure the compatibility with an interface of VISSIM simulation software, and transmitting the formatted data back to the simulation software to complete real-time vehicle behavior control.

According to the embodiment of the invention, the vehicle parameters are acquired from VISSIM simulation software in real time through the vehicle parameter acquisition unit so as to generate vehicle control instructions, such as instructions of acceleration, steering angle and the like, and the behavior of the vehicle in a simulation environment is controlled; the vehicle control instructions generated in real time are packaged into the first interface message transmission format, so that the instructions can be correctly transmitted in a standardized format, and the behavior of the automatic driving vehicle under different scenes can be effectively simulated.

The cloud control layer 3 comprises a plurality of traffic simulation strategies and is used for acquiring strategy parameters of the simulation layer in real time based on the second interface; generating a second control instruction by using the traffic simulation strategy based on the strategy parameters; and transmitting a second control instruction to the simulation layer based on the second interface.

In the embodiment of the invention, the traffic simulation strategy at least comprises a vehicle-road cooperation strategy, a safety traffic rule strategy, a communication working condition and scene strategy and a case series-parallel strategy.

It can be understood that the vehicle-road cooperative strategy can improve traffic mobility and road utilization rate through information exchange and cooperation between vehicles and road infrastructure; the behavior of the automatic driving vehicle can be ensured to accord with the road traffic regulations based on the traffic regulations and the regulations through the safety traffic regulation policy; through the communication working conditions and the scene strategies, the simulation layer can simulate different traffic communication conditions, such as traffic flow, signal control, communication delay and the like; the simulation layer can simulate different traffic flow control strategies, such as vehicle series-parallel connection and the like through case series-parallel connection strategies.

In some alternative embodiments, referring to fig. 4, the cloud control layer 3 specifically includes:

a policy parameter obtaining unit 31, configured to obtain policy parameters in real time based on the second interface;

a policy parameter generating unit 32, configured to generate a policy control instruction in real time based on the traffic simulation policy and the policy parameter;

a policy parameter formatting unit 33, configured to package the policy control instruction into a second interface message transmission format as a second control instruction;

the policy parameter transmitting unit 34 is configured to transmit the second control instruction to the emulation layer through the second interface.

The policy parameters characterize the control policy parameters of traffic flow in the process of executing simulation test by the VISSIM simulation software.

It should be noted that, the cloud control layer mainly includes a cloud control platform, and the cloud control platform has multiple functions: (1) Supporting large-scale autopilot simulation, allowing simultaneous simulation of multiple vehicles and complex road networks; (2) Allowing a user to dynamically configure simulation resources according to needs, wherein the simulation resources at least comprise a virtual machine, a GPU, storage and the like so as to meet the requirements of specific tasks; (3) A large amount of data generated by simulation can be stored in the cloud, so that subsequent analysis and playback are convenient; (4) The automatic test flow is supported, and at least comprises automatic scene generation, test execution, result analysis and the like, so that the test efficiency is improved and the manual intervention is reduced; (5) Interoperability of different simulation tools and simulation environments is supported, enabling developers to select tools and components appropriate for their needs.

The first interface in the embodiment of the invention is a COM interface provided by the VISSIM simulation software. The cloud control layer acquires strategy parameters in the simulation environment in real time through the COM interface, the strategy parameters at least comprise traffic flow parameters, signal lamp state parameters and the like, strategy control instructions are generated in real time based on traffic simulation strategies and strategy parameters, the strategy control instructions at least comprise traffic signal lamp adjustment, traffic flow adjustment and the like, the strategy control instructions are packaged into a COM interface message transmission format, the formatted messages at least comprise instruction types, real-time strategy control parameter values and the like so as to ensure interface compatibility with VISSIM simulation software, and the formatted data are transmitted back to the simulation software so as to complete real-time traffic flow strategy control.

The simulation layer 4 is used for carrying out initialization operation of the road cloud model based on the road model data, the scene model data, the preset initial vehicle parameters and the preset initial strategy parameters, and carrying out vehicle road cloud model simulation test based on the initialized vehicle road cloud model; updating the vehicle Lu Yun model in real time based on the first control instruction and the second control instruction in the vehicle road cloud model simulation test process, and continuously executing the vehicle road cloud model simulation test based on the updated vehicle road cloud model; transmitting vehicle parameters generated in real time in the vehicle-road cloud simulation test process to an algorithm layer, and transmitting strategy parameters generated in real time in the vehicle-road cloud simulation test to a cloud control layer; the vehicle parameter characterization simulation layer characterizes the state parameters of each vehicle in the simulation test process; the strategy parameters represent control strategy parameters of traffic flow in the simulation test process of the simulation layer.

In some alternative embodiments, referring to fig. 5, the simulation layer 4 specifically includes:

an initialization unit 41, configured to complete an initialization operation of the vehicle road cloud model based on the road model data, the scene model data, and the preset initial vehicle parameters and initial policy parameters;

the simulation execution unit 42 is configured to execute a vehicle road cloud model simulation test according to the initialized vehicle road cloud model;

A simulation data generating unit 43 for generating real-time vehicle behavior simulation data based on the first control instruction, and generating real-time strategy simulation data based on the second control instruction;

the simulation test updating unit 44 is configured to update the model Lu Yun of the vehicle in real time based on the real-time vehicle behavior simulation data and the real-time policy simulation data, and continue to perform the vehicle road cloud model simulation test;

the simulation data transmission unit 45 is configured to transmit, to the algorithm layer, vehicle parameters generated in the vehicle-road cloud simulation test process, and transmit policy parameters generated in the vehicle-road cloud simulation test process to the cloud control layer.

For example, the vehicle road cloud model is initialized by using the road model data, the scene model data, and the preset initial vehicle parameters and initial strategy parameters, and the initialization operation of the vehicle road cloud model at least comprises: the vehicle behavior simulation method comprises the steps of performing simulation test of a vehicle road cloud model by using VISSIM simulation software based on an initialized vehicle road cloud model, simulating vehicle behavior, traffic flow, traffic lights and the like in a simulation process, recording simulation results, generating real-time vehicle behavior simulation data based on the VISSIM simulation software and a first control instruction, generating real-time strategy simulation data based on the VISSIM simulation software and a second control instruction, updating a vehicle Lu Yun model in real time by using a visual interface module of the VISSIM simulation software, transmitting vehicle parameters generated in the simulation process to an algorithm layer for processing and analyzing by using an automatic driving algorithm, transmitting strategy parameters generated in the simulation process to a cloud control layer for strategy control and adjustment of the cloud control layer, and further realizing data interaction and collaborative work of the vehicle road cloud simulation test and the algorithm layer and the cloud control layer.

In some alternative embodiments, referring to fig. 5, the simulation layer 4 further includes:

the simulation result feedback unit 46 is configured to obtain a test result output after the simulation test of the Lu Yun model of the vehicle is finished;

the simulation evaluation unit 47 is configured to obtain a comprehensive evaluation result of the vehicle Lu Yun model simulation test based on the test result and the preset evaluation index.

The test results are analyzed and processed, key information such as vehicle track, real-time vehicle position, traffic flow, traffic scene and the like is extracted, corresponding calculation or analysis is performed for each evaluation index, and the results of each evaluation index are comprehensively considered to obtain comprehensive evaluation results of the vehicle road cloud model simulation test. The preset evaluation indexes at least comprise driving safety, driving efficiency, riding comfort, low-carbon environment friendliness and driving operation compliance. The evaluation result refers to the comprehensive evaluation of the simulation test, and is presented in the form of a score or a grade.

the simulation backtracking unit 48 is used for acquiring a log backvisit file output after the simulation test of the vehicle Lu Yun model is finished, and the log backvisit file is used for backtracking the simulation test process of the vehicle road cloud model.

After the VISSIM simulation test is finished, the simulation test log return visit function is opened, a log return visit file to be saved is selected, the log return visit file is saved in a designated file, and the simulation test process and result are analyzed according to file record information. The log return visit file at least comprises simulation conditions, input data, output results and system behaviors. If a problem or a fault is found, the log return visit file can help to carry out retrospective analysis, find the root cause of the problem and help engineers to carry out fault diagnosis and repair.

The simulation backtracking unit is used for acquiring a log backvisit file output after the simulation test of the vehicle Lu Yun model is finished by the VISSIM simulation software, and the file records detailed information in the simulation test process; the problems in the simulation process can be solved through simulation backtracking, the problems of potential system faults, algorithm errors, strategy errors and the like are solved, and the reliability of automatic driving is further optimized.

In some optional embodiments, the algorithm layer 2 is further configured to generate an algorithm optimization requirement of an autopilot algorithm based on a preset algorithm performance index and a preset scene requirement, and transmit the algorithm optimization requirement to the cloud control layer 3; the cloud control layer 3 is also used for generating algorithm optimization parameters based on algorithm optimization requirements by utilizing a vehicle-road cooperation strategy, a safety traffic rule strategy, a communication working condition and a scene strategy, and transmitting the algorithm optimization parameters to the algorithm layer 2; algorithm layer 2, also used for optimizing the automatic driving algorithm based on the algorithm optimization parameters.

The preset performance indexes at least comprise a shortest path, a fastest arrival time and the like, the scene requirement at least comprises traffic flow and the like, after the cloud control layer receives the algorithm optimization requirement transmitted by the algorithm layer, the algorithm layer generates algorithm optimization parameters by utilizing a vehicle-road cooperation strategy, a safety traffic rule strategy, a communication working condition and the scene strategy, and the algorithm layer optimizes an automatic driving path planning algorithm according to the algorithm optimization parameters transmitted by the cloud control layer, wherein the specific optimization content of the algorithm at least comprises path selection logic optimization, speed planning logic optimization, lane changing strategy logic optimization and the like.

The embodiments of the present invention provide a vehicle-road cloud integrated joint simulation method embodiment, and it should be noted that, the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and, although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different from that illustrated herein. In this embodiment, a vehicle-road cloud integrated joint simulation method is provided and applied to a gateway, and fig. 6 is a flowchart of the vehicle-road cloud integrated joint simulation method according to an embodiment of the present invention, as shown in fig. 6, where the flowchart includes the following steps:

In step S601, road model data and scene model data are generated based on the actual road and the actual scene, and the road model data and the scene model data are imported into the simulation layer.

Step S602, acquiring vehicle parameters of a simulation layer in real time based on a first interface; generating a first control command based on vehicle parameters using an autopilot algorithm; the first control instruction is transmitted to the simulation layer based on the first interface.

Step S603, acquiring strategy parameters of the simulation layer in real time based on the second interface; generating a second control instruction by using the traffic simulation strategy based on the strategy parameters; and transmitting a second control instruction to the simulation layer based on the second interface.

Step S604, carrying out initialization operation of a road cloud model based on road model data, scene model data, preset initial vehicle parameters and initial strategy parameters, and executing a vehicle road cloud model simulation test based on the initialized vehicle road cloud model; updating the vehicle Lu Yun model in real time based on the first control instruction and the second control instruction in the vehicle road cloud model simulation test process, and continuously executing the vehicle road cloud model simulation test based on the updated vehicle road cloud model; and transmitting the vehicle parameters generated in real time in the vehicle-road cloud simulation test process to an algorithm layer, and transmitting the strategy parameters generated in real time in the vehicle-road cloud simulation test to a cloud control layer.

The specific implementation manner of each method step and some optional method embodiments of the method provided by the embodiment of the present invention are the same as the functions implemented by each functional module of the above system, and are not described herein again.

According to the method provided by the embodiment of the invention, the road model data and the scene model data are generated at the application layer according to the actual road scene and are imported into VISSIM simulation software of the simulation layer, so that accurate simulation can be performed based on the actual road condition, and the actual traffic condition including the road condition, the vehicle position, the speed and the like is reflected; the algorithm layer acquires vehicle parameters of the simulation layer in real time through the first interface, and generates a first control instruction by utilizing an automatic driving algorithm, so that an automatic driving vehicle can be allowed to make a real-time decision according to the actual traffic condition, and the road safety and the traffic efficiency are improved; the cloud control layer acquires strategy parameters of the simulation layer in real time through the second interface, generates a second control instruction by utilizing the traffic simulation strategy, and can monitor and adjust the control strategy of the traffic flow in real time and test different traffic strategies in the simulation, so that the traffic mobility is improved, and the congestion and traffic accidents are reduced; the simulation layer transmits the vehicle parameters generated in real time to the algorithm layer and transmits the strategy parameters generated in real time to the cloud control layer, so that the algorithm layer and the cloud control layer can feed back and adjust in real time according to the simulation result to optimize an automatic driving algorithm and a traffic simulation strategy, and the performance and the effect of the system are improved; the system provided by the invention can reduce the cost and risk of testing on an actual road by using the simulation layer simulation, and simultaneously allows large-scale testing under different conditions so as to accelerate the development of an automatic driving system.

The embodiment of the invention also provides computer equipment, which is provided with the vehicle-road cloud integrated joint simulation system shown in the figures 1 to 5.

Referring to fig. 7, fig. 7 is a schematic structural diagram of a computer device according to an alternative embodiment of the present invention, as shown in fig. 7, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are communicatively coupled to each other using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the computer device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In some alternative embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple computer devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 10 is illustrated in fig. 7.

The processor 10 may be a central processor, a network processor, or a combination thereof. The processor 10 may further include a hardware chip, among others. The hardware chip may be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general-purpose array logic, or any combination thereof.

Wherein the memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform a method for implementing the embodiments described above.

The memory 20 may include a storage program area that may store an operating system, at least one application program required for functions, and a storage data area; the storage data area may store data created from the use of the computer device of the presentation of a sort of applet landing page, and the like. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 20 may optionally include memory located remotely from processor 10, which may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

Memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk, or solid state disk; the memory 20 may also comprise a combination of the above types of memories.

The computer device also includes a communication interface 30 for the computer device to communicate with other devices or communication networks.

The embodiments of the present invention also provide a computer readable storage medium, and the method according to the embodiments of the present invention described above may be implemented in hardware, firmware, or as a computer code which may be recorded on a storage medium, or as original stored in a remote storage medium or a non-transitory machine readable storage medium downloaded through a network and to be stored in a local storage medium, so that the method described herein may be stored on such software process on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, a solid state disk or the like; further, the storage medium may also comprise a combination of memories of the kind described above. It will be appreciated that a computer, processor, microprocessor controller or programmable hardware includes a storage element that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the methods illustrated by the above embodiments.

Although the embodiments of the present invention have been described with reference to the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope of the invention as defined in the specification.

Claims

1. A vehicle-road cloud integrated joint simulation system, the system comprising: an application layer, an algorithm layer, a cloud control layer and a simulation layer;

2. The system of claim 1, wherein the autopilot algorithm comprises at least a perception algorithm, a positioning and mapping algorithm, a path planning and decision algorithm, a control algorithm, a communication and networking algorithm;

the traffic simulation strategy at least comprises a vehicle-road cooperation strategy, a safety traffic rule strategy, a communication working condition and scene strategy and a case series-parallel strategy.

3. The system of claim 1, wherein the application layer comprises:

the point cloud acquisition unit is used for acquiring point cloud data of an actual road and an actual scene based on acquisition equipment;

the model extraction unit is used for cutting and filtering the point cloud data to extract earth surface model information;

the information labeling unit is used for labeling road element information, corresponding attributes and connection relations of the earth surface model information, labeling scene element information, corresponding attributes and connection relations of the earth surface model information, and converting the labeled earth surface model information into an application map when the vehicle is driven automatically;

the model generating unit is used for importing the road element information, the corresponding attribute and the connection relation in the application map into a twin model, generating static road grids and traffic facility position information as road model data and generating a traffic facility entity structure as scene model data;

and the data transmission unit is used for converting the road model data and the scene model data into readable formats and importing the road model data and the scene model data into the simulation layer.

4. The system of claim 1, wherein the algorithm layer comprises:

The vehicle parameter acquisition unit is used for acquiring vehicle parameters in real time based on the first interface;

a vehicle instruction generation unit for generating a vehicle control instruction in real time based on an automatic driving algorithm and vehicle parameters;

a vehicle command formatting unit, configured to package the vehicle control command into a first interface message transmission format as a first control command;

and the vehicle instruction transmission unit is used for transmitting the first control instruction to the simulation layer through the first interface.

5. The system of claim 1, wherein the cloud control layer comprises:

the strategy parameter acquisition unit is used for acquiring strategy parameters in real time based on the second interface;

the strategy parameter generation unit is used for generating strategy control instructions in real time based on the traffic simulation strategy and the strategy parameters;

a policy parameter formatting unit, configured to package the policy control instruction into a second interface message transmission format as a second control instruction;

and the strategy parameter transmission unit is used for transmitting the second control instruction to the simulation layer through a second interface.

6. The system of claim 1, wherein the simulation layer comprises:

the initialization unit is used for completing the initialization operation of the vehicle road cloud model based on the road model data, the scene model data, the preset initial vehicle parameters and the preset initial strategy parameters;

The simulation execution unit is used for executing the vehicle road cloud model simulation test according to the initialized vehicle road cloud model;

the simulation data generation unit is used for generating real-time vehicle behavior simulation data based on the first control instruction and real-time strategy simulation data based on the second control instruction;

the simulation test updating unit is used for updating the model Lu Yun of the vehicle in real time based on the real-time vehicle behavior simulation data and the real-time strategy simulation data and continuously executing the simulation test of the vehicle road cloud model;

the simulation data transmission unit is used for transmitting the vehicle parameters generated in the vehicle-road cloud simulation test process to the algorithm layer and transmitting the strategy parameters generated in the vehicle-road cloud simulation test process to the cloud control layer.

7. The system of claim 6, wherein the emulation layer further comprises:

the simulation result feedback unit is used for obtaining a test result output after the simulation test of the Lu Yun model of the vehicle is finished;

and the simulation evaluation unit is used for acquiring the comprehensive evaluation result of the vehicle Lu Yun model simulation test based on the test result and a preset evaluation index.

8. The system of claim 6, wherein the emulation layer further comprises:

And the simulation backtracking unit is used for acquiring a log backtracking file output after the simulation test of the vehicle Lu Yun model is finished, and the log backtracking file is used for backtracking the simulation test process of the vehicle road cloud model.

9. The system of claim 2, wherein the algorithm layer is further configured to generate an algorithm optimization requirement of an autopilot algorithm based on a preset algorithm performance index and a preset scene requirement, and transmit the algorithm optimization requirement to the cloud control layer;

the cloud control layer is also used for generating algorithm optimization parameters based on the algorithm optimization requirements by utilizing a vehicle-road cooperation strategy, a safety traffic rule strategy, a communication working condition and a scene strategy, and transmitting the algorithm optimization parameters to the algorithm layer;

the algorithm layer is also used for optimizing the automatic driving algorithm based on the algorithm optimization parameters.

10. A vehicle-road cloud integrated joint simulation method, wherein the method is applied to the vehicle-road cloud integrated joint simulation system of claim 1, and the method comprises:

generating road model data and scene model data based on an actual road and an actual scene, and importing the road model data and the scene model data into a simulation layer;

acquiring vehicle parameters of the simulation layer in real time based on the first interface; generating a first control instruction by using an automatic driving algorithm based on the vehicle parameters; transmitting the first control instruction to a simulation layer based on a first interface;

Acquiring strategy parameters of the simulation layer in real time based on the second interface; generating a second control instruction by using a traffic simulation strategy based on the strategy parameters; transmitting the second control instruction to a simulation layer based on a second interface;

carrying out initialization operation of a road cloud model based on the road model data, the scene model data, and preset initial vehicle parameters and initial strategy parameters, and executing a vehicle road cloud model simulation test based on the initialized vehicle road cloud model; updating the vehicle Lu Yun model in real time based on the first control instruction and the second control instruction in the vehicle road cloud model simulation test process, and continuously executing the vehicle road cloud model simulation test based on the updated vehicle road cloud model; and transmitting the vehicle parameters generated in real time in the vehicle-road cloud simulation test process to an algorithm layer, and transmitting the strategy parameters generated in real time in the vehicle-road cloud simulation test to a cloud control layer.

11. A computer device, comprising:

the vehicle-road cloud integrated joint simulation method comprises a memory and a processor, wherein the memory and the processor are in communication connection, computer instructions are stored in the memory, and the processor executes the computer instructions, so that the vehicle-road cloud integrated joint simulation method is executed.

12. A computer-readable storage medium having stored thereon computer instructions for causing a computer to perform the vehicle-road cloud integrated joint simulation method of claim 10.