Simulation method and device for intelligent driving test, electronic equipment, program product and storage medium

Abstract

The invention provides a simulation method and device for an intelligent driving test, electronic equipment, a program product and a storage medium. The simulation method comprises the steps that a to-be-tested problem set and a traffic sign data set of a target area are acquired; according to each to-be-tested problem in the to-be-tested problem set, processing an acquired data set of a vehicle, and determining an acquired data subset corresponding to each to-be-tested problem; on the basis of the collected data subset corresponding to each to-be-tested problem, real vehicle simulation of each to-be-tested problem is generated; traversing and combining all traffic sign data in the traffic sign data set to generate corresponding road network simulation; and combining the real vehicle simulation of each to-be-tested problem with the road network simulation to obtain target simulation of the intelligent driving test. Therefore, integrated verification of high authenticity, high coverage, compliance and repeatability of intelligent driving is realized.

Description

Technical Field

[0001] This invention relates to the automotive field, and more particularly to a simulation method, apparatus, electronic device, program product, and storage medium for intelligent driving testing. Background Technology

[0002] With the rapid development of intelligent driving technology, assistance functions such as ISA (Intelligent Speed ​​Assistance) and TSR (Traffic Sign Recognition) are gradually becoming important safety features for evaluating a vehicle's intelligent driving capabilities. Taking ISA as an example, by monitoring traffic signs such as speed limits on the road and combining information from the navigation system, it provides drivers with accurate speed limit prompts and issues warnings or speed control when speeding occurs, thereby ensuring driving safety. However, to ensure the reliability and accuracy of intelligent driving functions in various complex traffic environments, rigorous testing and verification are essential.

[0003] However, testing using real vehicles is inefficient, and existing simulation tests often fail to cover complex road conditions. Summary of the Invention

[0004] This invention addresses the problems of low efficiency in real-vehicle testing and difficulty in covering complex situations in simulation testing during intelligent driving in existing technologies. It proposes a simulation method, device, electronic equipment, program product, and storage medium for intelligent driving testing.

[0005] In a first aspect, embodiments of the present invention provide a simulation method for intelligent driving testing, the simulation method comprising: Obtain the set of questions to be tested and the set of traffic sign data for the target area; The vehicle's collected data set is processed according to each test question in the set of test questions to determine the subset of collected data corresponding to each test question; Based on the subset of collected data corresponding to each of the test problems, a real vehicle simulation of each test problem is generated; The traffic sign data in the traffic sign dataset is traversed and combined to generate the corresponding road network simulation. By combining the real vehicle simulation of each of the problems to be tested with the road network simulation, the target simulation for intelligent driving testing is obtained.

[0006] Optionally, the collected data set includes video data; The step of processing the vehicle's collected data set according to each test question in the test question set to determine the collected data subset corresponding to each test question includes: For each of the questions to be tested, a matching video data segment is determined from the video data, and a subset of collected data corresponding to each question to be tested is generated based on the video data segment.

[0007] Optionally, the step of traversing and combining all traffic sign data in the traffic sign data set to generate a corresponding road network simulation includes: The traffic sign data in the traffic sign dataset is traversed and combined to generate the corresponding road network simulation using Worldsim.

[0008] Optionally, generating a real-vehicle simulation for each of the tested problems based on a subset of collected data corresponding to each tested problem includes: Based on the subset of collected data corresponding to each of the problems to be tested, a real vehicle simulation of each problem to be tested is generated using Logsim.

[0009] Optionally, the step of combining the real-vehicle simulation of each of the problems to be tested with the road network simulation to obtain the target simulation for intelligent driving testing further includes: In HIL, the real vehicle simulation and the road network simulation are input to the hardware under test to obtain the target simulation for intelligent driving testing.

[0010] Optionally, the set of problems to be tested includes at least one of software version, test region, test problem category, and test problem description; Optionally, the test questions in the set of test questions are generated according to the test items of the ISA regulations; Optionally, the collected data set includes at least one of video data, image data, and sensor data; Optionally, the traffic sign data set includes at least one of the following: displayed digital speed limit signs, implicit digital speed limit signs, implicit non-digital speed limit signs, numerical zones, traffic reduction zones, highways, urban expressways, and urban boundaries.

[0011] Secondly, embodiments of the present invention provide a simulation device for intelligent driving testing, the simulation device comprising: The acquisition module is used to acquire the set of questions to be tested and the set of traffic sign data for the target test area; The processing module is used to process the vehicle's collected data set according to each test problem in the test problem set, and determine the collected data subset corresponding to each test problem; The first generation module is used to generate a real vehicle simulation of each of the tested problems based on the subset of collected data corresponding to each tested problem. The second generation module is used to traverse and combine all traffic sign data in the traffic sign data set to generate a corresponding road network simulation. The module combines the real vehicle simulation of each problem to be tested with the road network simulation to obtain the target simulation for intelligent driving testing.

[0012] Thirdly, embodiments of the present invention provide an electronic device, which includes a processor and a memory. The memory stores at least one instruction, at least one program, code set, or instruction set. The at least one instruction, at least one program, code set, or instruction set is loaded and executed by the processor to implement the simulation method for intelligent driving testing as described in any one of the first aspects.

[0013] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing at least one instruction or at least one program, wherein the at least one instruction or at least one program is loaded and executed by a processor to implement a simulation method for intelligent driving testing as described in any one of the first aspects.

[0014] Fifthly, embodiments of the present invention provide a computer program product, including a computer program that, when executed by a processor, implements a simulation method for intelligent driving testing as described in any one of the first aspects.

[0015] The present invention provides the following technical advantages: by traversing and combining all traffic sign data in the traffic sign dataset, road network simulation can be greatly generalized. Real vehicle data playback alone cannot cover all potential situations, especially extreme and edge scenarios required by regulations. These need to be supplemented through virtual generalization, thereby ensuring test coverage and robustness, and significantly improving performance. It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0016] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 A flowchart illustrating a simulation method for intelligent driving testing, provided as an exemplary embodiment of the present invention; Figure 2 A structural diagram of a simulation device for intelligent driving testing provided as an exemplary embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided as an exemplary embodiment of the present invention. Detailed Implementation

[0017] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, without limitation, the embodiments and features described in the embodiments of the present invention can be combined with each other.

[0018] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0019] Before describing the embodiments of the present invention, the concepts that may be used in this embodiment will be explained first: LogSim (data playback simulation) is a method for building simulation scenarios based on real road data. It collects real road data, including traffic environment, obstacle locations, and vehicle speed, through various sensors on vehicles, and then replays this data in the simulation system to simulate various possible traffic scenarios. LogSim's advantage lies in its ability to realistically reflect the complex and ever-changing conditions of the actual traffic environment, especially for specific scenarios that have already occurred, where it can accurately reproduce and verify them. LogSim's limitation is that it can only verify known scenarios and cannot generate and test new or unknown scenarios.

[0020] WorldSim (virtual simulation) is a modeling-based simulation method. It constructs simulation scenarios based on pre-defined factors such as roads, obstacles, and traffic rules, and allows for the free addition and modification of elements within these scenarios, enabling the free creation and generalization of virtual scenarios. WorldSim's advantages lie in its high flexibility and scalability, capable of generating various possible traffic scenarios for testing the robustness and adaptability of autonomous driving systems. WorldSim's limitations include the complexity of constructing its simulation scenarios, requiring significant manpower and time, and the somewhat limited accuracy of the simulation.

[0021] HIL (Hardware-in-the-Loop) is a testing method that places real hardware within a closed loop of a simulation system. An HIL test bench is the experimental platform that carries out this type of testing. It typically consists of the hardware under test (ECU / domain controller / forward-looking integrated machine, etc.), a simulation system (simulating the environment, vehicle dynamics, sensor models, traffic scenarios, etc.), I / O interfaces and bus simulators (CAN, LIN, FlexRay, Ethernet, etc.), and execution and control equipment (video darkroom, signal generator, data acquisition card, monitoring PC). In HIL, the simulation system runs in real time, generating environmental signals (such as vehicle speed, acceleration, and sensor inputs). These signals are sent to the real hardware (device under test) through the I / O interface. The hardware under test executes a control algorithm based on the input and outputs control signals (such as throttle, brake commands, and alarms). These output signals are then fed back to the simulation system to update the vehicle and environmental states, forming a closed loop.

[0022] Based on this, an exemplary embodiment of the present invention provides a simulation method for intelligent driving testing, see below. Figure 1 The methods include: S101. Obtain the set of questions to be tested and the set of traffic sign data for the target area.

[0023] In intelligent driving function testing, especially for ISA (Intelligent Driving Assurance), different regions have different regulatory requirements and traffic sign systems. To ensure the comprehensiveness and relevance of the testing, it is necessary to first identify the set of test questions for the target region. Typically, the test questions in this set are generated based on the relevant regulatory test items.

[0024] In this embodiment, the test problems in the test problem set are generated according to the ISA regulatory test items. Specifically, the test problem set includes at least one of the following: software version, test region, test problem category, and test problem description.

[0025] A traffic sign dataset refers to the collection of traffic signs and their parameterized descriptions related to vehicle speed limits within the regulatory requirements of a target region. This dataset typically includes at least one of the following: explicit numerical speed limit signs (e.g., "50 km / h"), implicit numerical speed limit signs (e.g., default speed limits in some EU countries, which are not explicitly marked but are mandated by law), implicit non-numerical speed limit signs (e.g., "residential area speed limit" or "school zone" signs), numerical zones (signs for specific speed limit zones, such as "30 km / h zone"), traffic reduction zones (e.g., low-emission zones or flow restriction zones, which impose restrictions on vehicle speed or entry conditions), highway signs, urban expressway signs, and urban boundary signs (which automatically trigger speed limit conditions when entering or leaving the city). Because traffic signs vary significantly across different regions, it is necessary to define the traffic signs for each region before simulation.

[0026] This embodiment summarizes how establishing a set of problems to be tested and a set of traffic sign data ensures that the test problems cover both typical and corner cases, providing a clear basis for subsequent scenario generation.

[0027] S102. Process the vehicle's collected data set according to each test problem in the test problem set to determine the collected data subset corresponding to each test problem.

[0028] The collected data set includes at least one of video data, image data, and sensor data. The collected data is typically acquired in real-time using onboard devices such as sensors, radar, and cameras during actual vehicle operation. The vehicle model from which the data is collected must match the actual vehicle model equipped with intelligent driving functions to avoid inaccurate test results due to model mismatch.

[0029] While the collected dataset includes various types of data, not all of it is suitable for simulation testing. For example, if the problem to be tested describes recognizing speed limit signs on a highway and alerting the user, then data collected from highways should be selected. This could be sensor data showing vehicle speeds greater than or equal to 100 km / h. Alternatively, if the problem to be tested describes yielding to pedestrians signs at intersections, then data collected from intersections should be selected, such as image data from intersections.

[0030] It should be noted that the matching between the collected data and the test questions can be done manually by staff, or the test questions and collected data can be identified separately according to the pre-trained model to obtain the labels of the test questions, and then the labels of the test questions are matched with the labels of the collected data to obtain the collected data corresponding to each test question. Typically, a test question includes one or more collected data, and one or more collected data constitutes a subset of the collected data corresponding to each test question.

[0031] In one embodiment, the processing of acquired data as video data is described, wherein the acquired data set includes video data. Step S102 includes: For each of the questions to be tested, a matching video data segment is determined from the video data, and a subset of collected data corresponding to each question to be tested is generated based on the video data segment.

[0032] In this embodiment, the vehicle collects a large amount of video, image, and sensor data during real-vehicle testing. Directly using the raw data for simulation would be redundant and inefficient. Therefore, it is necessary to filter the data in conjunction with the problem to be tested, extracting the collected data that matches the problem. Necessity: By processing the data and associating the massive amount of collected data with the specific test problem, it is possible to quickly locate and reproduce the problem scenario, significantly improving simulation efficiency and test relevance.

[0033] S103. Based on the subset of collected data corresponding to each of the problems to be tested, generate a real vehicle simulation for each of the problems to be tested.

[0034] Vehicle simulation, in particular, is the process of reproducing real traffic scenarios in a simulation environment by playing back data collected from actual vehicles. It typically uses camera video, vehicle operating parameters, or sensor data as input to reproduce the scenarios that occur during testing, thus meeting the needs of simulation testing.

[0035] In one embodiment, step S103 includes: Based on the subset of collected data corresponding to each of the problems to be tested, a real vehicle simulation of each problem to be tested is generated using Logsim.

[0036] The LogSim technology allows for the replay of the collected data subset corresponding to each test problem, thereby reproducing real-world traffic scenarios. This method ensures the authenticity and consistency of the scenarios. In intelligent driving testing, especially for ISA (Intelligent Driving Assignment), misidentification or missed identification can easily occur in different traffic environments. By reproducing real-world scenarios using LogSim, not only can the environment in which the problem occurred be accurately restored, but the repeatability and comparability of the test results can also be guaranteed.

[0037] S104. Traverse and combine all traffic sign data in the traffic sign data set to generate the corresponding road network simulation.

[0038] Road network simulation, based on virtual modeling, involves constructing a road topology and traffic sign model using traffic sign data sets for the target area, and generating a virtual traffic scenario through parameter combinations. Furthermore, the road network construction in simulation should establish road topologies covering various types of roads, including urban roads, rural one-way lanes, curves with different curvatures, expressways, and expressway ramps.

[0039] In this embodiment, by traversing and combining all traffic sign data in the traffic sign dataset, the road network simulation can be generalized to a great extent. Relying solely on the playback of data collected from real vehicles cannot cover all potential situations, especially the extreme and edge scenarios required by regulations. These need to be supplemented by virtual generalization to ensure test coverage and robustness.

[0040] In one embodiment, step S104 includes: The traffic sign data in the traffic sign dataset is traversed and combined to generate the corresponding road network simulation using Worldsim.

[0041] Among them, the WorldSim technology is used to construct road topology and traffic sign models, and scene generalization is achieved through traversal combination. It can quickly generate thousands of road network and sign combination scenarios, and further generalize the types and parameters of traffic participants.

[0042] S105. Combine the real vehicle simulation of each of the problems to be tested with the road network simulation to obtain the target simulation of intelligent driving test.

[0043] This allows for loading real-vehicle simulations of the problem under test into different nodes of the road network simulation, based on varying testing requirements. When a node is triggered, the real-vehicle simulation of the problem under test is displayed, which can be done on the front end.

[0044] In one embodiment, step S105 includes: In HIL, the inputs of the real vehicle simulation and the road network simulation are fed into the hardware under test to obtain the target simulation for intelligent driving testing, thereby realizing the fusion testing of the two types of simulations.

[0045] In addition, HIL can also provide closed-loop testing, that is, when HIL receives instructions from the user during the test, it adjusts the target simulation according to the user's instructions.

[0046] In this embodiment, real-vehicle simulation based on real-vehicle data collection is combined with road network simulation based on traffic sign data sets to verify intelligent driving functions. This significantly improves test coverage while ensuring the realism of the simulation scenario. Therefore, it can not only reproduce problem scenarios that occur on actual roads, but also generate large-scale generalized test scenarios that meet the regulatory requirements of the target region, thereby achieving integrated verification of high realism, high coverage, compliance, and repeatability of intelligent driving.

[0047] An exemplary embodiment of the present invention also provides a simulation device for intelligent driving testing, see below. Figure 2 The simulation device includes: The acquisition module 21 is used to acquire the set of questions to be tested and the set of traffic sign data for the target test area.

[0048] The processing module 22 is used to process the vehicle's collected data set according to each test problem in the set of test problems, and determine the collected data subset corresponding to each test problem.

[0049] The first generation module 23 is used to generate a real vehicle simulation of each of the test problems based on the subset of collected data corresponding to each test problem.

[0050] The second generation module 24 is used to traverse and combine all traffic sign data in the traffic sign data set to generate a corresponding road network simulation.

[0051] Module 25 is used to combine the real vehicle simulation of each of the problems to be tested with the road network simulation to obtain the target simulation of intelligent driving test.

[0052] In one embodiment, the collected data set includes video data.

[0053] The processing module 22 is further configured to determine a matching video data segment from the video data according to each of the problems to be tested, and generate a subset of collected data corresponding to each of the problems to be tested according to the video data segments.

[0054] In one embodiment, the second generation module 24 is further configured to traverse and combine all traffic sign data in the traffic sign data set, and generate a corresponding road network simulation using Worldsim.

[0055] The first generation module 23 is also used to generate a real vehicle simulation of each of the test problems using Logsim based on the subset of collected data corresponding to each test problem.

[0056] In one embodiment, module 25 is further configured to input the real vehicle simulation and the road network simulation into the hardware under test in the HIL to obtain the target simulation for intelligent driving testing.

[0057] In one embodiment, the set of issues to be tested includes at least one of software version, test region, test issue category, and test issue description; In one embodiment, the test questions in the set of test questions are generated according to the ISA regulatory test entries.

[0058] In one embodiment, the collected data set includes at least one of video data, image data, and sensor data.

[0059] In one embodiment, the traffic sign data set includes at least one of the following: explicit digital speed limit signs, implicit digital speed limit signs, implicit non-digital speed limit signs, numerical zones, traffic reduction zones, highways, urban expressways, and urban boundaries.

[0060] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs.

[0061] An exemplary embodiment of the present invention also provides an electronic device, see below. Figure 3 The electronic device 300 can vary significantly due to differences in configuration or performance. It may include one or more central processing units (CPUs) 310 (CPUs 310 may include, but are not limited to, microprocessors (MCUs) or programmable logic devices (FPGAs), a memory 330 for storing data, and one or more storage media 320 (e.g., one or more mass storage devices) for storing application programs 323 or data 322. The memory 330 and storage media 320 may be temporary or persistent storage. The storage media 320 stores at least one instruction, at least one program, code set, or instruction set. The processor loads and executes the at least one instruction, at least one program, code set, or instruction set to implement the aforementioned simulation method for intelligent driving testing.

[0062] Furthermore, the central processing unit 310 can be configured to communicate with the storage medium 320 and execute a series of instructions stored in the storage medium 320 on the electronic device 300. The electronic device 300 may also include one or more power supplies 330, one or more wired or wireless network interfaces 30, one or more input / output interfaces 340, and / or one or more operating systems 321, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0063] The input / output interface 340 can be used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the electronic device 300. In one example, the input / output interface 340 includes a network interface controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the input / output interface 340 may be a radio frequency (RF) module used for wireless communication with the Internet.

[0064] Those skilled in the art will understand that Figure 3 The structure shown is for illustrative purposes only and does not limit the structure of the electronic device described above. For example, the electronic device 300 may also include... Figure 3 The more or fewer components shown, or having the same Figure 3 The different configurations shown.

[0065] Embodiments of the present invention also provide a computer-readable storage medium, which can be disposed in a server to store at least one instruction, at least one program, code set, or instruction set related to a simulation method for implementing intelligent driving testing, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the above-described simulation method for intelligent driving testing.

[0066] Optionally, in this embodiment, the storage medium may be located at at least one of the multiple network servers in a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0067] Embodiments of the present invention also provide a computer program product, including a computer program that, when executed by a processor, implements the above-described simulation method for intelligent driving testing.

[0068] The program code for executing the computer program product of this disclosure can be written in any combination of one or more programming languages, and the program code can be executed entirely on a user device, partially on a user device, as a stand-alone software package, partially on a user device and partially on a remote device, or entirely on a remote device.

[0069] As is known from common technical knowledge, this invention can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative in all respects and are not the only ones. All modifications within the scope of this invention or its equivalents are included in this invention.

[0070] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0071] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0072] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0073] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A simulation method for intelligent driving testing, characterized in that, The simulation method includes: Obtain the set of questions to be tested and the set of traffic sign data for the target area; The vehicle's collected data set is processed according to each test question in the set of test questions to determine the subset of collected data corresponding to each test question; Based on the subset of collected data corresponding to each of the test problems, a real vehicle simulation of each test problem is generated; The traffic sign data in the traffic sign dataset is traversed and combined to generate the corresponding road network simulation. By combining the real vehicle simulation of each of the problems to be tested with the road network simulation, the target simulation for intelligent driving testing is obtained.

2. The simulation method as described in claim 1, characterized in that, The collected data set includes video data; The step of processing the vehicle's collected data set according to each test question in the test question set to determine the collected data subset corresponding to each test question includes: For each of the questions to be tested, a matching video data segment is determined from the video data, and a subset of collected data corresponding to each question to be tested is generated based on the video data segment.

3. The simulation method as described in claim 1, characterized in that, The step of traversing and combining all traffic sign data in the traffic sign data set to generate the corresponding road network simulation includes: The traffic sign data in the traffic sign dataset is traversed and combined to generate the corresponding road network simulation using Worldsim.

4. The simulation method as described in claim 1, characterized in that, The step of generating a real-vehicle simulation for each of the tested problems based on a subset of collected data corresponding to each tested problem includes: Based on the subset of collected data corresponding to each of the problems to be tested, a real vehicle simulation of each problem to be tested is generated using Logsim.

5. The simulation method as described in claim 1, characterized in that, The step of combining the real-vehicle simulation of each of the problems to be tested with the road network simulation to obtain the target simulation for intelligent driving testing also includes: In HIL, the real vehicle simulation and the road network simulation are input to the hardware under test to obtain the target simulation for intelligent driving testing.

6. The simulation method according to any one of claims 1-5, characterized in that, The set of issues to be tested includes at least one of the following: software version, test region, test issue category, and test issue description; And / or, the test questions in the set of test questions are generated according to the test entries of the ISA regulations; And / or, the collected data set includes at least one of video data, image data, and sensor data; And / or, the traffic sign data set includes at least one of the following: displayed digital speed limit signs, implicit digital speed limit signs, implicit non-digital speed limit signs, numerical zones, traffic reduction zones, highways, urban expressways, and urban boundaries.

7. A simulation device for intelligent driving testing, characterized in that, The simulation device includes: The acquisition module is used to acquire the set of questions to be tested and the set of traffic sign data for the target test area; The processing module is used to process the vehicle's collected data set according to each test problem in the test problem set, and determine the collected data subset corresponding to each test problem; The first generation module is used to generate a real vehicle simulation of each of the tested problems based on the subset of collected data corresponding to each tested problem. The second generation module is used to traverse and combine all traffic sign data in the traffic sign data set to generate a corresponding road network simulation. The module combines the real vehicle simulation of each test problem with the road network simulation to obtain the target simulation for intelligent driving testing.

8. An electronic device, characterized in that, The electronic device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set, or instruction set, and the processor loads and executes the at least one instruction, at least one program, code set, or instruction set to implement the simulation method for intelligent driving testing as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction or at least one program, which is loaded and executed by a processor to implement the simulation method for intelligent driving testing as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the simulation method for intelligent driving testing as described in any one of claims 1-6.

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