Simulation test method and device based on autonomous vehicle, medium and product

Abstract

The embodiment of the invention relates to the technical field of traffic, and discloses a simulation test method and device based on an automatic driving vehicle, a medium and a product. The method comprises the following steps: acquiring position data of the electronic fence; obtaining threshold data configured based on the type of the traffic event; automatically simulating vehicle driving according to the position data and the threshold data to obtain trajectory data; and performing a simulation test according to the trajectory data. The technical problem that potential safety hazards exist in a method for simulating and triggering various traffic events by manually driving a real vehicle in the prior art can be at least solved.

Description

Technical Field

[0001] The present application relates to the field of transportation technology, and in particular to a simulation test method, equipment, medium and product based on an autonomous driving vehicle. Background Art

[0002] When driving on the road, an autonomous driving vehicle needs to be able to accurately perceive the surrounding environment, including other autonomous driving vehicles, pedestrians, traffic signals, etc. This perception capability is crucial for the safe driving of autonomous driving vehicles. Among them, the perception equipment or system installed next to the road is loaded with a roadside perception algorithm, which can detect autonomous driving vehicles on the road. Therefore, the autonomous driving vehicle is generally tested for safety based on the roadside perception algorithm. Specifically, it mainly tests whether the autonomous driving vehicle has driven to a limited area, whether it triggers corresponding traffic events (such as approaching other autonomous driving vehicles, violating traffic rules, etc.) and issues warnings. This safety test is conducive to the autonomous driving system to accurately respond to the traffic events.

[0003] In the related art, the method for performing safety testing on the autonomous driving vehicle based on the roadside perception algorithm generally includes:

[0004] 1. Manually draw the electronic fence using software tools such as QG IS;

[0005] 2. Deploy the drawn electronic fence into the autonomous driving system;

[0006] 3. Based on the autonomous driving system, configure a threshold value for triggering an early warning for the traffic event to be tested;

[0007] 4. It is necessary to simulate and trigger various traffic events by manually driving real vehicles (running real value vehicles).

[0008] 5. When the autonomous driving system detects a traffic incident and triggers an early warning, it is necessary to review and verify the traffic incident and early warning situation by manually processing logs or by redeveloping an early warning platform.

[0009] However, the inventors have discovered that there are at least the following technical problems in the related art: the method of simulating and triggering various traffic events by manually driving a real vehicle (running a real vehicle) has safety hazards, because actual driving may encounter unpredictable situations, increasing the risk of accidents. Summary of the invention

[0010] One purpose of the present application is to provide a simulation test method based on an autonomous driving vehicle, at least to solve the technical problem of potential safety hazards in the related art of simulating and triggering various traffic incidents by manually driving a real vehicle (running a real value vehicle).

[0011] To achieve the above objectives, some embodiments of the present application provide the following aspects:

[0012] In the first aspect, some embodiments of the present application also provide a simulation test method based on an autonomous driving vehicle, the method comprising: obtaining location data of the electronic fence; obtaining threshold data configured based on the type of traffic event; automatically simulating vehicle driving according to the location data and the threshold data to obtain trajectory data; and performing a simulation test according to the trajectory data.

[0013] In a second aspect, some embodiments of the present application further provide an electronic device, comprising: one or more processors; and a memory storing computer program instructions, wherein the computer program instructions, when executed, cause the processor to perform the steps of the method described above.

[0014] In a third aspect, some embodiments of the present application further provide a computer-readable medium having computer program instructions stored thereon, wherein the computer program instructions can be executed by a processor to implement the method as described above.

[0015] In a fourth aspect, some embodiments of the present application further provide a computer program product, comprising a computer program / instruction, which implements the steps of the method described above when executed by a processor.

[0016] Compared with the related art, in the solution provided in the embodiment of the present application, the location data of the electronic fence is obtained; the threshold data configured based on the type of traffic event is obtained; the vehicle driving is automatically simulated according to the location data and the threshold data to obtain the trajectory data; and the simulation test is performed according to the trajectory data. Since in the embodiment of the present application, the vehicle driving is automatically simulated according to the location data and the threshold data, the trajectory data is obtained, and the simulation test is performed according to the trajectory data, there is no need for the traditional manual driving method to perform the simulation test, thereby solving the technical problem of the safety hazard in the related art that the method of simulating and triggering various traffic events by manually driving a real vehicle can be solved. In addition, since the autonomous driving vehicle will encounter various traffic conditions on the actual road, a large number of simulation tests need to be performed in multiple scenarios such as different intersections and sections in actual applications. If the method of the related art is adopted, the efficiency of the simulation test is low. In the embodiment of the present application, different traffic events can be flexibly configured and simulated to automatically simulate vehicle travel according to the position data and the threshold data to generate corresponding trajectory data. It can be seen that compared with the related technology, the embodiment of the present application not only improves the safety of the test, but also helps to improve the efficiency of the test; and because the embodiment of the present application can realize automated simulation testing, it can reduce manual intervention and reduce the impact of time and personnel limitations on simulation testing, thereby further improving the accuracy and efficiency of the test. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0018] Figure 1 An exemplary flow chart of a simulation test method based on an autonomous driving vehicle provided according to some embodiments of the present application;

[0019] Figure 2 This is an exemplary flow chart of step S101 in a simulation test method based on an autonomous driving vehicle provided according to some embodiments of the present application;

[0020] Figure 3 This is an exemplary flow chart of step S102 in a simulation test method based on an autonomous driving vehicle provided according to some embodiments of the present application;

[0021] Figure 4 This is an exemplary flow chart of step S103 in a simulation test method based on an autonomous driving vehicle provided according to some embodiments of the present application;

[0022] Figure 5This is a schematic diagram of an exemplary structure provided according to some embodiments of the present application. Specific embodiments

[0023] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0024] First embodiment

[0025] The first embodiment of the present application relates to a simulation test method based on an autonomous driving vehicle. Figure 1 As shown, the method may include the following steps:

[0026] Step S101, obtaining the location data of the electronic fence;

[0027] Step S102, obtaining threshold data configured based on the type of traffic event;

[0028] Step S103, automatically simulating vehicle travel according to the position data and the threshold data to obtain trajectory data;

[0029] Step S104: performing a simulation test according to the trajectory data.

[0030] An embodiment of the present application provides an automated testing tool, which is used to execute the steps described above. The following text takes the example of the automated testing tool being specifically deployed in an autonomous driving system to describe each of the above steps in detail.

[0031] With respect to step S101, specifically, in some examples, the relevant personnel can determine the location data used to characterize the electronic fence based on the specific application scenario presented in the display interface of the electronic device, and the electronic fence is used as a virtual geographical boundary to define the area where the autonomous driving vehicle triggers the traffic event. The relevant personnel can deploy the electronic fence at a preset position of the display interface by dragging, clicking, etc., and then the electronic device can obtain the location data of the electronic fence according to the position.

[0032] For step S102, specifically, in some examples, the traffic event refers to an event that may affect traffic flow, road safety or road use efficiency in a road traffic system. Different traffic events may correspond to the same or different types, and the type of each traffic event usually refers to a specific situation or problem. Exemplarily, the type of the traffic event may include but is not limited to high-speed events, low-speed events, lane change events, etc. Relevant personnel can use the automated test tool provided in the embodiment of the present application to select the type of traffic event, and configure parameters according to the type of traffic event, and the value of the parameter is the threshold data. The threshold data is used to determine the triggering condition of the traffic event. Exemplarily, assuming that the type of the traffic event is a high-speed event, the threshold data configured based on the speeding event can be 5 meters / second (m / s), so that when the speed exceeds 5 meters / second, the early warning of the high-speed event is triggered. Assuming that the type of the traffic event is a low-speed event, the threshold data configured based on the low-speed event can be 1 meter / second (m / s), so that when it is less than 1 meter / second, the early warning of the low-speed event is triggered. It should be noted that the limiting object of the threshold data may be not only speed but also driving time, etc., and the embodiment of the present application does not make any specific limitation on this.

[0033] Regarding step S103, specifically, in some examples, the automated testing tool can automatically simulate the vehicle driving according to the corresponding type of traffic event based on the position data and the threshold data, and generate trajectory data; the trajectory data is the data corresponding to the dynamic situation of the autonomous driving vehicle in the real traffic environment. For example, assuming that the type of the traffic event is a high-speed event, the automated testing tool will automatically simulate the vehicle driving according to the position data and the threshold data, so that the driving speed of the autonomous driving vehicle exceeds the threshold data (such as 5 meters / second), and generate trajectory data, thereby triggering an early warning of the high-speed event. Among them, the automated testing tool automatically simulates the vehicle driving according to the corresponding type of traffic event in the form of an interface.

[0034] Regarding step S104, specifically, in some examples, when the trajectory data meets the triggering condition represented by the threshold data, the system will consider that a corresponding traffic event has occurred, thereby triggering a corresponding warning. Exemplarily, after the corresponding warning is triggered, the system can automatically display the warning information. For example, the warning information can be automatically displayed through interface prompts, sound alarms, etc., so that relevant personnel can understand the situation in a timely manner and take action.

[0035] Optionally, in some embodiments, the automated testing tool can automatically analyze the triggered traffic events to display early warnings and conduct event review analysis on different types of traffic events, evaluate the severity of the traffic events, the possible impact of the traffic events, etc., obtain analysis results, and perform corresponding processing based on the analysis results. For example, the corresponding processing can be: recording events, notifying relevant personnel, or automatically adjusting traffic flow. It can be understood that in the related art, manual analysis and verification of early warning events are required, which is not only time-consuming, but also requires a large amount of human resources. In the embodiments of the present application, the manual processing and analysis of traffic events is replaced by automated testing tools to uniformly process and analyze triggered traffic events, which can reduce dependence on human resources and help improve the speed and accuracy of analyzing and verifying early warning events.

[0036] It is not difficult to find that, compared with the related art, in the solution provided in the embodiment of the present application, by obtaining the position data of the electronic fence; obtaining the threshold data configured based on the type of traffic event; automatically simulating vehicle travel according to the position data and the threshold data, obtaining trajectory data; and performing simulation tests according to the trajectory data. Since in the embodiment of the present application, by automatically simulating vehicle travel according to the position data and the threshold data, trajectory data, and performing simulation tests according to the trajectory data, there is no need for traditional manual driving methods to perform simulation tests, thereby solving the technical problem of safety hazards in the related art, which is that the method of simulating and triggering various traffic events by manually driving real vehicles. In addition, since autonomous driving vehicles will encounter various traffic conditions on actual roads, a large number of simulation tests need to be performed in multiple scenarios such as different intersections and sections in actual applications. If the method of the related art is adopted, the efficiency of the simulation test is low. In the embodiment of the present application, different traffic events can be flexibly configured and simulated to automatically simulate vehicle travel according to the position data and the threshold data to generate corresponding trajectory data. It can be seen that compared with the related technology, the embodiment of the present application not only improves the safety of the test, but also helps to improve the efficiency of the test; and because the embodiment of the present application can realize automated simulation testing, it can reduce manual intervention and reduce the impact of time and personnel limitations on simulation testing, thereby further improving the accuracy and efficiency of the test.

[0037] Second embodiment

[0038] The second embodiment of the present application relates to a simulation test method based on an autonomous driving vehicle. The second embodiment is an improvement on the first embodiment, and the specific improvement is: in the second embodiment of the present application, a specific implementation method for obtaining the location data of the electronic fence is provided.

[0039] Specifically, in the embodiment of the present application, the step of obtaining the location data of the electronic fence, that is, step S101, may further include the following steps: Figure 2 As shown:

[0040] Step S1011, displaying at least one electronic fence for the user to select according to the type of the traffic event, and obtaining a target electronic fence; wherein the type of the traffic event and the electronic fence are associated with each other;

[0041] Step S1012, deploying the target electronic fence at a preset position;

[0042] Step S1013, obtaining the location data of the electronic fence according to the preset location.

[0043] Specifically, relevant personnel can use the automated testing tool provided in the embodiment of the present application to select the type of traffic event. According to the type of traffic event, the automated testing tool can provide a display interface, and the display interface displays at least one electronic fence associated with the type of traffic event. These electronic fences are pre-drawn, so that relevant personnel can intuitively view, manage, and select the electronic fence. Since the electronic fence is pre-drawn, relevant personnel can scale or expand the electronic fence to meet actual needs; if simply scaling or expanding the electronic fence cannot meet actual needs, relevant personnel can directly adjust and modify the electronic fence based on the shape that is closest to the required electronic fence, without the need for relevant personnel to redraw the electronic fence from scratch.

[0044] For example, different traffic event types may correspond to different types of electronic fences. For example, corresponding to traffic event type 1, the automated testing tool may automatically display three types of electronic fences, A\B\C; corresponding to traffic event type 2, the automated testing tool may automatically display four types of electronic fences, C\D\E\F.

[0045] Specifically, the electronic fence selected by the relevant personnel is the target electronic fence. The relevant personnel can deploy the target electronic fence at a preset position by dragging, clicking, etc., and then, the position data of the electronic fence can be obtained according to the preset position.

[0046] Optionally, in some embodiments, the step of deploying the target electronic fence at a preset position, i.e., step S1012, may further include the following steps:

[0047] Step S10121, displaying lane information of the road according to the map data;

[0048] Step S10122, determining a point address according to the lane information; the point address is used to indicate the deployment position of the target electronic fence;

[0049] Step S10123: deploy the target electronic fence at a preset position according to the point address.

[0050] For example, the map data may be obtained based on a high-precision map; based on the map data, the lane information of the displayed road may include but is not limited to: the number of lanes, lane lines, lane directions, traffic signs, etc. By using high-precision maps, the automated testing tool can more accurately display the lane layout of the road, helping relevant personnel to more accurately deploy electronic fences.

[0051] Exemplarily, the relevant personnel can determine the point address according to the lane information. For example, the relevant personnel can directly enter or select a specific point address in the automated testing tool. In some examples, the relevant personnel click on several point addresses on the lane information of the displayed road, and the automated testing tool can automatically load the target electronic fence according to the point address. In this way, the target electronic fence is automatically deployed at a preset position in the autonomous driving system.

[0052] In addition, the user can perform visual management on the drawn target electronic fence. For example, the user can intuitively view the shape, size, status, etc. of the target electronic fence.

[0053] It can be understood that in the related art, the drawing, visual display and deployment of multiple electronic fences are technically and operationally complex. In the embodiment of the present application, instead of using third-party tools to draw multiple electronic fences and deploy them manually, it is now possible to use automated testing tools to visualize the drawing and management of electronic fences and deploy them in real time, reducing the complexity of deployment and the complexity of drawing fences. Simply put, in the embodiment of the present application, automated testing tools can be used to quickly draw and deploy multiple electronic fences, dynamically adjust and optimize the target electronic fence deployment based on traffic events, reduce the time and complexity of manually drawing and deploying electronic fences, and further improve the efficiency and accuracy of testing work.

[0054] Third embodiment

[0055] The third embodiment of the present application relates to a simulation test method based on an autonomous driving vehicle. The third embodiment is an improvement on the first embodiment, and the specific improvement is: in the third embodiment of the present application, a specific implementation method for obtaining threshold data configured based on the type of traffic event is provided.

[0056] Specifically, in the embodiment of the present application, the step of obtaining the threshold data configured based on the type of traffic event, i.e., step S102, may further include the following steps: Figure 3 As shown:

[0057] Step S1021, determining a trigger condition corresponding to the type of the traffic event;

[0058] Step S1022: Acquire data information configured based on the trigger condition to obtain the threshold data according to the data information.

[0059] Specifically, in some examples, the trigger condition may include but is not limited to: the speed of the autonomous driving vehicle, the driving time of the autonomous driving vehicle (whether it is a restricted driving time period for the autonomous driving vehicle), etc., and data information configured based on the trigger condition is obtained, so that the threshold data can be determined based on the data information. Optionally, in some embodiments, the type of traffic event may include but is not limited to at least one of the following:

[0060] Abnormal parking type, road congestion type, pedestrian intrusion type, non-motor vehicle intrusion type, autonomous driving vehicle reversing type, solid line lane change type, speeding type, underspeed type;

[0061] Correspondingly, the simulation test performed according to the trajectory data is specifically as follows:

[0062] According to the trajectory data, at least one of the following simulation tests is performed:

[0063] Abnormal parking detection, road congestion detection, pedestrian intrusion detection, non-motor vehicle intrusion detection, autonomous driving vehicle reverse detection, solid line lane change detection, speeding detection, and low speed detection.

[0064] Among them, the abnormal parking detection is used to detect the parking behavior of autonomous driving vehicles in prohibited parking areas; the road congestion detection is used to identify congestion on the road; the pedestrian intrusion detection is used to monitor the entry of pedestrians into the driving area of ​​autonomous driving vehicles; the non-motor vehicle intrusion detection is used to detect the entry of non-motorized autonomous driving vehicles such as bicycles and electric vehicles into motor vehicle lanes or other prohibited areas; the autonomous driving vehicle reversing detection is used to identify the behavior of autonomous driving vehicles reversing on the road; the solid line lane change detection is used to monitor the behavior of autonomous driving vehicles changing lanes in solid line areas where lane changes are not allowed; the speeding detection is used to detect whether the autonomous driving vehicle exceeds the prescribed speed limit; the low speed detection is used to monitor whether the autonomous driving vehicle is driving below the prescribed speed.

[0065] Among them, relevant personnel can add new types of traffic events in the automated testing tool according to their own testing requirements, so that the automated testing tool can automatically perform corresponding simulation tests according to various types of traffic events.

[0066] It should be noted that the third embodiment of the present application may also be an improvement based on the second embodiment.

[0067] It is not difficult to find that in an embodiment of the present application, by determining the trigger condition corresponding to the type of the traffic event; obtaining data information configured based on the trigger condition to obtain the threshold data according to the data information, a specific implementation method for obtaining threshold data configured based on the type of traffic event is provided.

[0068] Fourth embodiment

[0069] The fourth embodiment of the present application relates to a simulation test method based on an autonomous driving vehicle. The fourth embodiment is an improvement on the first embodiment, and the specific improvement is: providing a specific implementation method for automatically simulating vehicle driving according to the position data and the threshold data to obtain trajectory data.

[0070] Specifically, in the embodiment of the present application, the vehicle driving is automatically simulated according to the position data and the threshold data to obtain the trajectory data, that is, step S103 can further include the following steps: Figure 4 As shown:

[0071] Step S1031, determining a target data field based on the position data and the threshold data; the target data field includes at least the position coordinates of the electronic fence, the initial position coordinates of the autonomous driving vehicle, the speed data of the autonomous driving vehicle, and the heading angle data of the autonomous driving vehicle;

[0072] Step S1032, automatically simulating vehicle driving according to the target data field to obtain trajectory data.

[0073] Optionally, in some embodiments, the automatic simulation of vehicle driving according to the target data field to obtain trajectory data, that is, step S1032, may further include the following steps:

[0074] Step S10321, determining a position change of the autonomous driving vehicle in each time step according to the initial position coordinates of the autonomous driving vehicle, the speed data of the autonomous driving vehicle, and the heading angle data of the autonomous driving vehicle;

[0075] Step S10322, determining a positional relationship between the autonomous driving vehicle and the area represented by the electronic fence according to the position coordinates of the electronic fence and the position change of the autonomous driving vehicle in each time step;

[0076] Step S10323, obtaining trajectory data according to the position relationship and the position change of the autonomous driving vehicle in each time step.

[0077] Exemplarily, the new position of the autonomous driving vehicle at the end of each time step (such as every second) can be calculated based on the target data field, so that the position change of the autonomous driving vehicle in each time step can be determined based on the initial position coordinates and the new position of the autonomous driving vehicle, and then the position change of the autonomous driving vehicle in each time step obtained during the simulation process is compared with the position coordinates (boundary coordinates) of the electronic fence to determine the position relationship between the autonomous driving vehicle and the area represented by the electronic fence. Among them, the position relationship between the autonomous driving vehicle and the area represented by the electronic fence may include: the autonomous driving vehicle enters the area represented by the electronic fence, the autonomous driving vehicle exits the area represented by the electronic fence, or the autonomous driving vehicle passes through the area represented by the electronic fence, etc., so that the trajectory data can be obtained based on the position relationship and the position change of the autonomous driving vehicle in each time step.

[0078] It should be noted that the fourth embodiment of the present application may also be an improvement based on the second embodiment and / or the third embodiment.

[0079] It is not difficult to find that in an embodiment of the present application, the target data field is determined based on the position data and the threshold data; the target data field includes at least the position coordinates of the electronic fence, the initial position coordinates of the autonomous driving vehicle, the speed data of the autonomous driving vehicle and the heading angle data of the autonomous driving vehicle, and the vehicle driving is automatically simulated according to the target data field to obtain trajectory data, thereby providing a specific implementation method for automatically simulating vehicle driving according to the position data and the threshold data to obtain trajectory data.

[0080] The step division of the above methods is only for the purpose of clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent; adding insignificant modifications to the algorithm or process or introducing insignificant designs without changing the core design of the algorithm and process are all within the scope of protection of this patent.

[0081] Combining the above first to fourth embodiments, it is not difficult to find that in the embodiments of the present application, as the iteration speed of the roadside perception algorithm becomes faster and faster, the applicant cleverly and innovatively combines the simulation test and the automated testing tool. Through the automated testing tool, the roadside perception algorithm of the autonomous driving vehicle is extensively tested, so that it can safely and efficiently simulate a variety of different road environments and traffic conditions, triggering different types of traffic events, which is conducive to ensuring the safety and reliability of autonomous driving vehicles in various complex traffic environments. And it can greatly reduce the time and cost of manual input, reduce the workload of manual observation and analysis of warning events, and improve testing efficiency.

[0082] The automated testing tool can fully cover the entire simulation test process from the triggering of traffic events to early warning detection and subsequent location investigation. The triggering of traffic events means that when the roadside perception system detects an event that may affect traffic safety, the system will automatically issue an early warning; the early warning detection means that after the early warning is issued, the automated testing tool can detect whether these early warnings are correctly received and processed; the subsequent location investigation means that after the early warning is detected, the specific location and cause of the problem need to be further determined so that corresponding measures can be taken.

[0083] It can be understood that the use of automated testing tools can also help accelerate the verification and optimization process of the roadside perception algorithm, so that autonomous driving technology can adapt to changing traffic environments more quickly and improve its reliability and safety in practical applications.

[0084] In addition, some embodiments of the present application also provide an electronic device. The electronic device may be a digital computer in various forms, such as a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, etc. The electronic device may also be a mobile device in various forms, such as a personal digital processing, a cellular phone, a smart phone, a wearable device, and other similar computing devices.

[0085] The electronic device includes: one or more processors; and a memory storing computer program instructions, wherein when the computer program instructions are executed, the processor executes the steps of the method provided in any one or more of the above embodiments. Figure 5 An exemplary structural diagram of the electronic device is disclosed. Figure 5As shown, the electronic device includes: one or more processors 1101, memory 1102, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some other embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Among them, the components shown in this article, their connections and relationships, and their functions are only used as examples, and are not intended to limit the implementation of the present application described and / or required herein.

[0086] The electronic device may further include: an input device 1103 and an output device 1104. The processor 1101, the memory 1102, the input device 1103 and the output device 1104 may be connected via a bus or other means. Figure 5 The example of connecting through bus is taken in the following.

[0087] The input device 1103 can receive input digital or character information, and generate key signal input related to the user settings and function control of the electronic device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator rod, one or more mouse buttons, a track ball, a joystick and other input devices. The output device 1104 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The display device may include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display and a plasma display. In some embodiments, the display device may be a touch screen.

[0088] To provide interaction with a user, the electronic device may be a computer. The computer has: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball), through which the user can provide input to the computer. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0089] In the embodiments of the present application, a computer program / instruction is stored on a computer-readable medium, and when the computer program / instruction is executed by a processor, the steps of the method provided by any one or more of the above embodiments are implemented. The computer-readable medium may be included in the electronic device described in the above embodiments; or it may exist independently without being assembled into the device. The above computer-readable medium carries one or more computer-readable instructions.

[0090] The memory 1102 can be used as a non-transient computer-readable storage medium, which can be used to store non-transient software programs, non-transient computer executable programs and modules. The processor 1101 executes various functional applications and data processing of the server by running the non-transient software programs, instructions and modules stored in the memory 1102, so as to implement the program instructions / modules corresponding to the method provided by any one or more embodiments in the embodiments of the present application.

[0091] The memory 1102 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 1102 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 1102 may optionally include a memory remotely arranged relative to the processor 1101, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0092] It should be noted that the computer-readable medium described in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0093] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, modules of programs or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0094] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet).

[0095] In the above-described embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. For example, it can be implemented by using an application specific integrated circuit (ASIC), a general-purpose computer or any other similar hardware device. In certain embodiments, the software program of the present application can be executed by a processor to implement the above steps or functions. Similarly, the software program of the present application (including relevant data structures) can be stored in a computer-readable recording medium, for example, a RAM memory, a magnetic or optical drive or a floppy disk and similar devices. In addition, some steps or functions of the present application can be implemented by hardware, for example, as a circuit that cooperates with a processor to perform each step or function.

[0096] The computer program product provided in the embodiment of the present application includes one or more computer programs / instructions, and when the computer program / instructions are executed by the processor, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrations. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive so lid state di sk (SSD)), etc.

[0097] The flow chart or block diagram in the accompanying drawings shows the possible architecture, function and operation of the equipment, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated system for hardware that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0098] The scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present application. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the device claim may also be implemented by one unit or device through software or hardware. The words "first", "second", etc. are only used to distinguish the description, and do not indicate any particular order, nor can they be understood as indicating or implying relative importance.

[0099] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily mention changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims, and the above embodiments should be regarded as exemplary and non-restrictive.

Claims

1. A simulation test method based on an autonomous driving vehicle, characterized in that: The method comprises: Get the location data of the electronic fence; Obtain threshold data configured based on the type of traffic event; Automatically simulate vehicle travel according to the position data and the threshold data to obtain trajectory data; A simulation test is performed according to the trajectory data.

2. The method according to claim 1, characterized in that The obtaining of the location data of the electronic fence comprises: According to the type of traffic event, at least one electronic fence is displayed for the user to select, and a target electronic fence is obtained; wherein the type of the traffic event and the electronic fence are associated with each other; Deploy the target electronic fence at a preset position; According to the preset position, the position data of the electronic fence is obtained.

3. The method according to claim 2, characterized in that The deploying the target electronic fence at a preset position includes: Display lane information of the road based on map data; Determine a point address according to the lane information; the point address is used to indicate the deployment position of the target electronic fence; According to the point address, the target electronic fence is deployed at a preset position.

4. The method according to claim 1, characterized in that: The obtaining of threshold data configured based on the type of traffic event includes: Determine a trigger condition corresponding to the type of the traffic event; Acquire data information configured based on the trigger condition to obtain the threshold data according to the data information.

5. The method according to claim 1, characterized in that: The type of the traffic event includes at least one of the following: Abnormal parking type, road congestion type, pedestrian intrusion type, non-motor vehicle intrusion type, autonomous driving vehicle reversing type, solid line lane change type, speeding type, underspeed type; Correspondingly, the simulation test performed according to the trajectory data is specifically as follows: According to the trajectory data, at least one of the following simulation tests is performed: Abnormal parking detection, road congestion detection, pedestrian intrusion detection, non-motor vehicle intrusion detection, autonomous driving vehicle reverse detection, solid line lane change detection, speeding detection, and low speed detection.

6. The method according to any one of claims 1 to 5, characterized in that The automatically simulating vehicle travel according to the position data and the threshold data to obtain trajectory data comprises: Based on the position data and the threshold data, a target data field is determined; the target data field includes at least the position coordinates of the electronic fence, the initial position coordinates of the autonomous driving vehicle, the speed data of the autonomous driving vehicle, and the heading angle data of the autonomous driving vehicle; The vehicle driving is automatically simulated according to the target data field to obtain trajectory data.

7. The method according to claim 6, characterized in that The automatically simulating vehicle driving according to the target data field to obtain trajectory data includes: Determine a position change of the autonomous driving vehicle within each time step according to the initial position coordinates of the autonomous driving vehicle, the speed data of the autonomous driving vehicle, and the heading angle data of the autonomous driving vehicle; Determining a positional relationship between the autonomous driving vehicle and the area represented by the electronic fence according to the position coordinates of the electronic fence and the position change of the autonomous driving vehicle in each time step; According to the position relationship and the position change of the autonomous driving vehicle in each time step, trajectory data is obtained.

8. An electronic device, characterized in that: The electronic device comprises: one or more processors; and A memory storing computer program instructions, which, when executed, cause the processor to perform the steps of the method as claimed in any one of claims 1 to 7.

9. A computer readable medium having a computer program / instructions stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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