Vehicle testing method and device, electronic equipment and vehicle

Abstract

The invention relates to a vehicle testing method and device, electronic equipment and a vehicle, and relates to the technical field of tests.The method comprises the steps that firstly, real vehicle motion state information of the vehicle is obtained, and target simulation scene information corresponding to the real vehicle motion state information is determined; inputting the real vehicle motion state information and the target simulation scene information into an AEB algorithm module to obtain an output result of the AEB algorithm module; and analyzing the output result, and obtaining a test result of whether the vehicle collides with the obstacle target in the target simulation scene information when the vehicle runs in the target simulation scene information according to the real vehicle motion state information. According to the technical scheme, when the user selects the AEB algorithm verification mode, the system obtains the simulation scene meeting the standard scene condition corresponding to the AEB algorithm module test, so that the vehicle can be tested in the accurate C-NCAP standard scene, the vehicle test cost is reduced, and meanwhile, the vehicle detection accuracy is improved.

Description

Technical Field

[0001] The present application relates to the field of testing technology, and in particular to a vehicle testing method, device, electronic equipment and vehicle. Background Art

[0002] The issue of safe driving has become an important focus of vehicle design, and active safety and passive safety have become the technologies that automobile manufacturers are most concerned about. The Autonomous Emergency Braking (AEB) system is an active safety technology for automobiles. Through the built-in AEB algorithm, the AEB system can actively brake when the distance to the vehicle in front or pedestrians is less than the safe distance, thus avoiding or reducing the occurrence of rear-end collisions.

[0003] At present, the AEB algorithm module is verified by real vehicle testing. Specifically, high-precision fake people and fake cars are used to build the China-New Car Assessment Program (C-NCAP) standard scenario, and the test is carried out in the actual driving state of the real vehicle.

[0004] However, the high-precision fake people and fake cars in the real vehicle test method are expensive, which increases the cost of vehicle testing. In addition, this artificially constructed scene will also have errors and cannot fully meet the requirements of the C-NCAP standard scene, affecting the accuracy of vehicle testing. Summary of the invention

[0005] In view of this, the present application provides a vehicle testing method, device, electronic equipment and vehicle, the main purpose of which is to improve the technical problems that the current existing vehicle testing methods are high in cost, the standard scenarios constructed have errors, and the vehicle testing accuracy is low.

[0006] In a first aspect, the present application provides a vehicle testing method, comprising:

[0007] Acquire the actual vehicle motion state information of the vehicle, and determine the target simulation scene information corresponding to the actual vehicle motion state information, wherein the target simulation scene information meets the standard scene conditions corresponding to the AEB algorithm module test and the target simulation scene information includes an obstacle target;

[0008] Inputting the real vehicle motion state information and the target simulation scene information into the AEB algorithm module to obtain an output result of the AEB algorithm module;

[0009] The output result is analyzed to obtain a test result of whether the vehicle will collide with an obstacle target in the target simulation scene information when traveling in the target simulation scene information with the real vehicle motion state information.

[0010] In a second aspect, the present application provides a vehicle testing device, comprising:

[0011] An acquisition module is configured to acquire real vehicle motion state information of the vehicle, and determine target simulation scene information corresponding to the real vehicle motion state information, wherein the target simulation scene information meets the standard scene conditions corresponding to the AEB algorithm module test and the target simulation scene information includes obstacle targets;

[0012] A calculation module is configured to input the real vehicle motion state information and the target simulation scene information into the AEB algorithm module to obtain an output result of the AEB algorithm module;

[0013] The test module is configured to parse the output result and obtain a test result of whether the vehicle will collide with an obstacle target in the target simulation scene information when traveling in the target simulation scene information with the real vehicle motion state information.

[0014] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the vehicle testing method described in the first aspect.

[0015] In a fourth aspect, the present application provides an electronic device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor implements the vehicle testing method described in the first aspect when executing the computer program.

[0016] In a fifth aspect, the present application provides a vehicle, comprising: the vehicle testing device as described in the second aspect, or the electronic device as described in the fourth aspect.

[0017] By means of the above technical scheme, the present application provides a vehicle testing method, device, electronic device and vehicle. Compared with the current existing vehicle testing methods, the present application can save the real scene cost, build an error-free standard scene through simulation, and improve the accuracy of vehicle detection. Specifically, firstly, the real vehicle motion state information of the vehicle is obtained, and the target simulation scene information corresponding to the real vehicle motion state information is determined, wherein the target simulation scene information meets the standard scene conditions corresponding to the AEB algorithm module test and the target simulation scene information includes obstacle targets; then the real vehicle motion state information and the target simulation scene information are input into the AEB algorithm module for calculation to obtain the output result of the AEB algorithm module; then the output result is analyzed to obtain the test result of whether the vehicle will collide with the obstacle target in the target simulation scene information when driving in the target simulation scene information with the real vehicle motion state information. By applying the technical solution of the present application, when the user selects the AEB algorithm verification mode, the system automatically obtains a simulation scene that meets the standard scene conditions corresponding to the AEB algorithm module test, and inputs it into the AEB algorithm module together with the actual vehicle motion state information, so that the vehicle can be tested in a precise standard test scene (such as C-NCAP standard scene, etc.), reducing the vehicle testing cost while improving the accuracy of vehicle detection.

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0021] Figure 1 A schematic diagram of a vehicle detection method provided in an embodiment of the present application is shown;

[0022] Figure 2 A schematic diagram showing an example provided by an embodiment of the present application;

[0023] Figure 3A schematic diagram showing a flow chart of another vehicle detection method provided in an embodiment of the present application is shown;

[0024] Figure 4 A schematic structural diagram of a vehicle detection device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0025] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the scheme of the present application will be further described below. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0026] In order to improve the current technical problems of high vehicle testing costs and errors in the built standard scenarios, resulting in low vehicle testing accuracy. This embodiment provides a vehicle testing method, such as Figure 1 As shown, the method includes:

[0027] Step 101: Acquire actual vehicle motion state information of a vehicle, and determine target simulation scene information corresponding to the actual vehicle motion state information.

[0028] Among them, the target simulation scene information meets the standard scene conditions corresponding to the AEB algorithm module test and the target simulation scene information includes obstacle targets, such as C-NCAP standard scenes.

[0029] Optionally, the actual vehicle motion state information of the vehicle may include: speed, deceleration, rate of change of deceleration, actuator response characteristics, braking distance, etc.

[0030] In this embodiment, the real vehicle test is used to ensure that the vehicle's motion state is real and to provide real vehicle signal input for AEB algorithm verification. For example, another existing vehicle testing method currently uses a full simulation method, specifically to establish a virtual vehicle through simulation, and at the same time to build a virtual C-NCAP standard test scene, and use the virtual vehicle and standard test scene to perform vehicle testing. Since the C-NCAP scene is obtained through simulation, the motion trajectory and speed of the vehicle and pedestrian targets can be completely consistent with the C-NCAP standard scene. However, the computer virtual vehicle motion state is based on the vehicle kinematic model, and the current most advanced vehicle kinematic model cannot accurately calculate the vehicle speed, deceleration, deceleration change rate, actuator response characteristics, braking distance and other vehicle motion state data. Therefore, from the actual effect, the error of the motion trajectory of the virtual vehicle established by simulation will be relatively large. For example, depending on the braking distance, the error range of the motion trajectory of the virtual vehicle in the C-NCAP scene is 1 to 6m, and such an error is sufficient to affect the accuracy of AEB algorithm verification. Compared with the above-mentioned existing full simulation vehicle testing method, this embodiment can ensure that the vehicle's motion state is real and improve the accuracy of vehicle detection.

[0031] Exemplarily, the target simulation scene information corresponding to the real vehicle motion state information is a C-NCAP standard scene (such as a C-NCAP2024 standard scene) built through simulation. C-NCAP is the first version of the China New Car Assessment Program (C-NCAP) officially released by the China Automotive Technology and Research Center on March 2, 2006 in order to promote the healthy development of China's automobile industry and accelerate the globalization process of the domestic automobile market. C-NCAP conducts all-round safety performance tests on vehicles with stricter and more comprehensive requirements, including occupant protection, pedestrian protection, active safety, etc., so as to provide consumers with more systematic and objective vehicle safety information and promote automobile companies to continuously improve the safety performance of the whole vehicle. New models purchased on the market will be tested for collision safety performance according to stricter and more comprehensive requirements than the existing mandatory standards in my country, and the evaluation results will be divided into star ratings. C-NCAP has largely borrowed from the European Euro C-NCAP collision standard. Through testing, consumers can intuitively see the safety performance of the car, and it also plays a supervisory role in the production of enterprises.

[0032] For another example, C-NCAP requires a vehicle model to be subjected to collision tests such as a frontal collision with a rigid fixed barrier at a speed of 50 km / h with a 100% overlap rate, a frontal offset collision with a deformable barrier at a speed of 64 km / h with a 40% overlap rate, and a side collision with a deformable movable barrier at a speed of 50 km / h. The collision safety performance is graded according to the scores obtained in the test, and the results of the C-NCAP test can be used as a reference for vehicle safety performance.

[0033] Specifically, Figure 2 As shown, in this embodiment, the "virtual scene generator" is a software algorithm in the Full Self-Driving (FSD) controller, which consists of a "scene editor" and a "scene update" module. The "scene editor" sets the initial attributes (position, speed, direction, shape, target type, etc.) and action events (for example, when the vehicle approaches, the target moves from the initial position along the set route to a certain position) of obstacles and lane lines through manual editing or file import according to the test specifications specified in the C-NCAP regulations. The initial attributes and action events of obstacles and lane lines are obtained from the "scene editor", and the attributes (position, speed, direction, etc.) of obstacles and lane lines are updated according to the actual vehicle motion state transmitted from the "chassis controller", and action events such as obstacles are triggered when the set conditions are met.

[0034] Specifically, before starting the vehicle inspection, the vehicle must arrive at the test starting point and remain completely still. The "start" command is manually issued to enter the AEB algorithm verification mode. After that, the vehicle starts moving for testing. The "virtual scene generator" will continuously update the properties of virtual obstacles and lane lines according to the vehicle's motion status, and trigger the obstacle action when the conditions are met. The AEB algorithm can then receive accurate virtual obstacle and lane line properties to complete the algorithm verification.

[0035] For example, the existing full-real vehicle testing equipment still has errors no matter how accurate it is. Algorithm verification requires the sensing input of target position, speed, and heading angle information, but the sensing detection has errors and cannot fully meet the C-NCAP standard scenario. This embodiment builds a C-NCAP standard scenario to simulate the movement trajectory and speed of vehicles and pedestrians, without spending a lot of time, energy, and money to build a real scene that meets the standard, and improves the accuracy of vehicle detection.

[0036] Step 102: input the real vehicle motion state information and the target simulation scene information into the AEB algorithm module to obtain an output result of the AEB algorithm module.

[0037] In this embodiment, AEB is mainly composed of three modules, including a control module (ECU), a distance measurement module, and a braking module. When a vehicle is equipped with this system, it will issue a warning or take different degrees of braking, or even completely stop the vehicle, depending on the situation, when the vehicle encounters an emergency, so as to avoid accidents or reduce accident losses, thereby improving vehicle safety. The AEB algorithm can determine whether the vehicle will collide with an obstacle and calculate the collision time, and then take corresponding measures based on the specific data. The two conditions required in the AEB algorithm verification stage are: the movement trajectory and speed of the vehicle and pedestrian targets are completely consistent with the CNCAP standard scene; the driving state of the vehicle is completely true. In this way, the AEB algorithm module verification results can be guaranteed to be more accurate.

[0038] For example, Figure 2 As shown in the figure, the "AEB algorithm module" needs to obtain two inputs corresponding to the conditions required in the algorithm verification phase: one is the obstacle target and lane line information sent by the "scene update", and the other is the actual vehicle motion status information (speed, deceleration, heading angle, etc. in the XYZ direction) sent by the "chassis controller". After receiving the two input information, the "AEB algorithm module" returns the deceleration request to the chassis controller and performs algorithm verification.

[0039] Specifically, the AEB algorithm module combines the vehicle's motion states such as speed, deceleration, rate of change of deceleration, actuator response characteristics, and braking distance with the properties of obstacles and lane lines in the corresponding simulation scene. When an obstacle movement is detected, a deceleration request is sent to the vehicle's chassis controller to brake or stop the vehicle, thereby ensuring vehicle safety.

[0040] Step 103, analyzing the output result of the AEB algorithm module, and obtaining a test result of whether the vehicle will collide with an obstacle target in the target simulation scene information when traveling in the target simulation scene information with the real vehicle motion state information.

[0041] For example, the test result of the AEB algorithm module determines whether the vehicle will collide with the obstacle target in the target simulation scene information when driving. During the test, the corresponding braking distance and braking time and other information can be calculated based on the deceleration, braking force and other data output by the AEB algorithm module to determine whether the vehicle will collide with the obstacle target. If it collides, it is determined that the AEB algorithm module test has failed. The system evaluates the AEB performance of the vehicle through these data and can generate a corresponding test report.

[0042] Compared with the existing vehicle tests, this embodiment can save the real scene cost, build an error-free standard scene by simulation, and improve the accuracy of vehicle detection. Specifically, first obtain the real vehicle motion state information of the vehicle, and determine the target simulation scene information corresponding to the real vehicle motion state information, wherein the target simulation scene information meets the standard scene conditions corresponding to the automatic emergency braking function AEB algorithm module test and the target simulation scene information includes obstacle targets; then input the real vehicle motion state information and the target simulation scene information into the AEB algorithm module for calculation to obtain the output result of the AEB algorithm module; then analyze the output result to obtain the test result of whether the vehicle will collide with the obstacle target in the target simulation scene information when driving in the target simulation scene information with the real vehicle motion state information. By applying the technical solution of this embodiment, when the user selects the AEB algorithm verification mode, the system obtains the simulation scene that meets the standard scene conditions corresponding to the AEB algorithm module test, and inputs it into the AEB algorithm module together with the real vehicle motion state information, so that the vehicle can be tested in a precise C-NCAP standard scene, reducing the scene construction cost and improving the accuracy of vehicle detection.

[0043] Further, as a refinement and extension of the above embodiment, in order to fully illustrate the specific implementation process of the method of this embodiment, this embodiment provides the following Figure 3 The specific method shown includes:

[0044] Step 201: Obtain the working mode of the AEB algorithm module.

[0045] If the working mode is the normal working mode, real scene information is obtained through the vehicle's perception module, and the real scene information is used to be input into the AEB algorithm module.

[0046] Specifically, Figure 2 As shown in the figure, when the "AEB algorithm module" is in normal mode, obstacle targets and lane line information are obtained through the "real scene active safety perception module". The "real scene active safety perception module" is composed of the "visual perception module", "lidar perception module", "radar perception module" and "fusion module". The AEB algorithm module receives the vehicle motion state output by the "chassis controller" and the real scene information output by the "real scene active safety perception module", so that the vehicle can use the AEB function normally in the real scene.

[0047] Step 202: If the working mode of the AEB algorithm module is set to the test working mode, the actual vehicle motion state information of the vehicle is obtained, and the target simulation scene information corresponding to the actual vehicle motion state information is determined.

[0048] Among them, the target simulation scene information meets the standard scene conditions corresponding to the AEB algorithm module test.

[0049] Specifically, Figure 2 As shown in the figure, the system adds a new input, namely the "virtual scene generator", to the original active safety algorithm link of the vehicle, which is switched through the mode selection switch so that the AEB verification algorithm can work on the real vehicle. The software provides a mode switch, namely "mode selection". When the "AEB algorithm module" is in the verification algorithm mode, the obstacle target and lane line information are switched to be obtained from the "virtual scene generator". When the verification algorithm mode is just entered, the scene is the default empty scene. By switching between the normal working mode and the test working mode through the switch, it is convenient to test the vehicle without affecting the normal driving of the vehicle in the real scene. The user can switch freely according to actual needs. For example, after testing a section of the journey, if the current road conditions are not good, the normal working mode can be selected to drive the vehicle normally; when the current intersection is good, the AEB algorithm module can be tested by switching back to the test working mode, and so on.

[0050] In some embodiments, determining the target simulation scene information corresponding to the real vehicle motion state information may specifically include: based on the real vehicle motion state information, obtaining the target simulation scene information that can cause the vehicle to collide with the obstacle target within a preset time period from various pre-edited simulation scene information. For example, first determining the current driving speed and direction of the vehicle from the real vehicle motion state information; and then obtaining the target simulation scene information that can cause the vehicle traveling at the driving speed and direction to collide with the obstacle target within a preset time period from various pre-edited simulation scene information.

[0051] Since the vehicle's motion state is real and updated in real time, the software algorithm needs to update the target simulation scenario that can achieve collision according to the different vehicle motion states from the C-NCAP standard scenario built in advance through simulation, so as to create conditions for verifying the AEB algorithm.

[0052] Step 203: input the actual vehicle motion state information and the target simulation scene information into the AEB algorithm module for calculation to obtain the output result of the AEB algorithm module.

[0053] In some embodiments, the target simulation scene information includes: simulated obstacle target information and lane line information. Accordingly, step 203 may specifically include: inputting the obstacle target information and lane line information, as well as the real vehicle motion state information, into the AEB algorithm module; the output result includes the judgment result of whether the vehicle will collide with the obstacle target, and the deceleration information when the vehicle will collide with the obstacle target.

[0054] For example, the target simulation scenario may be an event in which a vehicle is about to collide with an obstacle, a vehicle deviates from a lane line, a vehicle encounters a red light at an intersection, or a road surface is damaged.

[0055] In some examples, the obstacle target information includes: initial attribute information and action event information of the obstacle target; wherein, the initial attribute information of the obstacle target may include: the initial position, speed and moving direction of the obstacle target; the action event information of the obstacle target may include: the obstacle target moves from the initial position at the speed and moving direction along the set route to the target position, wherein the target position is the position reached by the vehicle after driving for the preset time based on the actual vehicle motion state information.

[0056] For example, obstacle targets can be vehicles, pedestrians, foreign objects, fixed facilities, etc.

[0057] Step 204: parse the output result of the AEB algorithm module to obtain a test result of whether the vehicle will collide with an obstacle target in the target simulation scene information when driving in the target simulation scene information with the actual vehicle motion state information.

[0058] In some embodiments, step 204 may specifically include: obtaining the current distance between the vehicle and the obstacle target; triggering the vehicle to brake according to the deceleration information, and obtaining the braking distance and braking time of the vehicle; if the braking distance is less than the current distance or the braking time is less than the preset time (which can be pre-set according to actual needs), it is determined that the vehicle will not collide with the obstacle target.

[0059] The vehicle is triggered to brake according to the deceleration information, and the braking distance and braking time of the vehicle are calculated; if it is determined that the vehicle will collide with an obstacle target based on the braking distance and braking time, it is determined that the AEB algorithm module test has failed; if it is determined that the vehicle will not collide with an obstacle target based on the braking distance and braking time, it is determined that the AEB algorithm module test has passed.

[0060] The C-NCAP standard scenario built through simulation will activate the AEB algorithm module through sensors to decelerate the vehicle and avoid collision when a collision event is triggered, just like in the real scenario. If the AEB algorithm module calculates the corresponding deceleration information of the vehicle in the scenario, it can further analyze whether the output deceleration information meets the vehicle safety requirements of the current situation; if the deceleration information fails to be output, it means that the AEB algorithm module cannot automatically brake when encountering an emergency obstacle and does not meet the vehicle test standards.

[0061] Furthermore, in order to improve the accuracy of vehicle testing and meet vehicle testing requirements, the obtaining of the vehicle's actual vehicle motion state information and determining the target simulation scene information corresponding to the actual vehicle motion state information in step 202 include: obtaining the vehicle's actual vehicle motion state information at a preset time interval and determining the target simulation scene information corresponding to the actual vehicle motion state information; accordingly, step 203 may specifically include: inputting the actual vehicle motion state information and the target simulation scene information obtained at the preset time interval into the AEB algorithm module for calculation to obtain the output result of the AEB algorithm module; accordingly, step 204 may specifically include: obtaining the test result of the AEB algorithm module by analyzing the various output results obtained at the preset time interval.

[0062] By presetting the time interval, multiple groups of vehicle motion status information and target simulation scene information can be obtained, and the AEB algorithm verification module can work continuously. By presetting the time interval, a group of AEB algorithm module output results can be obtained in each time interval until the test working mode is switched back to the normal working mode, and then the collection of the AEB algorithm module output results is stopped. Multiple groups of output results can be obtained by presetting the time interval, and the test results can be comprehensively judged based on these output results to obtain the test results of the vehicle AEB algorithm module. The more output results obtained, the higher the accuracy of vehicle detection.

[0063] By applying the technical solution of this embodiment, when the user selects the AEB algorithm verification mode, the system automatically generates a virtual scene, so that the vehicle can be tested in a precise C-NCAP standard scene, reducing the cost of scene construction and improving the accuracy of vehicle detection.

[0064] Further, as Figure 1 and Figure 3 The specific implementation of the method shown in the embodiment provides a song recommendation device, such as Figure 4 As shown, the device includes: an acquisition module 31, a calculation module 32, and a testing module 33.

[0065] The acquisition module 31 is configured to acquire the real vehicle motion state information of the vehicle, and determine the target simulation scene information corresponding to the real vehicle motion state information, wherein the target simulation scene information meets the standard scene conditions corresponding to the AEB algorithm module test and the target simulation scene information includes obstacle targets;

[0066] The calculation module 32 is configured to input the real vehicle motion state information and the target simulation scene information into the AEB algorithm module for calculation to obtain an output result of the AEB algorithm module;

[0067] The test module 33 is configured to parse the output result and obtain a test result of whether the vehicle will collide with an obstacle target in the target simulation scene information when traveling in the target simulation scene information with the real vehicle motion state information.

[0068] In a specific application scenario, the acquisition module 31 is specifically configured to determine the current driving speed and direction of the vehicle from the real vehicle motion state information; obtain the simulation results of the vehicle driving at the driving speed and the direction in each pre-edited simulation scene information, wherein the simulation scene information includes obstacle targets; if the simulation result indicates that the vehicle collides with the obstacle target within a preset time period, the corresponding simulation scene information is used as the target simulation scene information.

[0069] In a specific application scenario, optionally, the target simulation scene information includes: simulated obstacle target information and lane line information; accordingly, the calculation module 32 is specifically configured to input the obstacle target information and the lane line information, as well as the actual vehicle motion state information, into the AEB algorithm module; the output result includes a judgment result of whether the vehicle will collide with the obstacle target, and deceleration information when the vehicle collides with the obstacle target.

[0070] In a specific application scenario, the test module 33 is specifically configured to obtain the current distance between the vehicle and the obstacle target; trigger the vehicle to brake according to the deceleration information, and obtain the braking distance and braking time of the vehicle; if the braking distance is less than the current distance or the braking time is less than the preset time, it is determined that the vehicle will not collide with the obstacle target.

[0071] In a specific application scenario, optionally, the obstacle target information includes: initial attribute information and action event information of the obstacle target; the initial attribute information includes: the initial position, speed and moving direction of the obstacle target; the action event information includes: the obstacle target moves from the initial position at the speed and moving direction along the set route to the target position, wherein the target position is the position reached by the vehicle after driving for the preset time based on the actual vehicle motion state information.

[0072] In a specific application scenario, the acquisition module 31 is also configured to obtain the working mode of the AEB algorithm module before determining the target simulation scene information corresponding to the actual vehicle motion state information; if the working mode is the normal working mode, the real scene information is obtained through the perception module of the vehicle, and the real scene information is used to be input into the AEB algorithm module; if the working mode is set to the test working mode, the target simulation scene information corresponding to the actual vehicle motion state information is determined.

[0073] It should be noted that for other corresponding descriptions of the functional units involved in the vehicle testing device provided in this embodiment, reference can be made to Figure 1 and Figure 3 The corresponding description in will not be repeated here.

[0074] Based on the above Figure 1 and Figure 3 The method shown in the embodiment also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned Figure 1 and Figure 3 The method shown.

[0075] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of the present application.

[0076] Based on the above Figure 1 and Figure 3 The method shown, and Figure 4 In order to achieve the above-mentioned purpose, the embodiment of the present application also provides an electronic device that can be configured on the vehicle side (such as a new energy vehicle or a traditional fuel vehicle), and the device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to achieve the above-mentioned Figure 1 and Figure 3 The method shown.

[0077] Optionally, the above-mentioned physical device may also include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), etc.

[0078] Those skilled in the art will appreciate that the above-mentioned physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or a combination of certain components, or different arrangements of components.

[0079] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the above-mentioned physical device, and supports the operation of the information processing program and other software and / or programs. The network communication module is used to realize the communication between the components inside the storage medium, and the communication with other hardware and software in the information processing physical device.

[0080] Based on the above electronic device, the embodiment of the present application further provides a vehicle, which may specifically include: Figure 3 The device shown in the figure or the electronic device as described above. The vehicle can be a new energy vehicle, a traditional fuel vehicle, etc.

[0081] Through the above description of the implementation methods, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform, or by hardware. By applying the technical solution of this embodiment, when the user selects the AEB algorithm verification mode, the system automatically generates a virtual scene, so that the vehicle can be tested in a precise C-NCAP standard scene, reducing the scene construction cost and improving the accuracy of vehicle detection.

[0082] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0083] The above is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features applied for herein.

Claims

1. A vehicle testing method, characterized in that: include: Acquire the actual vehicle motion state information of the vehicle, and determine the target simulation scene information corresponding to the actual vehicle motion state information, wherein the target simulation scene information meets the standard scene conditions corresponding to the automatic emergency braking function AEB algorithm module test and the target simulation scene information includes an obstacle target; Inputting the real vehicle motion state information and the target simulation scene information into the AEB algorithm module to obtain an output result of the AEB algorithm module; The output result is analyzed to obtain a test result of whether the vehicle will collide with an obstacle target in the target simulation scene information when traveling in the target simulation scene information with the real vehicle motion state information.

2. The method according to claim 1, characterized in that: The determining of the target simulation scene information corresponding to the real vehicle motion state information includes: Determining the current speed and direction of the vehicle from the actual vehicle motion state information; Acquire a simulation result of the vehicle traveling in each pre-edited simulation scene information according to the driving speed and the traveling direction, wherein the simulation scene information includes an obstacle target; If the simulation result indicates that the vehicle collides with the obstacle target within a preset time period, the corresponding simulation scene information is used as the target simulation scene information.

3. The method according to claim 2, characterized in that The target simulation scene information includes: simulated obstacle target information and lane line information; The step of inputting the real vehicle motion state information and the target simulation scene information into the AEB algorithm module to obtain the output result of the AEB algorithm module includes: The obstacle target information, the lane line information, and the actual vehicle motion state information are input into the AEB algorithm module; the output result includes a judgment result of whether the vehicle will collide with the obstacle target, and deceleration information when the vehicle collides with the obstacle target.

4. The method according to claim 3, characterized in that The analyzing the output result to obtain a test result of whether the vehicle will collide with an obstacle target in the target simulation scene information when traveling in the target simulation scene information with the real vehicle motion state information includes: Obtaining the current distance between the vehicle and the obstacle target; Triggering the vehicle to brake according to the deceleration information, and obtaining a braking distance and a braking time of the vehicle; If the braking distance is less than the current distance or the braking time is less than the preset time, it is determined that the vehicle will not collide with the obstacle target.

5. The method according to claim 3, characterized in that: The obstacle target information includes: initial attribute information and action event information of the obstacle target; The initial attribute information includes: the initial position, speed and moving direction of the obstacle target; The action event information includes: the obstacle target moves from the initial position to the target position along the set route at the speed and moving direction, wherein the target position is the position reached by the vehicle after driving for the preset time based on the actual vehicle motion state information.

6. The method according to claim 1, characterized in that Before determining the target simulation scene information corresponding to the real vehicle motion state information, the method further includes: Obtaining the working mode of the AEB algorithm module; If the working mode is a normal working mode, real scene information is obtained through the perception module of the vehicle, and the real scene information is used to be input into the AEB algorithm module; If the working mode is set to the test working mode, the target simulation scene information corresponding to the real vehicle motion state information is determined.

7. A vehicle testing device, characterized in that: include: An acquisition module is configured to acquire real vehicle motion state information of the vehicle, and determine target simulation scene information corresponding to the real vehicle motion state information, wherein the target simulation scene information meets the standard scene conditions corresponding to the automatic emergency braking function AEB algorithm module test and the target simulation scene information includes obstacle targets; A calculation module is configured to input the real vehicle motion state information and the target simulation scene information into the AEB algorithm module to obtain an output result of the AEB algorithm module; The test module is configured to parse the output result and obtain a test result of whether the vehicle will collide with an obstacle target in the target simulation scene information when traveling in the target simulation scene information with the real vehicle motion state information.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

9. An electronic device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

10. A vehicle, characterized in that: include: The vehicle testing device as claimed in claim 7, or the electronic device as claimed in claim 9.

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