Method, device and equipment for generating expected function security test scene library

Abstract

The invention relates to the technical field of safety testing, and particularly provides a method, device and equipment for generating an expected function safety testing scene library, and the method comprises the steps: obtaining a parameter combination of key parameters in a pre-constructed basic scene; wherein the key parameters comprise environment parameters, traffic dynamic parameters and vehicle system parameters; generating a plurality of test scenes based on the parameter combination; after screening the plurality of test scenes, constructing a test scene library; wherein the test scene library is used for an expected function safety test of the intelligent driving vehicle. According to the embodiment of the invention, a rich expected function security test scene library can be constructed, and support is provided for expected function security test.

Description

Technical Field

[0001] This application relates to the field of security testing technology, and more specifically, to a method, apparatus, and equipment for generating a library of expected functional safety test scenarios. Background Technology

[0002] With the development of intelligent vehicles, the State of Expected Functional Safety (SOTIF) testing of autonomous driving control and execution systems is a crucial activity for ensuring the safety of autonomous driving. The core of SOTIF testing is the test scenario, which involves setting up corresponding test scenarios based on the operating conditions of known and unknown safety domains to achieve the testing and verification of expected functional safety. Currently, traditional scenario libraries rely solely on historical data and human experience for classification, failing to flexibly expand to cover more unknown and unsafe scenarios. This results in insufficient coverage of current SOTIF testing, leading to the omission of long-tail risks.

[0003] Therefore, how to provide a technical solution for generating an efficient library of expected functional safety test scenarios has become an urgent technical problem to be solved. Summary of the Invention

[0004] The purpose of some embodiments of this application is to provide a method, apparatus, and device for generating a library of expected functional safety test scenarios. The technical solutions of the embodiments of this application can automatically generate a rich and diverse library of expected functional safety test scenarios with a wide coverage, thereby achieving the accuracy and coverage of expected functional safety testing.

[0005] In a first aspect, some embodiments of this application provide a method for generating a library of expected functional safety test scenarios, comprising: obtaining parameter combinations of key parameters in a pre-constructed basic scenario; wherein the key parameters include environmental parameters, traffic dynamic parameters, and vehicle system parameters; generating multiple test scenarios based on the parameter combinations; and constructing a test scenario library after filtering the multiple test scenarios; wherein the test scenario library is used for expected functional safety testing of intelligent driving vehicles.

[0006] Some embodiments of this application initially generate multiple test scenarios by combining key parameters in a basic scenario, and then filter these multiple test scenarios to determine the test scenarios in the test scenario library. Embodiments of this application can automatically generate a rich and diverse library of expected functional safety test scenarios, with broad coverage, achieving both accuracy and coverage in expected functional safety testing.

[0007] In some embodiments, obtaining the parameter combination of key parameters in a pre-constructed basic scenario includes: extracting initial parameters from the basic scenario; wherein the initial parameters include environmental parameters, traffic dynamic parameters, and vehicle system parameters; filtering the initial parameters to obtain the key parameters; and processing the key parameters using a parameter perturbation strategy to obtain the parameter combination.

[0008] Some embodiments of this application extract initial parameters from the basic scenario, filter them to obtain key parameters, and finally use a parameter perturbation strategy to process them to obtain parameter combinations, thereby providing support for the subsequent generation of diverse test scenarios.

[0009] In some embodiments, before extracting the initial parameters from the basic scenario, the method further includes: obtaining the basic scenario based on real road test data, intelligent driving standard scenario data, and intelligent driving function documents.

[0010] Some embodiments of this application obtain basic scenarios through real road test data, intelligent driving standard scenario data, and intelligent driving function documents, providing scenario support for building a test scenario library.

[0011] In some embodiments, generating multiple test scenarios based on the parameter combination includes configuring a simulation engine based on the parameter combination so that the multiple test scenarios can be simulated by the configured simulation engine.

[0012] Some embodiments of this application configure the simulation engine through parameter combinations, which can efficiently generate multiple test scenarios and achieve high efficiency.

[0013] In some embodiments, the step of constructing a test scenario library after screening the multiple test scenarios includes: analyzing each test scenario among the multiple test scenarios using a risk quantification model to obtain a risk value for each test scenario; adding test scenarios with risk values ​​greater than a risk threshold to a database to obtain the test scenario library.

[0014] Some embodiments of this application use a risk quantification model to perform risk analysis on each test scenario to obtain a risk value. Then, the risk value is compared with a risk threshold to determine the test scenarios to be added to the database, thus creating a test scenario library. This embodiment can effectively filter test scenarios, select appropriate test scenarios, and provide precise test support for the expected functional safety testing.

[0015] In some embodiments, after constructing the test scenario library, the method further includes: testing the test scenarios in the test scenario library and obtaining test results; the test results serve as a reference for optimizing the test scenario library.

[0016] Some embodiments of this application obtain test results by testing test scenarios in the test scenario library, and then use the test results to optimize the test scenario library, thereby achieving dynamic optimization of the test scenario library and improving the reliability and effectiveness of expected functional safety testing.

[0017] Secondly, some embodiments of this application provide an apparatus for generating a library of expected functional safety test scenarios, comprising: an acquisition module for acquiring parameter combinations of key parameters in a pre-built basic scenario; wherein the key parameters include environmental parameters, traffic dynamic parameters, and vehicle system parameters; a generation module for generating multiple test scenarios based on the parameter combinations; and a construction module for constructing a test scenario library after filtering the multiple test scenarios; wherein the test scenario library is used for expected functional safety testing of intelligent driving vehicles.

[0018] In some embodiments, the acquisition module is used to: extract initial parameters from the basic scenario; wherein the initial parameters include environmental parameters, traffic dynamic parameters, and vehicle system parameters; filter the initial parameters to obtain the key parameters; and process the key parameters using a parameter perturbation strategy to obtain the parameter combination.

[0019] In some embodiments, the construction module is used to: analyze each of the plurality of test scenarios using a risk quantification model to obtain a risk value for each test scenario; and add test scenarios with risk values ​​greater than a risk threshold to a database to obtain the test scenario library.

[0020] Thirdly, some embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, can implement the method described in any embodiment of the first aspect.

[0021] Fourthly, some embodiments of this application provide an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, can implement the method as described in any embodiment of the first aspect.

[0022] Fifthly, some embodiments of this application provide a computer program product, the computer program product including a computer program, wherein the computer program, when executed by a processor, can implement the method described in any embodiment of the first aspect. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of some embodiments of this application, the accompanying drawings used in some embodiments of this application will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 System diagrams generated for the expected functional safety test scenario library provided for some embodiments of this application; Figure 2 One of the method flowcharts for generating a library of expected functional safety test scenarios provided for some embodiments of this application; Figure 3 The second flowchart illustrates the method for generating a library of expected functional safety test scenarios for some embodiments of this application. Figure 4 Device composition block diagrams generated for the expected functional safety test scenario library provided for some embodiments of this application; Figure 5 A schematic diagram of an electronic device provided for some embodiments of this application. Detailed Implementation

[0025] The technical solutions of some embodiments of this application will now be described with reference to the accompanying drawings.

[0026] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0027] In related technologies, the traditional scenario library for expected functional safety testing relies on historical data, which makes it difficult to cover unknown and unsafe scenarios, such as sensor failure under extreme weather conditions; moreover, scenario classification is highly subjective; and the testing efficiency is low, as it cannot exhaustively list all relevant parameter combinations under all test scenarios, requiring the use of scientific methods to screen high-risk scenarios; at the same time, insufficient test coverage leads to the omission of long-tail risks.

[0028] In view of this, some embodiments of this application provide a method for generating a library of expected functional safety test scenarios. This method first constructs basic scenarios using known relevant data, then scientifically perturbs key parameters of the basic scenarios (such as adjusting vehicle speed, weather, sensor noise, etc.) to generate a large number of test scenarios. Finally, these test scenarios are filtered to construct a test scenario library. The method for constructing the scenario library provided by some embodiments of this application has good controllability and scalability. During the generation process, the parameter range can be adjusted to accurately cover the boundaries of the expected functional safety domain (ODD), supporting the derivation of edge cases (i.e., test scenarios) from known scenarios, improving the richness and efficiency of test scenario library construction, and providing reliable test scenarios for expected functional safety testing.

[0029] The following is in conjunction with the appendix Figure 1 The overall composition structure of the system generated by the expected functional safety test scenario library provided by some embodiments of this application is illustrated by way of example.

[0030] like Figure 1 As shown, some embodiments of this application provide a system for generating a library of expected functional safety test scenarios. This system may include a terminal 100 and a generation server 200. The generation server 200 can obtain relevant data from the terminal 100, such as past real-world road test data, industry-standard test scenarios, and functional limitation documents, and construct basic scenarios based on this. Then, it combines key parameters from the basic scenarios to generate multiple test scenarios. Finally, it uses a model to screen for high-risk scenarios from the multiple test scenarios, constructing a test scenario library. This test scenario library can provide rich and reliable test scenarios for expected functional safety testing, thereby improving the accuracy and coverage of expected functional safety testing.

[0031] In some embodiments of this application, if the terminal 100 has functions related to building basic scenarios, generating test scenarios, and filtering test scenarios, such as generating server 200, then generating server 200 may not be required. The specific choice can be made according to the actual application scenario, and this embodiment of the application does not impose specific limitations here.

[0032] In some embodiments of this application, the terminal 100 can be a mobile terminal or a non-portable computer terminal, and the embodiments of this application are not specifically limited here.

[0033] The following is in conjunction with the appendix Figure 2 The present application provides an exemplary implementation process for generating a library of expected functional safety test scenarios executed by a generation server 200, based on some embodiments of this application.

[0034] Please see the appendix Figure 2 , Figure 2A flowchart illustrating a method for generating a library of expected functional safety test scenarios is provided for some embodiments of this application. The method for generating the library of expected functional safety test scenarios may include: S210, obtain the parameter combination of key parameters in the pre-constructed basic scenario; wherein, the key parameters include environmental parameters, traffic dynamic parameters and vehicle system parameters.

[0035] For example, in a specific embodiment of this application, environmental parameters such as light intensity, visibility, and rainfall are extracted from the basic scene; dynamic parameters such as vehicle speed and pedestrian crossing speed (as a specific example of traffic dynamic parameters); and system parameters such as sensor sampling frequency and communication delay (as a specific example of vehicle system parameters). Then, parameter space modeling is performed on the key parameters to obtain parameter combinations.

[0036] In some embodiments of this application, the method for generating the expected functional safety test scenario library before executing S210 may further include: obtaining the basic scenarios based on real road test data, intelligent driving standard scenario data, and intelligent driving function documents.

[0037] For example, in specific embodiments of this application, real road test data may include real road topology, road facilities, pedestrians, vehicles, vehicle reaction status, and other data; intelligent driving standard scenario data, which can be simply referred to as standard scenarios, are reference standard scenarios specifically designed for intelligent driving testing; and intelligent driving function documents are function limitation documents, which contain relevant definitions of intelligent driving functions. These three elements can be used to construct basic scenarios related to the expected functional safety testing.

[0038] In some embodiments of this application, S210 may include: extracting initial parameters from the basic scenario; wherein the initial parameters include environmental parameters, traffic dynamic parameters, and vehicle system parameters; filtering the initial parameters to obtain the key parameters; and processing the key parameters using a parameter perturbation strategy to obtain the parameter combination.

[0039] For example, in a specific embodiment of this application, the initial parameters can be environmental parameters such as light intensity, visibility, and rainfall; dynamic parameters such as vehicle speed and pedestrian crossing speed; and system parameters such as sensor sampling frequency and communication delay. By performing parameter space modeling, defining parameter ranges and constraints, and selecting key parameters that meet the range and constraint requirements, parameter correlation analysis is achieved. For example, visibility parameters within a preset visibility range are selected, while parameters outside the range are eliminated. Parameter correlation analysis can identify strongly correlated parameters such as visibility and camera false detection rate, avoiding the generation of invalid scenarios. Subsequently, using perturbation methods such as Monte Carlo random sampling and boundary value perturbation, a parameter perturbation strategy is designed to randomly combine the aforementioned key parameters, resulting in parameter combinations. Different parameter combinations can contain different values ​​of different parameters from environmental, dynamic, and system parameters, thereby subsequently constructing multiple different test scenarios.

[0040] S220, based on the parameter combination, generate multiple test scenarios.

[0041] For example, in a specific embodiment of this application, different test scenarios corresponding to different parameter combinations can be generated by using the above-mentioned randomly generated parameter combinations.

[0042] In some embodiments of this application, S220 may include: configuring the simulation engine based on the parameter combination so as to simulate the plurality of test scenarios through the configured simulation engine.

[0043] For example, in a specific embodiment of this application, the abstract parameter combination obtained above is transformed into a configuration executable by the simulation engine. After the simulation engine is configured, multiple different test scenarios can be initially simulated and generated.

[0044] S230, after filtering the multiple test scenarios, a test scenario library is constructed; wherein, the test scenario library is used for the expected functional safety testing of intelligent driving vehicles.

[0045] For example, in a specific embodiment of this application, the multiple test scenarios generated by the above simulation are subjected to risk screening to obtain the high-risk test results after screening, so as to construct a test scenario library.

[0046] In some embodiments of this application, S230 may include: analyzing each test scenario among the plurality of test scenarios using a risk quantification model to obtain a risk value for each test scenario; adding test scenarios with risk values ​​greater than a risk threshold to a database to obtain the test scenario library.

[0047] For example, in a specific embodiment of this application, a risk value (Risk) for each test scenario is calculated using a risk quantification model, where Risk = P * S * V; and P is the exposure probability, S is the severity, and V is the vulnerability. Test scenarios can be filtered by comparing Risk with a risk threshold (a). Specifically, test scenarios with Risk > a (a can be dynamically adjusted) are retained and stored in the database to obtain a test scenario library.

[0048] In some embodiments of this application, after executing S230, the method for generating the expected functional safety test scenario library may further include: testing the test scenarios in the test scenario library and obtaining test results; the test results serve as a reference for optimizing the test scenario library.

[0049] For example, in a specific embodiment of this application, testing tools are used to test the test scenarios in the test scenario library to obtain test results, thereby achieving iterative optimization of the test scenario library. Specifically, for test scenarios that fail the test, their parameter variants need to be added (e.g., adding new parameters), and the parameter perturbation range needs to be dynamically updated to make them pass the test. Test scenarios that have passed the test can have their weight reduced in subsequent tests or be archived. The dynamic update of the parameter perturbation range can be adjusted during the parameter perturbation strategy design stage or during the test scenario selection stage. For example, during the parameter perturbation strategy design stage, dynamic weights can be assigned to the sampling probability of each parameter, and these weights can be adjusted based on historical test results to prioritize the generation of high-risk parameter combinations and avoid resource waste caused by uniform sampling. During the test scenario selection stage, the weight coefficients of the three dimensions can be dynamically adjusted using the risk assessment model Risk=P*S*V. The dynamic weight adjustment method can be to test and evaluate the test scenarios generated by the current weight values, calculate the scores, and dynamically update the weights based on the scores.

[0050] In the embodiments described above, by designing parameter space modeling and parameter perturbation strategies, high-risk, unknown, and unsafe test scenarios can be generated efficiently, making up for the deficiencies of traditional scenario libraries in terms of coverage and automation, and improving the richness of the test scenario library.

[0051] The following is in conjunction with the appendix Figure 3 The present application provides an exemplary description of the specific process for generating the expected functional safety test scenario library provided by some embodiments.

[0052] Please see the appendix Figure 3 , Figure 3 A flowchart illustrating a method for generating a library of expected functional safety test scenarios, provided for some embodiments of this application.

[0053] The above process is illustrated below by example.

[0054] S310 acquires basic scenarios based on real road test data, intelligent driving standard scenario data, and intelligent driving function documents.

[0055] S320 extracts initial parameters from the basic scene.

[0056] S330 filters the initial parameters to obtain the key parameters.

[0057] S340 uses a parameter perturbation strategy to process key parameters and obtain parameter combinations.

[0058] The S350 allows for the configuration of the simulation engine based on parameter combinations, enabling the simulation of multiple test scenarios through the configured engine.

[0059] S360 uses a risk quantification model to analyze each test scenario in multiple test scenarios and obtain the risk value of each test scenario.

[0060] S370 adds test scenarios with risk values ​​greater than the risk threshold to the database to obtain a test scenario library.

[0061] S380 performs tests on test scenarios in the test scenario library and obtains test results.

[0062] It is understood that the specific implementation process of S310~S380 can be referred to the method embodiment provided above. To avoid repetition, detailed descriptions are omitted here.

[0063] Please refer to Figure 4 , Figure 4 The diagram illustrates the composition of an apparatus for generating a library of expected functional safety test scenarios provided in some embodiments of this application. It should be understood that this apparatus corresponds to the method embodiments described above and is capable of performing the various steps involved in the method embodiments. The specific functions of this apparatus can be found in the description above; detailed descriptions are omitted here to avoid repetition.

[0064] Figure 4 The apparatus for generating a test scenario library for expected functional safety includes at least one software functional module that can be stored in a memory or embedded in the apparatus in the form of software or firmware. The apparatus includes: an acquisition module 410 for acquiring parameter combinations of key parameters in a pre-built basic scenario; wherein the key parameters include environmental parameters, traffic dynamic parameters, and vehicle system parameters; a generation module 420 for generating multiple test scenarios based on the parameter combinations; and a construction module 430 for constructing a test scenario library after filtering the multiple test scenarios; wherein the test scenario library is used for expected functional safety testing of intelligent driving vehicles.

[0065] In some embodiments of this application, the acquisition module 410 is used to: extract initial parameters from the basic scenario; wherein the initial parameters include environmental parameters, traffic dynamic parameters, and vehicle system parameters; filter the initial parameters to obtain the key parameters; and process the key parameters using a parameter perturbation strategy to obtain the parameter combination.

[0066] In some embodiments of this application, the acquisition module 410 is used to: acquire the basic scenario based on real road test data, intelligent driving standard scenario data, and intelligent driving function documents.

[0067] In some embodiments of this application, the generation module 420 is used to: configure the simulation engine based on the parameter combination so as to simulate the plurality of test scenarios through the configured simulation engine.

[0068] In some embodiments of this application, the construction module 430 is used to: analyze each test scenario among the plurality of test scenarios using a risk quantification model to obtain a risk value for each test scenario; and add test scenarios with risk values ​​greater than a risk threshold to a database to obtain the test scenario library.

[0069] In some embodiments of this application, after the construction module 430, the apparatus for generating the expected functional safety test scenario library further includes: a test module (not shown in the figure) for: testing the test scenarios in the test scenario library and obtaining test results; the test results serve as a reference for optimizing the test scenario library.

[0070] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the aforementioned method, and will not be elaborated further here.

[0071] Some embodiments of this application also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, can perform the operation of any of the methods corresponding to the methods provided in the above embodiments.

[0072] Some embodiments of this application also provide a computer program product, which includes a computer program, wherein when the computer program is executed by a processor, it can implement the operation of any of the methods corresponding to the above embodiments provided in the above embodiments.

[0073] Figure 5 A schematic diagram of the structure of an electronic device 500 is shown. (See attached diagram.) Figure 5As shown, the electronic device 500 includes a processor 510 and a memory 520, and optionally may also include a power supply 530, a display unit 540, and an input unit 550.

[0074] The processor 510 is the control center of the electronic device 500. It connects various components through various interfaces and lines, and performs various functions of the electronic device 500 by running or executing software programs and / or data stored in the memory 520, thereby performing overall monitoring of the electronic device 500.

[0075] In this embodiment, when the processor 510 calls the computer program stored in the memory 520, it executes the steps in the above embodiments.

[0076] Optionally, processor 510 may include one or more processing units; preferably, processor 510 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into processor 510. In some embodiments, the processor and memory may be implemented on a single chip; in some embodiments, they may also be implemented separately on independent chips.

[0077] The memory 520 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, various applications, etc.; the data storage area may store data created based on the use of the electronic device 500, etc. In addition, the memory 520 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0078] Electronic device 500 also includes a power supply 530 (such as a battery) that supplies power to various components. The power supply can be logically connected to processor 510 through a power management system, thereby enabling the power management system to manage functions such as charging, discharging, and power consumption.

[0079] The display unit 540 can be used to display information input by the user or information provided to the user, as well as various menus of the electronic device 500. In this embodiment of the invention, it is mainly used to display the display interfaces of various applications in the electronic device 500, as well as text, images, and other objects displayed on the display interfaces. The display unit 540 may include a display panel 541. The display panel 541 may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like.

[0080] The input unit 550 can be used to receive information such as numbers or characters input by the user. The input unit 550 may include a touch panel 551 and other input devices 552. The touch panel 551, also known as a touch screen, can collect touch operations on or near the user (such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near the touch panel 551).

[0081] Specifically, the touch panel 551 can detect user touch operations and the signals generated by these operations, convert them into touch point coordinates, send them to the processor 510, and receive and execute commands from the processor 510. Furthermore, the touch panel 551 can be implemented using various types of sensors, including resistive, capacitive, infrared, and surface acoustic wave sensors. Other input devices 552 can include, but are not limited to, one or more of the following: physical keyboard, function keys (such as volume control buttons, power buttons, etc.), trackball, mouse, joystick, etc.

[0082] Of course, the touch panel 551 can cover the display panel 541. When the touch panel 551 detects a touch operation on or near it, it transmits the information to the processor 510 to determine the type of touch event. Subsequently, the processor 510 provides corresponding visual output on the display panel 541 according to the type of touch event. Although in Figure 5 In this embodiment, the touch panel 551 and the display panel 541 are two separate components to realize the input and output functions of the electronic device 500. However, in some embodiments, the touch panel 551 and the display panel 541 can be integrated to realize the input and output functions of the electronic device 500.

[0083] The electronic device 500 may also include one or more sensors, such as a pressure sensor, a gravity acceleration sensor, a proximity sensor, etc. Of course, depending on the specific application, the electronic device 500 may also include other components such as a camera. Since these components are not the focus of this application embodiment, therefore... Figure 5 It is not shown in the text and will not be described in detail here.

[0084] Those skilled in the art will understand that Figure 5 This is merely an example of an electronic device and does not constitute a limitation on the electronic device. It may include more or fewer components than shown, or a combination of certain components, or different components.

[0085] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0086] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0087] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for generating a library of functional safety test scenarios, characterized in that, The method comprises the following steps: obtaining a parameter combination of key parameters in a pre-constructed basic scene; wherein the key parameters comprise environmental parameters, traffic dynamic parameters and vehicle system parameters; generating a plurality of test scenes based on the parameter combination; constructing a test scene library after screening the plurality of test scenes; wherein the test scene library is used for expected function safety testing of an intelligent driving vehicle.

2. The method of claim 1, wherein, The step of obtaining the parameter combination of the key parameters in the pre-constructed basic scene comprises the following steps: extracting initial parameters in the basic scene; wherein the initial parameters comprise environmental parameters, traffic dynamic parameters and vehicle system parameters; screening the initial parameters to obtain the key parameters; processing the key parameters by using a parameter perturbation strategy to obtain the parameter combination.

3. The method of claim 2, wherein, Before the step of extracting the initial parameters in the basic scene, the method further comprises the following step: obtaining the basic scene based on real road test data, intelligent driving standard scene data and intelligent driving function documents.

4. The method of any one of claims 1-3, wherein, The step of generating a plurality of test scenes based on the parameter combination comprises the following step: configuring a simulation engine based on the parameter combination so as to obtain the plurality of test scenes by simulation of the configured simulation engine.

5. The method of any one of claims 1-3, wherein, The step of constructing a test scene library after screening the plurality of test scenes comprises the following steps: analyzing each test scene in the plurality of test scenes by using a risk quantification model to obtain a risk value of each test scene; adding a test scene with a risk value greater than a risk threshold to a database to obtain the test scene library.

6. The method of any one of claims 1-3, wherein, After the step of constructing the test scene library, the method further comprises the following step: testing the test scenes in the test scene library to obtain a test result; the test result is used as a reference basis for optimizing the test scene library.

7. An apparatus for generating a library of expected functional safety test scenarios, characterized in that, The method comprises the following steps: an obtaining module, configured to obtain a parameter combination of key parameters in a pre-constructed basic scene; wherein the key parameters comprise environmental parameters, traffic dynamic parameters and vehicle system parameters; a generating module, configured to generate a plurality of test scenes based on the parameter combination; a constructing module, configured to construct a test scene library after screening the plurality of test scenes; wherein the test scene library is used for expected function safety testing of an intelligent driving vehicle.

8. The apparatus of claim 7, wherein, The obtaining module is configured to: extract initial parameters in the basic scene; wherein the initial parameters comprise environmental parameters, traffic dynamic parameters and vehicle system parameters; screen the initial parameters to obtain the key parameters; process the key parameters by using a parameter perturbation strategy to obtain the parameter combination.

9. The apparatus of any one of claims 7-8, wherein, The constructing module is configured to: analyze each test scene in the plurality of test scenes by using a risk quantification model to obtain a risk value of each test scene; add a test scene with a risk value greater than a risk threshold to a database to obtain the test scene library.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, wherein the computer program is run by a processor to execute the method according to any one of claims 1-6.

11. An electronic device, comprising: A computer program product comprising a computer program, wherein the computer program, when executed by a processor, performs the method according to any one of claims 1-6.

12. A computer program product, characterised in that, The computer program product comprises a computer program, wherein the computer program, when executed by a processor, performs the method according to any one of claims 1-6.

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