Adaptive vehicle test case generation method and system

Abstract

The present application provides an adaptive whole vehicle test case generation method and system, which relates to the field of vehicle testing technology, including: obtaining basic operating components of the vehicle to be tested; generating an initial whole vehicle test case; based on the initial whole vehicle test case, connecting the seat adjustment component, introducing a left balance test sequence and a right balance test sequence; analyzing the operating dynamic characteristics of the basic operating components; adding operating dynamic characteristics under different operating environments, and performing parameter correction based on different operating instructions in combination with the initial whole vehicle test case, and performing adaptive adjustment according to the industry standards of the vehicle to be tested to obtain an adapted whole vehicle test case. This application can solve the technical problem in the prior art that the test efficiency is low due to the test cases not covering a full range of driving scenarios. By dynamically adjusting the test cases according to different operating instructions and operating environments, it is ensured that the test case set always maintains a high test coverage rate, thereby significantly improving the test efficiency.

Description

Technical Field

[0001] The present application relates to the field of vehicle testing technology, and in particular to a method and system for generating adaptive vehicle test cases. Background Art

[0002] With the continuous development of vehicle technology, especially the increasingly obvious trends of intelligence, automation, and electrification, vehicle testing has become a vital part of ensuring automobile safety, performance, and reliability. In order to comprehensively evaluate the performance of the entire vehicle, a large number of diverse tests are required. The generation of test cases is the core of vehicle testing and determines the breadth and depth of testing. Through accurate and comprehensive test cases, various driving scenarios can be simulated, thereby identifying potential safety hazards and performance bottlenecks in advance, ensuring that the vehicle can meet various actual usage requirements after being put on the market. However, most existing test cases rely on predefined rules, simulation models, or historical data to generate test cases, but they can only cover a portion of typical operating scenarios and cannot fully cover all actual driving scenarios and operating characteristics, resulting in low testing efficiency.

[0003] In summary, the existing technology has a technical problem of low testing efficiency due to the inability of test cases to fully cover all actual driving scenarios and operating characteristics. Summary of the Invention

[0004] The purpose of this application is to provide an adaptive vehicle test case generation method and system to solve the technical problem in the prior art that test cases cannot fully cover all actual driving scenarios and operating characteristics, resulting in low test efficiency.

[0005] In view of the above problems, the present application provides an adaptive vehicle test case generation method and system.

[0006] In the first aspect, the present application provides an adaptive vehicle test case generation method, which is implemented by an adaptive vehicle test case generation system, wherein the adaptive vehicle test case generation method includes: obtaining basic operating components of the vehicle to be tested, the basic operating components including a steering wheel, a brake pedal, an accelerator pedal, and a rearview mirror; based on the basic operating components, generating an initial vehicle test case, the initial vehicle test case including a force feedback strength test sequence, a response time test sequence, and an operation linearity test sequence; based on the initial vehicle test case, connecting the seat adjustment component and introducing the left Balance test sequence, right balance test sequence, wherein the left balance test sequence includes P left balance test points, and the right balance test sequence includes Q right balance test points; based on the left balance test sequence and the right balance test sequence, the operation line characteristics of the basic operating components are analyzed, and the operation line characteristics include symmetrical balance line characteristics and linear line characteristics; under different operating environments, the operation line characteristics are added, and according to different operating instructions, the parameters are corrected in combination with the initial vehicle test case, and adaptive adjustments are made according to the industry standards of the vehicle to be tested to obtain adapted vehicle test cases.

[0007] In the second aspect, the present application also provides an adaptive whole vehicle test case generation system for executing the adaptive whole vehicle test case generation method as described in the first aspect, wherein the adaptive whole vehicle test case generation system includes: a basic component acquisition module for acquiring basic operating components of the vehicle to be tested, wherein the basic operating components include a steering wheel, a brake pedal, an accelerator pedal, and a rearview mirror; a test case generation module for generating an initial whole vehicle test case based on the basic operating components, wherein the initial whole vehicle test case includes a force feedback strength test sequence, a response time test sequence, and an operation linearity test sequence; a balance test introduction module for connecting a seat adjustment component based on the initial whole vehicle test case to introduce A left-side balance test sequence and a right-side balance test sequence are input, wherein the left-side balance test sequence includes P left-side balance test points and the right-side balance test sequence includes Q right-side balance test points; a dynamic line feature analysis module is used to analyze the operation dynamic line features of the basic operating component based on the left-side balance test sequence and the right-side balance test sequence, wherein the operation dynamic line features include symmetrical balance dynamic line features and linear dynamic line features; a test case determination module is used to add the operation dynamic line features under different operating environments, and according to different operating instructions, perform parameter correction in combination with the initial vehicle test case, and perform adaptive adjustment according to the industry standard of the vehicle to be tested to obtain an adapted vehicle test case.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] By obtaining basic operating components of the vehicle to be tested, the basic operating components include a steering wheel, a brake pedal, an accelerator pedal, and a rearview mirror; based on the basic operating components, an initial whole vehicle test case is generated, and the initial whole vehicle test case includes a force feedback strength test sequence, a response time test sequence, and an operation linearity test sequence; based on the initial whole vehicle test case, a seat adjustment component is connected, and a left balance test sequence and a right balance test sequence are introduced, wherein the left balance test sequence includes P left balance test points, and the right balance test sequence includes Q right balance test points; based on the left balance test sequence and the right balance test sequence, the operation dynamic line characteristics of the basic operating components are analyzed, and the operation dynamic line characteristics include symmetrical balance dynamic line characteristics and linear dynamic line characteristics; under different operating environments, the operation dynamic line characteristics are added, and according to different operating instructions, the parameters are corrected in combination with the initial whole vehicle test case, and adaptive adjustments are made according to the industry standards of the vehicle to be tested to obtain adapted whole vehicle test cases. In other words, by designing force feedback intensity, response time and linearity test sequences based on basic operating components, and dynamically adjusting test cases according to different operating instructions and operating environments, we can ensure that the test case set always maintains a high test coverage rate, thereby significantly improving test efficiency.

[0010] 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, which can be implemented in accordance with the contents of the description, and 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 specifically listed below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in this application or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without creative work.

[0012] Figure 1 This is a flowchart of the adaptive vehicle test case generation method for this application;

[0013] Figure 2 This is a schematic diagram of the structure of the adaptive vehicle test case generation system for this application.

[0014] Explanation of the reference numerals: basic component acquisition module 11, test case generation module 12, balance test introduction module 13, movement line feature analysis module 14, test case determination module 15. DETAILED DESCRIPTION

[0015] This application addresses the existing technical problem of low test efficiency, which arises from the inability of test cases to fully cover all actual driving scenarios and operating characteristics, by providing an adaptive vehicle test case generation method and system. By designing force feedback intensity, response time, and linearity test sequences based on basic operating components and dynamically adjusting test cases based on different operating instructions and operating environments, the test case set maintains a high level of test coverage, significantly improving test efficiency.

[0016] Below, the technical solutions in this application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited to the example embodiments described herein. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should also be noted that, for the convenience of description, only the parts related to this application, rather than all of them, are shown in the accompanying drawings.

[0017] For example, see the attached Figure 1 The present application provides an adaptive vehicle test case generation method, wherein the adaptive vehicle test case generation method is applied to an adaptive vehicle test case generation system, and the adaptive vehicle test case generation method specifically includes the following steps:

[0018] S100: Acquire basic operating components of the vehicle to be tested, where the basic operating components include a steering wheel, a brake pedal, an accelerator pedal, and a rearview mirror.

[0019] Specifically, a vehicle under test refers to a vehicle that requires full vehicle testing, typically during the R&D phase or before mass production, to verify that its performance, functionality, safety, and other aspects meet standard requirements. Before conducting full vehicle testing, the core operating components of the vehicle under test are identified. These operating components serve as the fundamental interface between the driver and the vehicle and have a direct impact on the test results. Basic operating components play a key role in the driver's control of the vehicle and are primarily used to control basic vehicle operations such as driving, accelerating, and braking. These components include the steering wheel, brake pedal, accelerator pedal, and rearview mirror. The steering wheel controls the vehicle's direction; the brake pedal controls the vehicle's braking system to slow or stop it; the accelerator pedal controls the vehicle's acceleration system to increase its speed; and the rearview mirror allows the driver to monitor traffic conditions behind the vehicle to ensure safe driving. By acquiring these basic operating components, a preliminary understanding of the vehicle's operational performance can be gained.

[0020] S200: Based on the basic operating components, generate initial vehicle test cases, wherein the initial vehicle test cases include a force feedback strength test sequence, a response time test sequence, and an operation linearity test sequence.

[0021] Specifically, after obtaining the basic data of the basic operating components, an initial vehicle test case is generated based on the basic operating components. This serves as the starting point for the test sequence, covering the vehicle's basic operating functions and performance testing. The force feedback strength test sequence targets the force feedback of the steering wheel, brake pedal, accelerator pedal, and rearview mirror. By simulating various driving scenarios such as steering and emergency braking, the feedback force of the steering wheel, brake pedal, and accelerator pedal is recorded to determine whether it is within the design and safety standards. Although the rearview mirror does not directly participate in the vehicle's driving control, its performance has a significant impact on the driver's safety and comfort. By measuring the force required by the driver to adjust the rearview mirror, the force feedback strength of the rearview mirror is evaluated to determine whether it is within the design and safety standards, avoiding being too easy to turn the direction while also avoiding being difficult to turn.

[0022] The response time test sequence is used to evaluate the vehicle's responsiveness to operational commands. It typically tests the response time of operations such as the brake pedal, accelerator pedal, steering wheel, and rearview mirrors. This refers to the time interval between the driver's input of a command (such as pressing the accelerator pedal or turning the steering wheel) and the vehicle's actual reaction. For example, in a brake test, testers measure the time delay from the moment the driver presses the brake pedal to the moment the brake system begins to decelerate. For the accelerator pedal, the test records the response time from the moment the accelerator pedal is pressed to the moment the engine outputs power. For the steering wheel, the test records the time from the start of the rotation to the vehicle's initial turn by quickly turning the wheel to a certain angle. For the rearview mirror, the response time is evaluated by measuring the time it takes from the driver's initiation of adjustment to the moment the mirror begins to move.

[0023] The operation linearity test sequence is used to verify the linearity of vehicle operation, especially during the operation of the brake pedal, accelerator pedal and steering wheel, to test whether the relationship between these operations and the vehicle response is linear. Design an operation sequence to test the linearity of braking, acceleration and steering wheel operation. For example, gradually increase the pressure on the accelerator pedal, record the changes in vehicle acceleration, and check whether the changes are linear. For steering wheel testing, continuously rotate the steering wheel to record the relationship between the vehicle steering angle and response to ensure that the steering wheel feedback operation meets the requirements. By generating initial full-vehicle test cases, the performance of the vehicle on different operating components can be effectively evaluated, which helps to identify potential performance issues such as insufficient force feedback strength, excessive response time or poor operation linearity, thereby providing guidance for improving vehicle performance.

[0024] S300: Based on the initial vehicle test case, connect the seat adjustment component and introduce a left balance test sequence and a right balance test sequence, wherein the left balance test sequence includes P left balance test points and the right balance test sequence includes Q right balance test points.

[0025] Specifically, the seat adjustment assembly is connected, and the left and right balance test sequences are introduced. The seat adjustment assembly is introduced to simulate the driver's action of adjusting the seat in a real driving environment, which is crucial for evaluating the driver's comfort and ease of operation. The seat adjustment assembly is a device used to adjust the position and angle of the car seat, including functions such as fore and aft movement, backrest angle adjustment, and seat height adjustment. The seat adjustment assembly is introduced and a balance test is performed to ensure that the left and right sides of the driver's seat remain symmetrical and balanced under different seat adjustment settings. This is achieved through left and right balance test sequences. During the test, the different adjustment positions of the seat need to be verified to ensure that the two sides of the seat are adjusted synchronously and there is no asymmetry.

[0026] The left-side balance test sequence includes P left-side balance test points, representing different seat adjustment combinations, such as the seat most forward, the backrest angle upright, the seat height lowest, the seat position slightly rearward, the backrest angle slightly tilted, and the seat height higher. This covers multiple seat adjustment settings and ensures that the seat remains balanced in these settings. At each test point, a spirit level and other testing tools are used to assess the balance of the left and right sides of the seat to ensure there is no tilt or asymmetry caused by adjustment.

[0027] The right-side balance test sequence is similar to the left-side balance test. It includes Q right-side test points, each representing a different seat adjustment setting. These include seat forward, upright backrest, lowest seat height, slightly rearward, slightly tilted backrest, and higher seat height. This covers multiple seat adjustment settings, ensuring the seat remains balanced in these settings. At each test point, the right-side seat is verified for symmetry, ensuring that the seat maintains proper left-right balance under these settings.

[0028] By introducing left-side and right-side balance test sequences, the performance of the seat adjustment components is comprehensively evaluated to ensure that the driver can maintain good body balance when operating the vehicle, which helps to improve the driver's comfort and safety, and also helps to discover and improve possible problems with the seat adjustment components.

[0029] S400: Analyzing the operation dynamic line characteristics of the basic operating component based on the left balance test sequence and the right balance test sequence, where the operation dynamic line characteristics include symmetrical balance dynamic line characteristics and linear dynamic line characteristics.

[0030] Specifically, based on the left and right balance test sequences, the operating dynamics of basic operating components are analyzed to determine whether the operating dynamics of basic operating components such as seat adjustment, steering wheel, and pedals conform to symmetry and linearity during actual operation, thereby improving driver comfort and efficiency. The operating dynamics are the paths and behavioral characteristics formed by the driver when operating basic components. They reflect the driver's operating habits and operating space, and reflect the driver's activities and operating processes within the vehicle, including the smoothness, symmetry, and linearity of the movements.

[0031] During testing, the driver's movements of various basic components are recorded, and data is compared when the driver operates the steering wheel, brake pedal, and accelerator pedal from the left and right sides to assess the symmetry of the operating paths. Symmetrical and balanced movement paths are characterized by left-right symmetry in the driver's movements, particularly when operating basic components such as the steering wheel and pedals.

[0032] By analyzing the relationship between the driver's operational input (such as steering wheel rotation angle, pedal depression depth) and the vehicle's response (such as wheel steering angle, acceleration or deceleration), we assess whether a linear relationship exists. A linear dynamic line characteristic refers to the linearity of the driver's operating path or action during operation, that is, the operating path or action is smooth and linear without excessive bends or twists. Linear dynamic lines generally represent a more ideal operating experience and can improve driving efficiency and comfort. By analyzing symmetrically balanced dynamic lines and linear dynamic lines, we ensure that the driver's movements are more coordinated and smoother when operating basic components, which not only improves driver comfort, but also optimizes operational efficiency, reduces unnecessary movements, and enhances the overall vehicle operating experience.

[0033] S500: Under different operating environments, the operating line features are added, and according to different operating instructions, parameters are modified in combination with the initial vehicle test case, and adaptation and adjustment are performed according to the industry standard of the vehicle to be tested to obtain an adapted vehicle test case.

[0034] Specifically, the initial vehicle test cases are adapted and adjusted based on different operating environments to ensure that the test cases can more comprehensively and accurately reflect the various situations encountered by drivers in actual driving, ultimately generating test cases adapted for the entire vehicle. Changes in the operating environment will affect the driver's operating dynamics. For example, when driving at high speeds, the driver may require faster reaction times and smoother operating dynamics; while when driving at low speeds in the city, the operation may focus more on fine control and delicate adjustments. The operating environment refers to the various physical and operational conditions under which the driver operates the basic components, such as vehicle speed, road conditions, driving mode (such as economy mode, sport mode, etc.), and external environmental factors (such as weather and temperature).

[0035] Operational movement characteristics refer to the movement characteristics when the driver operates basic components, including symmetrical and balanced movement characteristics and linear movement characteristics. Under different operating environments, operation movement characteristics are added to the initial vehicle test cases, and parameters are modified according to different operating instructions. Operation instructions are commands or operating instructions issued by the driver through basic operating components (such as the steering wheel, brake pedal, and accelerator pedal), such as braking, accelerating, and turning on the turn signal. For example, during the braking process, the force feedback strength and response time of the brake pedal may need to be adjusted accordingly to meet the requirements of different operating instructions.

[0036] Test cases are further adapted and adjusted based on the standard requirements of the industry in which the vehicle being tested operates, using industry standards and regulatory documentation for comparison to ensure that the test cases meet the specified safety, comfort, and performance requirements. For example, if industry standards require electric vehicles to achieve specific acceleration performance under specific conditions, the accelerator pedal test sequence in the test case will need to be adjusted accordingly. By adding operational trajectory features under different operating environments and performing parameter corrections and adaptive adjustments based on different operating instructions, the test cases are ensured to comprehensively evaluate the vehicle's performance in various actual driving situations, helping to discover and resolve more potential vehicle issues and improve vehicle safety and reliability.

[0037] Furthermore, the present application S200 includes:

[0038] The feedback force of the steering wheel at different speeds is calculated to generate a first force feedback strength test sequence, which includes feedback force data under three working conditions: low speed, medium speed, and high speed. The brake pedal, accelerator pedal, and rearview mirror in the basic operating components are traversed to obtain a second force feedback strength test sequence, a third force feedback strength test sequence, and a fourth force feedback strength test sequence. Based on the first force feedback strength test sequence, the second force feedback strength test sequence, the third force feedback strength test sequence, and the fourth force feedback strength test sequence, a multi-objective optimization algorithm is used to optimize the force feedback consistency to obtain the force feedback strength test sequence.

[0039] Specifically, during the test, the steering wheel feedback force was measured by simulating different vehicle speeds (low, medium, and high). The steering wheel feedback force typically changes as the speed increases. At low speeds, the feedback force is low; at medium speeds, the feedback force is high; and at high speeds, the feedback force decreases to improve handling stability. The steering wheel was rotated under these three operating conditions, and the feedback force exerted on the driver's hand was measured at different speeds. Based on the test data, the first force feedback strength test sequence was established, including feedback force data under low, medium, and high speed conditions.

[0040] Traverse the brake pedal, accelerator pedal, and rearview mirror in the basic operating components to generate the second, third, and fourth force feedback strength test sequences, respectively evaluating the force feedback strength of the brake pedal, accelerator pedal, and rearview mirror under different operating conditions. For example, for the brake pedal, test the feedback force at different pedal depths; for the accelerator pedal, test the feedback force at different pedal positions; and for the rearview mirror, test the feedback force at different adjustment angles.

[0041] According to the test sequence, feedback force data from different operating components (steering wheel, brake pedal, accelerator pedal, rearview mirror) under different working conditions are collected to obtain the first force feedback strength test sequence, the second force feedback strength test sequence, the third force feedback strength test sequence, and the fourth force feedback strength test sequence. A multi-objective optimization function is constructed, considering multiple objective functions at the same time, and finding a compromise solution by calculating the trade-offs between different objectives. The multi-objective optimization algorithm is an optimization method used to solve multi-objective problems. The goal is to find an optimal or near-optimal balance point between multiple objectives. For example, the objective function can include minimizing the fluctuation range of the feedback force of the steering wheel, brake pedal, accelerator pedal, and rearview mirror under low-speed, medium-speed, and high-speed conditions, and maintaining the same change trend of the feedback force under these conditions.

[0042] Multiple objective functions are comprehensively considered, and a compromise solution is obtained by calculating the trade-offs between each objective. Based on the feedback force data of each component (such as the steering wheel, brake pedal, accelerator pedal, and rearview mirror) under different operating conditions, the consistency and fluctuation range of the feedback force of each component are calculated. The feedback parameters of each component (such as feedback force intensity and response time) are then adjusted to ensure that their variation trends under different operating conditions are as consistent as possible. In other words, under the constraints of the multi-objective optimization function, the parameters of the basic operating components are continuously adjusted, the optimization results of all adjusted parameters are evaluated, and the optimal set of test sequences is selected: the force feedback intensity test sequence with the highest force feedback consistency. The force feedback intensity test sequence includes feedback force data from multiple operating components (such as the steering wheel, brake pedal, accelerator pedal, and rearview mirror) under different operating conditions (low, medium, and high speeds).

[0043] By optimizing the feedback force of the steering wheel, brake pedal, accelerator pedal and rearview mirror, and optimizing the feedback force of different operating components, consistency is improved. The driver can perceive similar force feedback when operating different components. The feedback force test of different components is covered in one test, which significantly improves test efficiency and reduces repeated testing processes.

[0044] Furthermore, the present application further comprises the following steps:

[0045] Response time data of the basic operating components are extracted and filtered to obtain a response time curve; a response time threshold is used to divide the response time curve into a first response time curve segment corresponding to a delay period, a second response time curve segment corresponding to a normal period, and a third response time curve segment corresponding to a high-speed period; and the response time test sequence is obtained based on the second response time curve segment corresponding to the normal period and the third response time curve segment corresponding to the high-speed period.

[0046] Specifically, the response time data of basic operating components is extracted. This refers to the time it takes for a basic vehicle component (such as the steering wheel, brake pedal, accelerator pedal, or rearview mirror) to respond after an operation. This time is typically the interval between the input signal (e.g., the driver's operation) and the output feedback (e.g., the vehicle's reaction). A filtering algorithm is used to smooth this response time data, eliminating fluctuations and noise in the data and creating a smoother and more accurate response time curve. For example, suppose during a test, when the driver manipulates the steering wheel, the response time data is [0.3 seconds, 0.25 seconds, 0.4 seconds, 0.35 seconds]. These data reflect the delay in the steering wheel's response. Smoothing this data using a low-pass filter (such as a Kalman filter or a mean filter) yields the smoothed curve data [0.3 seconds, 0.32 seconds, 0.33 seconds, 0.31 seconds], eliminating any abnormal fluctuations in the original data. After filtering, a response time curve is plotted to illustrate the temporal relationship between the operating command and the component's response.

[0047] Response time thresholds are set based on vehicle performance and industry standards, including delay periods, normal periods, and high-speed periods. Based on the preset response time thresholds, the response time curve is divided into different time periods: the first response time curve segment corresponding to the delay period (a period when the response time exceeds the set threshold), the second response time curve segment corresponding to the normal period (a period when the response time is within the normal threshold range), and the third response time curve segment corresponding to the high-speed period (a period with a lower response time and a quick response).

[0048] Ignoring the first response time curve segment corresponding to the delayed period, the final response time test sequence is formed based on data from the normal period (the second response time curve segment) and the high-speed period (the third response time curve segment). This serves as the basis for vehicle response time performance evaluation. Filtering removes noise and interference, resulting in a smoother response time curve that accurately reflects the system's true response. By categorizing response time into different time periods (delayed, normal, and high-speed), in-depth analysis of performance under different operating conditions allows identification of vehicle weaknesses and areas for improvement, improving testing efficiency and accelerating vehicle performance optimization.

[0049] Furthermore, the present application further comprises the following steps:

[0050] Based on the first response time curve segment corresponding to the delay period, the first state variable and the first covariance matrix are initialized; according to the second response time curve segment corresponding to the normal period, the state transfer function and the observation characteristics are set; based on the state transfer function and the observation characteristics, the first state variable and the first covariance matrix are updated to establish a first delay compensation loop.

[0051] Specifically, by establishing a delay compensation loop, the delay is dynamically compensated based on the response time data during the delay period, improving the accuracy and consistency of the response time. Based on the first response time curve segment corresponding to the delay period, the first state variable and the first covariance matrix are initialized. In a dynamic system, a state variable is a variable used to describe the current state of the system. It is typically used to describe the physical or control state of the vehicle and can help test the vehicle. The covariance matrix is ​​used to represent the correlation between system state variables and is widely used in estimation and filtering algorithms to reflect the uncertainty and accuracy of the system state.

[0052] Based on the second response time curve segment corresponding to the normal period, a state transfer function and observation characteristics are established. The state transfer function is a mathematical model that describes how the system state changes from one moment to the next. It is usually modeled based on the dynamic characteristics or historical data of the system and is used to predict the state at the next moment. The observation characteristics describe the measurement process of the system, that is, how to infer the state of the system through the observed values, reflect the relationship between the observed values ​​and the actual state variables, and define how to infer the internal state of the system from the measured output (such as response time data).

[0053] Based on the state transfer function and the observed characteristics, the first state variable and the first covariance matrix are updated. For example, assuming that the state transfer function is a linear relationship: k+1 =A×x k +B×u k , where x k is the current state variable, A is the state transfer matrix, u k is the input control signal; the observed feature is y k =H×x k +w k , where y k is the observation output, H is the observation matrix, w k Is the measurement noise. Through the Kalman filter algorithm, the estimated value and covariance matrix of the state variable are updated according to the new observation data.

[0054] Using the updated state variables and covariance matrix, a delay compensation loop is formed to dynamically adjust system delays, enabling the system to compensate for delays caused by input commands in real time. This process typically involves measuring the current state, estimating the delay state, updating the state, and compensating for the delay. By updating the state variables and covariance matrix based on the state transition function and observed characteristics, and establishing a delay compensation loop, dynamic adaptation to different delay scenarios is achieved, thereby improving the accuracy of delay compensation.

[0055] Furthermore, the present application further comprises the following steps:

[0056] According to the third response time curve segment corresponding to the high-speed period, the extreme points of the response time are identified, and key response characteristics are extracted, including the maximum response time, the minimum response time, and the average response time. Based on the key response characteristics, a second delay compensation loop is established with the goal of minimizing response time fluctuations and maximizing operational stability.

[0057] Specifically, based on the response time curve during the high-speed period, the extreme points in the response time are determined. The maximum and minimum values ​​in the response time curve represent the extreme responses within that period. Typically, the maximum response time represents the slowest moment of the system's response, the minimum response time represents the fastest moment of the system's response, and the average response time represents the average of the curve data. Key response characteristics are key performance indicators extracted from the response time curve, such as maximum response time, minimum response time, and average response time. They are used to evaluate the system's response performance under different operating conditions.

[0058] Based on the extracted key response characteristics, a second delay compensation loop was established with the goal of minimizing response time fluctuations and maximizing operational stability. Real-time adjustments were used to reduce response time variations, ensuring smooth vehicle operation during high-speed periods and avoiding excessive response fluctuations. Through multiple experiments and feedback, the parameters of the compensation loop were adjusted to ensure that response time fluctuations were effectively reduced and operational stability was significantly improved under different operating conditions (especially during high-speed periods). By dynamically optimizing the compensation parameters, the response time remained relatively stable during high-speed periods, avoiding large fluctuations. The control strategy was adaptively adjusted according to different driving environments (such as high and low speeds) to ensure optimal performance under various operating conditions.

[0059] Furthermore, the present application further comprises the following steps:

[0060] Defining response time fluctuation indicators , Operational stability index ;in, is the response time of the i-th test point, is the average response time, is the total number of test points, 、 is a stability adjustment parameter used to adjust the sensitivity of operational stability; according to the objective optimization function J, to minimize the response time fluctuation and maximize the operational stability, J( ) ,in, is a weight coefficient used to balance the priorities of response time fluctuation and operational stability.

[0061] Specifically, the response time fluctuation index is used to measure the degree of fluctuation of the system response time, that is, the standard deviation of the response time. The greater the fluctuation of the response time, the greater the instability. Operational stability is an indicator to measure the stability of the system during operation, which is used to indicate the degree of stability under the condition of response time fluctuation. , Operational stability index ;in, is the response time of the i-th test point, is the average response time, is the total number of test points, 、 It is a stability adjustment parameter used to adjust the sensitivity of operational stability. is the stability adjustment parameter, the sensitivity of the control system to stability, Is the stability threshold, used to adjust the benchmark for operational stability. A value of A higher value means that the system reacts more gradually to changes in stability.

[0062] The objective optimization function J comprehensively considers response time fluctuation and operation stability to minimize response time fluctuation and maximize operation stability at the same time, J( ) ,in, is the weight coefficient used to balance the priority of response time fluctuation and operation stability. When it is large, the optimization focus is to reduce the response time fluctuation; when When is small, the optimization focus is on improving operational stability. According to the objective optimization function J, the system parameters (such as control gain, response time threshold, etc.) are adjusted to minimize response time fluctuation and maximize operational stability.

[0063] By defining the response time fluctuation index and the operational stability index, the response time fluctuation and operational stability are considered at the same time, the vehicle response performance is optimized, the priority of response time fluctuation and operational stability is balanced, and the best optimization solution is found.

[0064] Furthermore, the present application further comprises the following steps:

[0065] The linearity indexes of the basic operating components are calculated and normalized to obtain standardized linearity data. Based on the standardized linearity data, a linearity feature matrix is ​​constructed, including a linearity feature vector of the steering wheel, a linearity feature vector of the brake pedal, and a linearity feature vector of the accelerator pedal. Based on the linearity feature matrix, an operation linearity test sequence is generated, wherein the operation linearity test sequence includes test points in a linear interval, a nonlinear interval, and a transition interval.

[0066] Specifically, the linearity index measures the relationship between the input and output of basic operating components (such as the steering wheel, brake pedal, and accelerator pedal). Higher linearity indicates a more direct and consistent relationship between the input and the system response. For each basic operating component (such as the steering wheel, brake pedal, and accelerator pedal), the linearity index is calculated by comparing the relationship between the input (such as angle change and pedal depth) and the vehicle response (such as steering wheel force feedback and vehicle speed changes).

[0067] Normalize the linearity metrics to bring them within a uniform range for easy comparison. A common normalization method involves linearly transforming the data based on its minimum and maximum values, so that the transformed range is [0, 1]. After normalization, the data is further standardized. Standardization subtracts the mean from the data and divides by the standard deviation, so that the data conforms to a standard normal distribution with a mean of 0 and a standard deviation of 1. Standardized linearity data is normalized linearity data, which unifies the linearity metrics of different operating components to the same scale for easier comparison and analysis.

[0068] The standardized linearity data calculated for each operating component (such as the steering wheel, brake pedal, and accelerator pedal) is organized into a characteristic matrix. Each row represents the linearity characteristic of a single operating component, and the columns in the matrix represent linearity data under different test conditions. The linearity characteristic matrix includes the linearity characteristic vectors for the steering wheel, brake pedal, and accelerator pedal—that is, the linearity characteristic vectors for each basic operating component.

[0069] Based on the linearity characteristic matrix, an operational linearity test sequence is generated, including linear, nonlinear, and transition intervals. Each interval corresponds to a different test point and is used to verify the system's performance in different operating ranges. The linear interval is the interval where the operational input and response output have a linear relationship; the nonlinear interval is the interval where the operational input and response output do not have a linear relationship, which typically occurs when the operating component approaches its extreme position; the transition interval is the interval between linear and nonlinear, typically the transition from the linear to the nonlinear interval. By identifying the linear, nonlinear, and transition intervals of the operating component, the test sequence accurately covers different operating conditions, ensuring stability and accuracy in different operating ranges.

[0070] In summary, the adaptive vehicle test case generation method provided by this application has the following technical effects:

[0071] By obtaining basic operating components of the vehicle to be tested, the basic operating components include a steering wheel, a brake pedal, an accelerator pedal, and a rearview mirror; based on the basic operating components, an initial whole vehicle test case is generated, and the initial whole vehicle test case includes a force feedback strength test sequence, a response time test sequence, and an operation linearity test sequence; based on the initial whole vehicle test case, a seat adjustment component is connected, and a left balance test sequence and a right balance test sequence are introduced, wherein the left balance test sequence includes P left balance test points, and the right balance test sequence includes Q right balance test points; based on the left balance test sequence and the right balance test sequence, the operation dynamic line characteristics of the basic operating components are analyzed, and the operation dynamic line characteristics include symmetrical balance dynamic line characteristics and linear dynamic line characteristics; under different operating environments, the operation dynamic line characteristics are added, and according to different operating instructions, the parameters are corrected in combination with the initial whole vehicle test case, and adaptive adjustments are made according to the industry standards of the vehicle to be tested to obtain adapted whole vehicle test cases. In other words, by designing force feedback intensity, response time and linearity test sequences based on basic operating components, and dynamically adjusting test cases according to different operating instructions and operating environments, we can ensure that the test case set always maintains a high test coverage rate, thereby significantly improving test efficiency.

[0072] Example 2: Based on the same inventive concept as the adaptive vehicle test case generation method in the aforementioned Example 1, this application also provides an adaptive vehicle test case generation system, see the attached Figure 2 , the adaptive vehicle test case generation system includes:

[0073] The basic component acquisition module 11 is used to obtain the basic operating components of the vehicle to be tested, and the basic operating components include a steering wheel, a brake pedal, an accelerator pedal, and a rearview mirror; the test case generation module 12 is used to generate an initial vehicle test case based on the basic operating components, and the initial vehicle test case includes a force feedback strength test sequence, a response time test sequence, and an operation linearity test sequence; the balance test introduction module 13 is used to connect the seat adjustment component based on the initial vehicle test case, and introduce a left balance test sequence and a right balance test sequence, wherein the left balance test sequence includes P The left balance test point and the right balance test sequence include Q right balance test points; the movement line feature analysis module 14 is used to analyze the operation movement line features of the basic operating component based on the left balance test sequence and the right balance test sequence, and the operation movement line features include symmetrical balance movement line features and linear movement line features; the test case determination module 15 is used to add the operation movement line features under different operating environments, and according to different operating instructions, perform parameter correction in combination with the initial vehicle test case, and perform adaptive adjustment according to the industry standard of the vehicle to be tested to obtain an adapted vehicle test case.

[0074] Furthermore, the test case generation module 12 in the adaptive vehicle test case generation system is further configured to:

[0075] The feedback force of the steering wheel at different speeds is calculated to generate a first force feedback strength test sequence, which includes feedback force data under three working conditions: low speed, medium speed, and high speed. The brake pedal, accelerator pedal, and rearview mirror in the basic operating components are traversed to obtain a second force feedback strength test sequence, a third force feedback strength test sequence, and a fourth force feedback strength test sequence. Based on the first force feedback strength test sequence, the second force feedback strength test sequence, the third force feedback strength test sequence, and the fourth force feedback strength test sequence, a multi-objective optimization algorithm is used to optimize the force feedback consistency to obtain the force feedback strength test sequence.

[0076] Furthermore, the test case generation module 12 in the adaptive vehicle test case generation system is further configured to:

[0077] Response time data of the basic operating components are extracted and filtered to obtain a response time curve; a response time threshold is used to divide the response time curve into a first response time curve segment corresponding to a delay period, a second response time curve segment corresponding to a normal period, and a third response time curve segment corresponding to a high-speed period; and the response time test sequence is obtained based on the second response time curve segment corresponding to the normal period and the third response time curve segment corresponding to the high-speed period.

[0078] Furthermore, the test case generation module 12 in the adaptive vehicle test case generation system is further configured to:

[0079] The linearity indexes of the basic operating components are calculated and normalized to obtain standardized linearity data. Based on the standardized linearity data, a linearity feature matrix is ​​constructed, including a linearity feature vector of the steering wheel, a linearity feature vector of the brake pedal, and a linearity feature vector of the accelerator pedal. Based on the linearity feature matrix, an operation linearity test sequence is generated, wherein the operation linearity test sequence includes test points in a linear interval, a nonlinear interval, and a transition interval.

[0080] Furthermore, the adaptive vehicle test case generation system further includes a first compensation module, which is further configured to:

[0081] Based on the first response time curve segment corresponding to the delay period, the first state variable and the first covariance matrix are initialized; according to the second response time curve segment corresponding to the normal period, the state transfer function and the observation characteristics are set; based on the state transfer function and the observation characteristics, the first state variable and the first covariance matrix are updated to establish a first delay compensation loop.

[0082] Furthermore, the adaptive vehicle test case generation system further includes a second compensation module, which is further configured to:

[0083] According to the third response time curve segment corresponding to the high-speed period, the extreme points of the response time are identified, and key response characteristics are extracted, including the maximum response time, the minimum response time, and the average response time. Based on the key response characteristics, a second delay compensation loop is established with the goal of minimizing response time fluctuations and maximizing operational stability.

[0084] Furthermore, the adaptive vehicle test case generation system further includes a target optimization module, which is further configured to:

[0085] Defining response time fluctuation indicators , Operational stability index ;in, is the response time of the i-th test point, is the average response time, is the total number of test points, 、 is a stability adjustment parameter used to adjust the sensitivity of operational stability; according to the objective optimization function J, to minimize the response time fluctuation and maximize the operational stability, J( ) ,in, is a weight coefficient used to balance the priorities of response time fluctuation and operational stability.

[0086] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1 The adaptive vehicle test case generation method and specific examples in Example 1 are also applicable to the adaptive vehicle test case generation system of this embodiment. Through the detailed description of the adaptive vehicle test case generation method above, those skilled in the art will clearly understand the adaptive vehicle test case generation system of this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. As for the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant details, please refer to the method description.

[0087] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

[0088] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. Adaptive vehicle test case generation method, characterized in that: include: Obtaining basic operating components of the vehicle to be tested, wherein the basic operating components include a steering wheel, a brake pedal, an accelerator pedal, and a rearview mirror; Based on the basic operating components, an initial vehicle test case is generated, wherein the initial vehicle test case includes a force feedback strength test sequence, a response time test sequence, and an operation linearity test sequence; Based on the initial vehicle test case, the seat adjustment assembly is connected, and a left balance test sequence and a right balance test sequence are introduced, wherein the left balance test sequence includes P left balance test points, and the right balance test sequence includes Q right balance test points; Based on the left balance test sequence and the right balance test sequence, analyzing the operation dynamic line characteristics of the basic operating component, the operation dynamic line characteristics including symmetrical balance dynamic line characteristics and linear dynamic line characteristics; Under different operating environments, the operating line features are added, and according to different operating instructions, parameters are modified in combination with the initial vehicle test case, and adaptive adjustments are made according to the industry standards of the vehicle to be tested to obtain an adapted vehicle test case; The initial vehicle test cases include a force feedback strength test sequence, a response time test sequence, and an operation linearity test sequence, including: Calculating the feedback force of the steering wheel at different speeds to generate a first force feedback strength test sequence, wherein the first force feedback strength test sequence includes feedback force data under three working conditions: low speed, medium speed, and high speed; Traversing the brake pedal, the accelerator pedal, and the rearview mirror among the basic operating components to obtain a second force feedback strength test sequence, a third force feedback strength test sequence, and a fourth force feedback strength test sequence; Based on the first force feedback strength test sequence, the second force feedback strength test sequence, the third force feedback strength test sequence, and the fourth force feedback strength test sequence, a multi-objective optimization algorithm is used to perform force feedback consistency optimization to obtain the force feedback strength test sequence; The initial vehicle test cases include a force feedback strength test sequence, a response time test sequence, and an operation linearity test sequence, including: Extracting response time data of the basic operating components, performing filtering processing, and obtaining a response time curve; Using a response time threshold, the response time curve is divided into a first response time curve segment corresponding to a delay period, a second response time curve segment corresponding to a normal period, and a third response time curve segment corresponding to a high-speed period; Obtaining the response time test sequence according to the second response time curve segment corresponding to the normal period and the third response time curve segment corresponding to the high-speed period; The initial vehicle test cases include a force feedback strength test sequence, a response time test sequence, and an operation linearity test sequence, including: Calculating a linearity index of the basic operating component, and performing normalization processing on the linearity index to obtain standardized linearity data; Based on the standardized linearity data, a linearity feature matrix is ​​constructed, including a linearity feature vector of a steering wheel, a linearity feature vector of a brake pedal, and a linearity feature vector of an accelerator pedal; Generate an operation linearity test sequence according to the linearity characteristic matrix, wherein the operation linearity test sequence includes test points in a linear interval, a nonlinear interval, and a transition interval; The response time curve is divided into a first response time curve segment corresponding to a delay period, a second response time curve segment corresponding to a normal period, and a third response time curve segment corresponding to a high-speed period using a response time threshold, including: Initializing a first state variable and a first covariance matrix based on a first response time curve segment corresponding to the delay period; setting a state transfer function and an observation characteristic according to a second response time curve segment corresponding to the normal period; Based on the state transfer function and the observation characteristics, the first state variable and the first covariance matrix are updated, and a first delay compensation loop is established.

2. The adaptive vehicle test case generation method according to claim 1, characterized in that: identifying extreme points of the response time according to the third response time curve segment corresponding to the high-speed period, and extracting key response features, including maximum response time, minimum response time, and average response time; Based on the key response characteristics, a second delay compensation loop is established with the goal of minimizing response time fluctuations and maximizing operation stability.

3. The adaptive vehicle test case generation method according to claim 2, characterized in that: include: Defining response time fluctuation indicators , Operational stability index ; in, is the response time of the i-th test point, is the average response time, is the total number of test points, 、 It is a stability adjustment parameter used to adjust the sensitivity of operational stability; According to the objective optimization function J, to simultaneously minimize response time fluctuation and maximize operational stability, J( ) ,in, is a weight coefficient used to balance the priorities of response time fluctuation and operational stability.

4. Adaptive vehicle test case generation system, characterized by: For implementing the steps of the method for generating an adaptive vehicle test case according to any one of claims 1 to 3, the adaptive vehicle test case generation system comprises: A basic component acquisition module is used to acquire basic operating components of the vehicle to be tested, wherein the basic operating components include a steering wheel, a brake pedal, an accelerator pedal, and a rearview mirror; A test case generation module, configured to generate an initial vehicle test case based on the basic operating components, wherein the initial vehicle test case includes a force feedback strength test sequence, a response time test sequence, and an operation linearity test sequence; a balance test introduction module, configured to connect the seat adjustment assembly and introduce a left balance test sequence and a right balance test sequence based on the initial vehicle test case, wherein the left balance test sequence includes P left balance test points and the right balance test sequence includes Q right balance test points; A dynamic line feature analysis module, configured to analyze the operation dynamic line features of the basic operating member based on the left balance test sequence and the right balance test sequence, wherein the operation dynamic line features include symmetrical balance dynamic line features and linear dynamic line features; The test case determination module is used to add the operation line features under different operating environments, and according to different operating instructions, perform parameter correction in combination with the initial vehicle test case, and perform adaptation and adjustment according to the industry standards of the vehicle to be tested to obtain an adapted vehicle test case.

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