Electric control braking system function safety evaluation method and device under longitudinal slope condition

Abstract

The embodiment of the invention discloses a function safety evaluation method and device for an electric control braking system under a longitudinal slope condition. The method comprises the following steps: estimating the road slope of the intelligent networked vehicle in the vehicle driving process based on an unscented Kalman filtering algorithm; constructing a safety test case library of the functions of the electric control braking system under the longitudinal slope condition; constructing a safety evaluation index of the electric control braking system function under the longitudinal slope condition; and receiving a test instruction, selecting each basic component element from the safety test case library to construct a test working condition, determining a subjective evaluation score and an objective evaluation score of the intelligent networked automobile under the test working condition, and obtaining a comprehensive scoring result. According to the embodiment of the invention, the function safety evaluation of the electric control braking system under the longitudinal slope condition is realized, and potential hazards and influences on driving safety after system faults and partial function failures of the electric control braking system under the road environment working condition formed by different basic composition elements can be better reflected.

Description

Technical Field

[0001] One or more embodiments of the present specification relate to intelligent connected vehicle testing technology, and in particular, to a method and device for functional safety evaluation of an electric control braking system under longitudinal slope conditions. Background Art

[0002] Intelligent connected vehicles have become an important direction for the development of the automotive industry, and the technological innovation and market changes it brings are constantly driving the progress of the entire industry. As an important part of connected vehicles, the electronic control braking system can achieve faster braking response and more accurate control precision.

[0003] With the rapid popularization of intelligent and electrified technologies, highly integrated and electronic automotive parts and in-vehicle controllers are subject to the risk of systematic failure and random hardware failure. Accordingly, the functional safety of electronic control braking systems has attracted great attention at home and abroad. Test evaluation is an important means to ensure the stable, safe and reliable performance of electronic control braking systems. Carrying out functional safety testing of electronic control braking systems plays an important role in ensuring vehicle driving safety and improving system performance.

[0004] However, the current functional safety test of the electronic control brake system is mainly aimed at horizontal, dry concrete or asphalt roads, and does not involve the functional safety test of the brake system under uphill and downhill conditions. Therefore, there is an urgent need for a functional safety evaluation method for the electronic control brake system under longitudinal slope conditions for vehicle hazard events such as unexpected deceleration and unexpected deceleration capacity reduction. Summary of the invention

[0005] In order to solve the above problems, one or more embodiments of the present specification describe a method and device for functional safety evaluation of an electric control braking system under longitudinal slope conditions.

[0006] According to a first aspect, a method for functional safety evaluation of an electric control braking system under longitudinal slope conditions is provided, the method comprising:

[0007] Predict the road slope of intelligent connected vehicles during driving based on the unscented Kalman filter algorithm;

[0008] Construct a safety test case library for the function of the electronic control brake system under a longitudinal slope condition, wherein the basic components of the functional safety test case library include vehicle hazards, injected fault types, fault injection time, and vehicle driving speed. The vehicle hazards include unexpected deceleration and unexpected deceleration capacity reduction. The injected fault type is determined based on the hazard type of the vehicle hazards, and the vehicle driving speed is determined based on the road slope.

[0009] Constructing safety evaluation indicators of the function of the electronic control braking system under longitudinal slope conditions, the safety evaluation indicators include subjective evaluation indicators and objective evaluation indicators, the subjective evaluation indicators include vehicle controllability after fault injection, instrument panel fault light display during fault injection, braking response speed after fault injection, braking direction stability after fault injection, and brake pedal feel after fault injection, and the objective evaluation indicators include braking stability, lateral error, average deceleration, and fault takeover time after fault injection;

[0010] Receive a test instruction, select each of the basic components from the safety test case library to construct this test condition, determine the subjective evaluation score and the objective evaluation score of the intelligent connected vehicle under the test condition, and obtain a comprehensive scoring result.

[0011] Preferably, the estimating the road slope of the intelligent connected vehicle during vehicle driving based on the unscented Kalman filter algorithm includes:

[0012] Acquiring vehicle force data of an intelligent network-connected vehicle traveling on a slope, and constructing a longitudinal dynamics model of the intelligent network-connected vehicle based on the vehicle force data;

[0013] A road slope recursive model based on unscented Kalman filtering is constructed according to the longitudinal dynamics model, and the road slope during vehicle driving is determined according to the road slope recursive model.

[0014] Preferably, constructing a road slope recursive model based on unscented Kalman filtering according to the longitudinal dynamics model, and determining the road slope during vehicle driving according to the road slope recursive model, comprises:

[0015] Determining a time update equation and a measurement update equation of the intelligent connected vehicle according to the longitudinal dynamics model;

[0016] Constructing a road slope recursive model based on unscented Kalman filtering based on the time update equation and the measurement update equation;

[0017] The road surface slope during vehicle driving is determined according to the longitudinal dynamics model and the road slope recursive model.

[0018] Preferably, determining the time update equation and the measurement update equation of the intelligent connected vehicle according to the longitudinal dynamics model includes:

[0019] Determining a system state quantity of the intelligent connected vehicle according to the longitudinal dynamics model;

[0020] After discretizing the system state quantity, generating a system state equation and a system measurement equation based on the system state quantity;

[0021] The time update equation and the measurement update equation of the intelligent connected vehicle are determined according to the system state equation and the system measurement equation.

[0022] Preferably, the injected fault type includes a parking control device failure, a throttle depth signal failure and a communication interface failure when the hazard type of the whole vehicle hazard is unexpected deceleration; the injected fault type includes a wheel speed sensor signal failure, a communication interface failure, a power supply failure and a motor feedback signal torque failure when the hazard type of the whole vehicle hazard is unexpected deceleration capability reduction.

[0023] Preferably, determining the subjective evaluation score and the objective evaluation score of the intelligent connected vehicle under the current test condition includes:

[0024] Acquire actual driving data of the intelligent connected vehicle under the test condition, and calculate an objective evaluation score based on the actual driving data;

[0025] Obtain the subjective evaluation score sent by the test terminal corresponding to the tester of the intelligent connected vehicle.

[0026] Preferably, after obtaining the comprehensive scoring result, the method further includes:

[0027] Record the comprehensive scoring result corresponding to the test condition.

[0028] According to a second aspect, a functional safety evaluation device for an electric control braking system under a longitudinal slope condition is provided, the device comprising:

[0029] An estimation module is used to estimate the road slope of an intelligent connected vehicle during its driving process based on an unscented Kalman filter algorithm;

[0030] The first construction module is used to construct a safety test case library of the electronic control brake system function under a longitudinal slope condition, wherein the basic components of the functional safety test case library include vehicle hazards, injected fault types, fault injection time, and vehicle driving speed. The vehicle hazards include unexpected deceleration and unexpected deceleration capacity reduction. The injected fault type is determined based on the hazard type of the vehicle hazards, and the vehicle driving speed is determined based on the road slope.

[0031] The second construction module is used to construct safety evaluation indicators of the electronic control braking system function under longitudinal slope conditions, wherein the safety evaluation indicators include subjective evaluation indicators and objective evaluation indicators, wherein the subjective evaluation indicators include vehicle controllability after fault injection, instrument panel fault light display during fault injection, brake response speed after fault injection, brake direction stability after fault injection, and brake pedal feel after fault injection, and the objective evaluation indicators include brake stability after fault injection, lateral error, average deceleration, and fault takeover time;

[0032] The evaluation module is used to receive the test instruction, select each of the basic components from the safety test case library to construct the test condition, determine the subjective evaluation score and the objective evaluation score of the intelligent connected vehicle under the test condition, and obtain a comprehensive scoring result.

[0033] According to a third aspect, there is provided an electronic device, comprising a processor and a memory;

[0034] The processor is connected to the memory;

[0035] The memory is used to store executable program code;

[0036] The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the steps of the method provided in the first aspect or any possible implementation manner of the first aspect.

[0037] According to a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and the computer-readable storage medium stores instructions, which, when the instructions are executed on a computer or a processor, cause the computer or the processor to execute the method provided in the first aspect or any possible implementation of the first aspect.

[0038] The method and device provided in the embodiments of this specification realize the functional safety evaluation of the electric control braking system under longitudinal slope conditions, and can better reflect the potential hazards and impacts on driving safety after the electric control braking system has system failures and partial functional failures under road environment conditions composed of different basic components. Compared with a single evaluation index, the present invention proposes multi-dimensional subjective and objective evaluation indicators for the functional safety performance of the electric control braking system, thereby realizing a more reliable and comprehensive scientific quantitative evaluation of the functional safety of the electric control braking system. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 It is a flow chart of a method for functional safety evaluation of an electric control braking system under a longitudinal slope condition in one embodiment of the present specification.

[0041] Figure 2It is a schematic diagram of the structure of a functional safety evaluation device for an electric control braking system under a longitudinal slope condition in one embodiment of the present specification.

[0042] Figure 3 It is a structural diagram of an electronic device in one embodiment of this specification. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0044] In the following introduction, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. The following introduction provides multiple embodiments of the present application, and different embodiments can be replaced or combined, so the present application can also be considered to include all possible combinations of the same and / or different embodiments recorded. Therefore, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then the present application should also be considered to include embodiments containing one or more of all other possible combinations of A, B, C, and D, although the embodiment may not be clearly recorded in the following text.

[0045] The following description provides examples and does not limit the scope, applicability or examples set forth in the claims. Changes may be made to the functions and arrangements of the elements described without departing from the scope of the present application. Various processes or components may be appropriately omitted, substituted or added to each example. For example, the described method may be performed in an order different from the order described, and various steps may be added, omitted or combined. In addition, features described in some examples may be combined in other examples.

[0046] See also Figure 1 , Figure 1 : is a flow chart of a method for functional safety evaluation of an electric control brake system under longitudinal slope conditions provided in an embodiment of the present application. In the embodiment of the present application, the method includes:

[0047] S101. Estimate the road slope of an intelligent connected vehicle during its driving process based on an unscented Kalman filter algorithm.

[0048] The execution entity of this application can be a cloud server.

[0049] In an embodiment of the present specification, the cloud server can obtain relevant driving and force data of the intelligent networked vehicle on the ramp, such as driving force, air resistance, slope resistance, rolling resistance, engine torque, etc., to calculate and represent the road slope of the ramp where the intelligent networked vehicle is located. In this calculation process, in order to more accurately estimate the road slope, an unscented Kalman filter algorithm is also used. Unscented Kalman Filter (UKF) is a nonlinear Kalman filtering method, and its core idea is to approximate the probability distribution of the system through a set of sample points called sigma points. These sigma points are selected according to the mean and covariance of the system, and after nonlinear transformation, they can be used to approximate the new mean and covariance without calculating the Jacobian matrix or Taylor series expansion, thus avoiding the linearization process required in the extended Kalman filter, thereby reducing the problem of error accumulation and improving the accuracy of the final estimation of the road slope.

[0050] In one possible implementation, the method of estimating the road slope of the intelligent connected vehicle during vehicle driving based on the unscented Kalman filter algorithm includes:

[0051] Acquiring vehicle force data of an intelligent network-connected vehicle traveling on a slope, and constructing a longitudinal dynamics model of the intelligent network-connected vehicle based on the vehicle force data;

[0052] A road slope recursive model based on unscented Kalman filtering is constructed according to the longitudinal dynamics model, and the road slope during vehicle driving is determined according to the road slope recursive model.

[0053] In the embodiment of this specification, the cloud server first performs a force analysis on the intelligent network-connected vehicle traveling on a slope to obtain the vehicle force data, and uses this to construct a longitudinal dynamics model of the vehicle. The longitudinal dynamics model can be expressed as:

[0054] ma=F w -F a -F g -F μ

[0055] Among them, F w Indicates driving force, F a Indicates air resistance, F g Indicates slope resistance, F μ represents rolling resistance, a represents acceleration, and m represents the mass of the car.

[0056] Expanding the above formula, we can get:

[0057]

[0058] Among them, Te represents the engine torque, i0 represents the transmission ratio of the main reducer, η represents the transmission efficiency, R represents the wheel radius, C d Represents the air resistance coefficient, A w represents the frontal area, ρ represents the air density, v represents the vehicle speed, g represents the acceleration due to gravity, f represents the rolling resistance coefficient, θ represents the road slope, and δ represents the rotational mass conversion coefficient.

[0059] The above formula can be used to determine the relationship between the remaining data in the longitudinal dynamics model and the road slope, and then a road slope recursive model based on unscented Kalman filtering can be constructed. Finally, the road slope of the section where the intelligent connected vehicle is actually tested will be estimated based on this model.

[0060] In one possible implementation, constructing a road slope recursive model based on an unscented Kalman filter according to the longitudinal dynamics model, and determining the road slope during vehicle driving according to the road slope recursive model, includes:

[0061] Determining a time update equation and a measurement update equation of the intelligent connected vehicle according to the longitudinal dynamics model;

[0062] Constructing a road slope recursive model based on unscented Kalman filtering based on the time update equation and the measurement update equation;

[0063] The road surface slope during vehicle driving is determined according to the longitudinal dynamics model and the road slope recursive model.

[0064] In the embodiment of the present specification, the road slope recursive model based on the unscented Kalman filter can be specifically composed of a time update equation and a measurement update equation, and the time update equation and the measurement update equation can be specifically determined according to the parameters obtained from the longitudinal dynamics model. The time update equation can be expressed as the following two equations:

[0065]

[0066] Where A represents the state transfer matrix, Represents the estimated value of the state quantity at time k.

[0067]

[0068] in, represents the prior error covariance matrix at time k, the superscript T represents the matrix transpose, Q k-1 Represents the noise covariance matrix at time k-1.

[0069] The measurement update equation can be expressed as:

[0070]

[0071] Among them, ε k represents the residual at time k, Z k represents the observed value at time k, K k represents the gain matrix at time k, H represents the observation matrix, r k-1 represents the observation noise at time k-1, P k represents the posterior error covariance matrix at time k, R k-1 represents the noise covariance matrix at time k-1, and l represents the identity matrix.

[0072] According to the longitudinal dynamics model and road slope recursive model constructed above, the road slope θ during vehicle driving can be accurately and real-time recursively calculated.

[0073] In one possible implementation, determining the time update equation and the measurement update equation of the intelligent connected vehicle according to the longitudinal dynamics model includes:

[0074] Determining a system state quantity of the intelligent connected vehicle according to the longitudinal dynamics model;

[0075] After discretizing the system state quantity, generating a system state equation and a system measurement equation based on the system state quantity;

[0076] The time update equation and the measurement update equation of the intelligent connected vehicle are determined according to the system state equation and the system measurement equation.

[0077] In the embodiments of this specification, the system state quantity of the intelligent network-connected vehicle can be expressed as:

[0078] x k =[v k θ k ] T

[0079] Among them, x k represents the state quantity at time k, v k represents the vehicle speed at time k, θ k Represents the road slope at the vehicle's location at time k.

[0080] Discretize the system state quantity and get:

[0081]

[0082] Wherein, Δt represents the sampling interval.

[0083] The system state equation after discretization is:

[0084]

[0085] Among them, q k-1 represents the state estimation noise at time k-1, Represents the differential of the velocity at time k-1.

[0086] The system observation equation is:

[0087] Z k =Hx k +r k-1

[0088] Among them, Z(k) represents the observation equation at time k, H represents the observation matrix, and r k-1 represents the observation noise at time k-1.

[0089] Then, the time update equation and the measurement update equation can be determined based on the system state equation and the system observation equation.

[0090] S102. Construct a safety test case library for the electric control braking system function under longitudinal slope conditions.

[0091] Among them, the basic components of the functional safety test case library include vehicle hazards, injected fault types, fault injection time and vehicle speed. The vehicle hazards include unexpected deceleration and unexpected deceleration capacity reduction. The injected fault type is determined based on the hazard type of the vehicle hazards, and the vehicle speed is determined based on the road slope.

[0092] In the embodiments of this specification, a safety test case library of the electric control brake system function under longitudinal slope conditions will also be constructed, which includes several different basic components, each of which has at least two parameters. By selecting one of each basic component and combining them, a test condition can be formed. Fault injection time includes: 500ms, 1000ms, 1500ms and 2000ms. Vehicle speed includes 20km / h, 30km / h, 40km / h, 50km / h, 60km / h, ..., v g，max .

[0093] Among them, v g，max Indicates the upper limit of the vehicle speed when going uphill, which can be calculated by the following formula:

[0094] v g，max =v max (1-βsinθ)

[0095] Among them, v max It indicates the upper limit of the speed of the wheels on a flat road, in meters per second, and β indicates the speed coefficient.

[0096] In one possible implementation, the injected fault type includes a parking control device failure, a throttle depth signal failure and a communication interface failure when the hazard type of the whole vehicle hazard is unexpected deceleration, and the injected fault type includes a wheel speed sensor signal failure, a communication interface failure, a power supply failure and a motor feedback signal torque failure when the hazard type of the whole vehicle hazard is an unexpected decrease in deceleration capability.

[0097] In the embodiments of this specification, the specific optional categories corresponding to the injected fault types are different according to the different vehicle hazards selected. The injected fault types in the unexpected deceleration scenario include parking control device failure, throttle depth signal failure, and communication interface failure. The injected fault types in the unexpected deceleration capability reduction scenario include but are not limited to wheel speed sensor signal failure, communication interface failure, power supply failure, and motor feedback signal torque failure.

[0098] S103. Construct safety evaluation indicators for the electric control braking system function under longitudinal slope conditions.

[0099] Among them, the safety evaluation indicators include subjective evaluation indicators and objective evaluation indicators. The subjective evaluation indicators include vehicle controllability after fault injection, instrument panel fault light display during fault injection, braking response speed after fault injection, braking direction stability after fault injection and brake pedal feel after fault injection. The objective evaluation indicators include braking stability, lateral error, average deceleration and fault takeover time after fault injection.

[0100] In the embodiments of this specification, in order to avoid evaluation bias caused by a single factor, subjective and objective comprehensive evaluation indicators are also introduced. Subjective evaluation indicators include:

[0101] S1 = [1, 2, 3]

[0102] Where S1 represents the controllability of the vehicle after fault injection, S1=1 means that the pedal suddenly changes and is uncontrollable or deviates from the lane, S1=2 means that the pedal is controllable and the pedal force changes, and S1=3 means no change.

[0103] S2 = [1, 2]

[0104] Wherein, S2 represents the display of the instrument panel fault light when the fault is injected, S2=1 represents that the instrument panel fault light is off, and S2=2 represents that the instrument panel fault light is on.

[0105] S3 = [1, 2, 3]

[0106] Wherein, S3 represents the braking response speed after fault injection, S3=1 represents a sharp braking response speed, S3=2 represents a relatively sharp response speed, and S3=3 represents a smooth response speed.

[0107] S4 = [1, 2, 3]

[0108] Where S4 represents the braking directional stability after fault injection, S4=1 indicates a large degree of sideslip, S4=2 indicates a certain degree of sideslip, and S4=3 indicates no sideslip.

[0109] S5 = [1, 2, 3]

[0110] Wherein, S5 represents the brake pedal feeling after fault injection, S5=1 means that the brake pedal feels too heavy or too light, S5=2 means that the brake pedal feels heavy or light, and S5=3 means that the brake pedal feels unchanged.

[0111] Objective evaluation indicators include:

[0112]

[0113] In the formula, ω MSE Represents the braking stability after fault injection, which is used to evaluate the stability from fault injection to vehicle stopping. k represents the yaw angular velocity at time k, which can be measured by the gyroscope, ω μ represents the expected yaw rate, and i represents the number of sampling points from fault injection to vehicle stopping.

[0114]

[0115] In the formula, σ MSE Represents the lateral error, which is used to evaluate the degree to which the vehicle deviates from the center line of the lane, σ k represents the distance between the center of mass of the vehicle and the center line of the lane at time k, σ μ Represents the expected distance between the vehicle's center of mass and the lane centerline.

[0116]

[0117] Where MFDD represents the fully exerted average deceleration, which is used to describe the average deceleration of the vehicle during the entire braking process, and u b Indicates 0.8 times the initial braking speed, u e Indicates 0.1 times the initial braking speed, s b Indicates the test vehicle speed from the initial braking speed to u b The distance between vehicles, s e Indicates the test vehicle speed from the initial braking speed to u e The distance between vehicles.

[0118] T p =T s -T b

[0119] Where, T p It represents the fault takeover time, which is used to evaluate the time required for the vehicle to reach the minimum risk state. s represents the fault injection time, T b Indicates the time when the instrument fault light signal is on.

[0120] S104, receiving a test instruction, selecting each of the basic components from the safety test case library to construct this test condition, determining a subjective evaluation score and an objective evaluation score of the intelligent connected vehicle under the test condition, and obtaining a comprehensive scoring result.

[0121] In the embodiment of this specification, when conducting a functional safety test of the electronic control brake system under longitudinal slope conditions, the cloud server will first receive the test instructions sent by the user, and then select the basic components from the safety test case library, that is, select sub-elements from the elements such as vehicle hazards, fault types, fault injection time, and vehicle driving speed to form this test condition. Then, the professional driver drives the vehicle under test on the ramp at the required speed. During this period, the fault is injected using the fault injection equipment installed on the vehicle. Finally, based on the vehicle performance after the fault is injected, the professional driver gives a subjective evaluation score and records the objective evaluation score generated based on the vehicle data. The subjective evaluation score and the objective evaluation score are integrated together to obtain a comprehensive scoring result.

[0122] In one possible implementation manner, determining the subjective evaluation score and the objective evaluation score of the intelligent connected vehicle under the current test condition includes:

[0123] Acquire actual driving data of the intelligent connected vehicle under the test condition, and calculate an objective evaluation score based on the actual driving data;

[0124] Obtain the subjective evaluation score sent by the test terminal corresponding to the tester of the intelligent connected vehicle.

[0125] In the embodiment of this specification, the cloud server will obtain the actual driving data of the intelligent networked vehicle under the test conditions to calculate the objective evaluation score. At the same time, the tester corresponding to the vehicle will also select the score for each subjective evaluation indicator through the test terminal used during the test, and the cloud server can directly obtain the subjective evaluation score through the test terminal.

[0126] In one possible implementation, after obtaining the comprehensive scoring result, the method further includes:

[0127] Record the comprehensive scoring result corresponding to the test condition.

[0128] In the embodiment of this specification, the cloud server will also automatically record the comprehensive score results of this test condition, rather than simply displaying it. In this way, as different test conditions are tested, the comprehensive score results corresponding to all test conditions can be recorded in the end, which is convenient for staff to compare and analyze.

[0129] The following will be combined with the attached Figure 2 , the functional safety evaluation device of the electric control braking system under the longitudinal slope condition provided by the embodiment of the present application is introduced in detail. It should be noted that the attached Figure 2 The functional safety evaluation device of the electric control braking system under the longitudinal slope condition shown is used to perform the application Figure 1 For the convenience of explanation, only the part related to the embodiment of the present application is shown. For the specific technical details not disclosed, please refer to the present application. Figure 1 The embodiment shown.

[0130] See also Figure 2 , Figure 2 Schematic diagram of the structure of a functional safety evaluation device for an electric control braking system under a longitudinal slope condition provided by an embodiment of the present application. Figure 2 As shown, the device comprises:

[0131] An estimation module 201 is used to estimate the road slope of the intelligent connected vehicle during the vehicle driving process based on an unscented Kalman filter algorithm;

[0132] The first construction module 202 is used to construct a safety test case library of the electric control brake system function under a longitudinal slope condition, wherein the basic components of the functional safety test case library include vehicle hazards, injected fault types, fault injection time, and vehicle driving speed. The vehicle hazards include unexpected deceleration and unexpected deceleration capacity reduction. The injected fault type is determined based on the hazard type of the vehicle hazards, and the vehicle driving speed is determined based on the road slope.

[0133] The second construction module 203 is used to construct safety evaluation indicators of the electronic control brake system function under longitudinal slope conditions, wherein the safety evaluation indicators include subjective evaluation indicators and objective evaluation indicators, wherein the subjective evaluation indicators include vehicle controllability after fault injection, instrument panel fault light display during fault injection, brake response speed after fault injection, brake direction stability after fault injection, and brake pedal feel after fault injection, and the objective evaluation indicators include brake stability after fault injection, lateral error, average deceleration, and fault takeover time;

[0134] The evaluation module 204 is used to receive the test instruction, select each of the basic components from the safety test case library to construct the test condition, determine the subjective evaluation score and the objective evaluation score of the intelligent connected vehicle under the test condition, and obtain a comprehensive scoring result.

[0135] In one possible implementation, the estimation module 201 is specifically used for:

[0136] Acquiring vehicle force data of an intelligent network-connected vehicle traveling on a slope, and constructing a longitudinal dynamics model of the intelligent network-connected vehicle based on the vehicle force data;

[0137] A road slope recursive model based on unscented Kalman filtering is constructed according to the longitudinal dynamics model, and the road slope during vehicle driving is determined according to the road slope recursive model.

[0138] In one possible implementation, the estimation module 201 is specifically used for:

[0139] Determining a time update equation and a measurement update equation of the intelligent connected vehicle according to the longitudinal dynamics model;

[0140] Constructing a road slope recursive model based on unscented Kalman filtering based on the time update equation and the measurement update equation;

[0141] The road surface slope during vehicle driving is determined according to the longitudinal dynamics model and the road slope recursive model.

[0142] In one possible implementation, the estimation module 201 is specifically used for:

[0143] Determining a system state quantity of the intelligent connected vehicle according to the longitudinal dynamics model;

[0144] After discretizing the system state quantity, generating a system state equation and a system measurement equation based on the system state quantity;

[0145] The time update equation and the measurement update equation of the intelligent connected vehicle are determined according to the system state equation and the system measurement equation.

[0146] In one possible implementation, the injected fault type includes a parking control device failure, a throttle depth signal failure and a communication interface failure when the hazard type of the whole vehicle hazard is unexpected deceleration, and the injected fault type includes a wheel speed sensor signal failure, a communication interface failure, a power supply failure and a motor feedback signal torque failure when the hazard type of the whole vehicle hazard is an unexpected decrease in deceleration capability.

[0147] In one possible implementation, the evaluation module 204 is specifically used to:

[0148] Acquire actual driving data of the intelligent connected vehicle under the test condition, and calculate an objective evaluation score based on the actual driving data;

[0149] Obtain the subjective evaluation score sent by the test terminal corresponding to the tester of the intelligent connected vehicle.

[0150] In one possible implementation, the evaluation module 204 is further configured to:

[0151] Record the comprehensive scoring result corresponding to the test condition.

[0152] Those skilled in the art can clearly understand that the technical solutions of the embodiments of the present application can be implemented with the help of software and / or hardware. The "unit" and "module" in this specification refer to software and / or hardware that can independently complete or cooperate with other components to complete specific functions, where the hardware can be, for example, a field programmable gate array (Field-Programmable Gate Array, FPGA), an integrated circuit (Integrated Circuit, IC), etc.

[0153] Each processing unit and / or module of the embodiments of the present application may be implemented by an analog circuit that implements the functions described in the embodiments of the present application, or may be implemented by software that executes the functions described in the embodiments of the present application.

[0154] See also Figure 3 , which shows a schematic diagram of the structure of an electronic device involved in an embodiment of the present application, the electronic device can be used to implement Figure 1 The method in the embodiment shown. Figure 3 As shown, the electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0155] The communication bus 302 is used to realize the connection and communication between these components.

[0156] The user interface 303 may include a display screen (Display) and a camera (Camera). The optional user interface 303 may also include a standard wired interface and a wireless interface.

[0157] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0158] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts within the entire electronic device 300, and executes various functions and processes data of the electronic device 300 by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics central processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0159] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 305 may also be optionally at least one storage device located away from the aforementioned processor 301. As Figure 3 As shown, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and program instructions.

[0160] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the electric control brake system functional safety evaluation application under the longitudinal slope condition stored in the memory 305, and specifically perform the following operations:

[0161] Predict the road slope of intelligent connected vehicles during driving based on the unscented Kalman filter algorithm;

[0162] Construct a safety test case library for the function of the electronic control brake system under a longitudinal slope condition, wherein the basic components of the functional safety test case library include vehicle hazards, injected fault types, fault injection time, and vehicle driving speed. The vehicle hazards include unexpected deceleration and unexpected deceleration capacity reduction. The injected fault type is determined based on the hazard type of the vehicle hazards, and the vehicle driving speed is determined based on the road slope.

[0163] Constructing safety evaluation indicators of the function of the electronic control braking system under longitudinal slope conditions, the safety evaluation indicators include subjective evaluation indicators and objective evaluation indicators, the subjective evaluation indicators include vehicle controllability after fault injection, instrument panel fault light display during fault injection, braking response speed after fault injection, braking direction stability after fault injection, and brake pedal feel after fault injection, and the objective evaluation indicators include braking stability, lateral error, average deceleration, and fault takeover time after fault injection;

[0164] Receive a test instruction, select each of the basic components from the safety test case library to construct this test condition, determine the subjective evaluation score and the objective evaluation score of the intelligent connected vehicle under the test condition, and obtain a comprehensive scoring result.

[0165] The present application also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a micro drive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0166] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0167] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0168] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0169] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0170] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0171] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk and other media that can store program code.

[0172] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by entering a program to instruct the relevant hardware. The program may be stored in a computer-readable memory, and the memory may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0173] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A functional safety evaluation method for an electronically controlled braking system under longitudinal slope conditions, characterized in that: The method comprises: Predict the road slope of intelligent connected vehicles during driving based on the unscented Kalman filter algorithm; Construct a safety test case library for the function of the electronic control brake system under a longitudinal slope condition, wherein the basic components of the functional safety test case library include vehicle hazards, injected fault types, fault injection time, and vehicle driving speed. The vehicle hazards include unexpected deceleration and unexpected deceleration capacity reduction. The injected fault type is determined based on the hazard type of the vehicle hazards, and the vehicle driving speed is determined based on the road slope. Constructing safety evaluation indicators of the electronic control braking system function under longitudinal slope conditions, the safety evaluation indicators include subjective evaluation indicators and objective evaluation indicators, the subjective evaluation indicators include vehicle controllability after fault injection, instrument panel fault light display during fault injection, braking response speed after fault injection, braking direction stability after fault injection, and brake pedal feel after fault injection, and the objective evaluation indicators include braking stability after fault injection, lateral error, average deceleration, and fault takeover time; Receive a test instruction, select each of the basic components from the safety test case library to construct this test condition, determine the subjective evaluation score and the objective evaluation score of the intelligent connected vehicle under the test condition, and obtain a comprehensive scoring result.

2. The method according to claim 1, characterized in that The method of estimating the road slope of the intelligent connected vehicle during vehicle driving based on the unscented Kalman filter algorithm includes: Acquiring vehicle force data of an intelligent network-connected vehicle traveling on a slope, and constructing a longitudinal dynamics model of the intelligent network-connected vehicle based on the vehicle force data; A road slope recursive model based on unscented Kalman filtering is constructed according to the longitudinal dynamics model, and the road slope during vehicle driving is determined according to the road slope recursive model.

3. The method according to claim 2, characterized in that The step of constructing a road slope recursive model based on an unscented Kalman filter according to the longitudinal dynamics model, and determining the road slope during vehicle driving according to the road slope recursive model, comprises: Determining a time update equation and a measurement update equation of the intelligent connected vehicle according to the longitudinal dynamics model; Constructing a road slope recursive model based on unscented Kalman filtering based on the time update equation and the measurement update equation; The road surface slope during vehicle driving is determined according to the longitudinal dynamics model and the road slope recursive model.

4. The method according to claim 3, characterized in that: The determining of the time update equation and the measurement update equation of the intelligent networked vehicle according to the longitudinal dynamics model includes: Determining a system state quantity of the intelligent connected vehicle according to the longitudinal dynamics model; After discretizing the system state quantity, generating a system state equation and a system measurement equation based on the system state quantity; The time update equation and the measurement update equation of the intelligent connected vehicle are determined according to the system state equation and the system measurement equation.

5. The method according to claim 1, characterized in that The injected fault type includes a parking control device failure, a throttle depth signal failure and a communication interface failure when the type of hazard to the whole vehicle is unexpected deceleration. The injected fault type includes a wheel speed sensor signal failure, a communication interface failure, a power supply failure and a motor feedback signal torque failure when the type of hazard to the whole vehicle is unexpected deceleration capability reduction.

6. The method according to claim 1, characterized in that Determining the subjective evaluation score and the objective evaluation score of the intelligent network-connected vehicle under the current test condition includes: Acquire actual driving data of the intelligent connected vehicle under the test condition, and calculate an objective evaluation score based on the actual driving data; Obtain the subjective evaluation score sent by the test terminal corresponding to the tester of the intelligent connected vehicle.

7. The method according to claim 1, characterized in that After obtaining the comprehensive scoring result, the method further includes: Record the comprehensive scoring result corresponding to the test condition.

8. A functional safety evaluation device for an electric control braking system under longitudinal slope conditions, characterized in that: The device comprises: An estimation module is used to estimate the road slope of an intelligent connected vehicle during its driving process based on an unscented Kalman filter algorithm; The first construction module is used to construct a safety test case library of the electronic control brake system function under a longitudinal slope condition, wherein the basic components of the functional safety test case library include vehicle hazards, injected fault types, fault injection time, and vehicle driving speed. The vehicle hazards include unexpected deceleration and unexpected deceleration capacity reduction. The injected fault type is determined based on the hazard type of the vehicle hazards, and the vehicle driving speed is determined based on the road slope. The second construction module is used to construct safety evaluation indicators of the electronic control braking system function under longitudinal slope conditions, wherein the safety evaluation indicators include subjective evaluation indicators and objective evaluation indicators, wherein the subjective evaluation indicators include vehicle controllability after fault injection, instrument panel fault light display during fault injection, brake response speed after fault injection, brake direction stability after fault injection, and brake pedal feel after fault injection, and the objective evaluation indicators include brake stability after fault injection, lateral error, average deceleration, and fault takeover time; The evaluation module is used to receive the test instruction, select each of the basic components from the safety test case library to construct the test condition, determine the subjective evaluation score and the objective evaluation score of the intelligent connected vehicle under the test condition, and obtain a comprehensive scoring result.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer-readable storage medium has instructions stored therein, and when the instructions are executed on a computer or a processor, the computer or the processor executes the steps of the method according to any one of claims 1 to 7.

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