Vehicle and method, apparatus, and medium for determining fault-tolerant time interval thereof

Abstract

The application discloses a vehicle and a fault tolerance time interval determination method, device and medium thereof, and relates to the technical field of vehicles. The method comprises the following steps: obtaining vehicle state information and obstacle identification results; determining a risk scene category according to the vehicle state information and the obstacle identification results; determining target behavior parameters of a driver based on a set of driver behavior parameters under the risk scene category; performing simulation analysis according to the target behavior parameters of the driver and a preset road simulation model, determining a collision occurrence time, and determining a fault tolerance time interval based on the collision occurrence time and a fault occurrence time. The fault tolerance time interval determination method can determine target behavior parameters of a driver based on a set of driver behavior parameters collected under the same vehicle type and the same risk scene category, and can determine a fault tolerance time interval by performing simulation calculation according to the target behavior parameters of the driver and a preset road simulation model, so that the accuracy of fault tolerance time interval calculation can be improved.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a method for determining a vehicle's fault tolerance time interval, a device for determining a vehicle's fault tolerance time interval, a computer-readable storage medium, and a vehicle. Background Technology

[0002] With the rapid development of the automotive industry towards electrification, intelligence, and connectivity, the complexity of automotive electronic and electrical systems is increasing, leading to higher safety risks. Functional safety is a crucial consideration in the design of automotive electronic and electrical systems. When a safety-related fault occurs, effective fault tolerance measures must be taken within a reasonable time interval to ensure the safe operation or degradation of the system and avoid or mitigate potential harm to personnel. Therefore, determining the fault tolerance time interval is of great significance for the functional safety design of the system.

[0003] In related technologies, the main method for confirming fault tolerance time intervals is the experience-based judgment method. This relies on the accumulated experience of development engineers, who make rough estimates based on their experience handling similar projects in the past. This lack of scientific analysis methods and objective data support leads to significant uncertainty, and the method may be unsuitable for complex systems or unknown operating conditions. To obtain fault tolerance time intervals more systematically and accurately, simulation calculations can be used. This involves simulating various system fault conditions, observing the system's response and fault tolerance capabilities, and then calculating the shortest time span from fault occurrence to hazard development. However, these simulation calculations require driver behavior parameters, and the accuracy and reliability of the results are affected by these parameters. Methods based on experience or statistical data often fail to obtain driver behavior parameters that accurately reflect the actual project, leading to deviations in the calculation results and affecting the design and implementation of functional safety monitoring mechanisms. Summary of the Invention

[0004] This application aims to at least partially address one of the technical problems in related technologies. To this end, the first objective of this application is to propose a method for determining the fault tolerance time interval of a vehicle. The method includes: acquiring vehicle status information and obstacle recognition results; determining a risk scenario category based on the vehicle status information and obstacle recognition results; determining the driver's target behavior parameters based on a set of driver behavior parameters under the risk scenario category; performing simulation analysis based on the driver's target behavior parameters and a preset road simulation model to determine the collision occurrence time; and determining the fault tolerance time interval based on the collision occurrence time and the fault occurrence time. The fault tolerance time interval determination method of this application determines the driver's target behavior parameters based on a set of driver behavior parameters summarized under the same vehicle model and the same risk scenario category, and performs simulation calculations based on the driver's target behavior parameters and a preset road simulation model to determine the fault tolerance time interval. This improves the accuracy of the fault tolerance time interval calculation, thereby avoiding false alarms or missed alarms in the functional safety monitoring mechanism to a certain extent.

[0005] The second objective of this application is to provide a device for determining the fault tolerance time interval of a vehicle.

[0006] The third objective of this application is to provide a computer-readable storage medium.

[0007] The fourth objective of this application is to propose a vehicle.

[0008] To achieve the above objectives, the first aspect of this application proposes a method for determining the fault tolerance time interval of a vehicle. The method includes: acquiring vehicle status information and obstacle recognition results; determining a risk scenario category based on the vehicle status information and obstacle recognition results; determining the driver's target behavior parameters based on a set of driver behavior parameters under the risk scenario category; performing simulation analysis based on the driver's target behavior parameters and a preset road simulation model to determine the collision occurrence time, and determining the fault tolerance time interval based on the collision occurrence time and the fault occurrence time.

[0009] According to one embodiment of this application, simulation analysis is performed based on the driver's target behavior parameters and a preset road simulation model to determine the collision occurrence time. This includes: obtaining a preset road simulation model corresponding to a risk scenario category; using the fault occurrence time and target behavior parameters as input to the preset road simulation model to output a simulation data time series; obtaining the vehicle position and obstacle position corresponding to each timestamp in the simulation data time series; and if the distance between the vehicle and the obstacle is less than a preset distance based on the vehicle and obstacle positions, the current timestamp is taken as the collision occurrence time. The preset distance can be determined according to actual conditions and is not specifically limited here.

[0010] According to one embodiment of this application, determining the fault tolerance time interval based on the collision occurrence time and the fault occurrence time includes: determining the fault tolerance time interval based on the difference between the collision occurrence time and the fault occurrence time.

[0011] According to one embodiment of this application, determining the target behavior parameters of a driver based on a set of driver behavior parameters under a risk scenario category includes: determining a corresponding set of driver behavior parameters based on the risk scenario category; determining the range of probability density estimation based on the minimum and maximum values ​​of the set of driver behavior parameters; determining multiple data points at preset intervals within the range of probability density estimation; determining the probability density estimate value corresponding to each data point based on a preset kernel function; fitting a probability density estimation curve based on the probability density estimate value corresponding to each data point; and determining the target behavior parameters based on the maximum point of the probability density estimation curve. The target behavior parameters include the target driver reaction time, the target braking process time, and the target deceleration. The set of driver behavior parameters includes a subset of driver reaction time, a subset of braking process time, and a subset of deceleration, and the target driver reaction time, target braking process time, and target deceleration are determined based on each subset, respectively.

[0012] According to one embodiment of this application, driver behavior parameters include driver reaction time, braking process time, and deceleration. Obtaining driver behavior parameters includes: acquiring vehicle data information of the risk scenario category, wherein the vehicle data information includes the moment of collision risk occurrence, the moment the brake pedal is depressed, and the moment the brake cylinder pressure stabilizes; determining the driver reaction time based on the difference between the moment the brake pedal is depressed and the moment the collision risk occurs; and determining the braking process time based on the difference between the moment the brake cylinder pressure stabilizes and the moment the brake pedal is depressed.

[0013] According to one embodiment of this application, determining the risk scenario category based on vehicle status information and obstacle recognition results includes: determining longitudinal collision distance, lateral collision distance, longitudinal relative speed, and lateral relative speed based on vehicle status information and obstacle recognition results; determining the longitudinal collision time interval based on the ratio of longitudinal collision distance to longitudinal relative speed; determining the lateral collision time interval based on the ratio of lateral collision distance to lateral relative speed; and determining the risk scenario category based on the longitudinal collision time interval and / or the lateral collision time interval.

[0014] According to one embodiment of this application, determining a risk scenario category based on the longitudinal collision time distance and / or the lateral collision time distance includes: determining the risk scenario category as a longitudinal level one collision risk when the longitudinal collision time distance is within a first preset range; determining the risk scenario category as a longitudinal level two collision risk when the longitudinal collision time distance is within a second preset range; wherein the upper limit of the second preset range is less than or equal to the lower limit of the first preset range; determining the risk scenario category as a lateral level one collision risk when the lateral collision time distance is within a third preset range; and determining the risk scenario category as a lateral level two collision risk when the lateral collision time distance is within a fourth preset range; wherein the upper limit of the fourth preset range is less than or equal to the lower limit of the third preset range.

[0015] To achieve the above objectives, a second aspect of this application proposes a vehicle fault tolerance time interval determination device. The device includes: a first acquisition module for acquiring vehicle status information and obstacle recognition results; a first determination module for determining a risk scenario category based on the vehicle status information and obstacle recognition results; a second determination module for determining the driver's target behavior parameters based on a set of driver behavior parameters under the risk scenario category; and a third determination module for performing simulation analysis based on the driver's target behavior parameters and a preset road simulation model to determine the collision occurrence time and determine the fault tolerance time interval based on the collision occurrence time and the fault occurrence time.

[0016] To achieve the above objectives, a third aspect of this application provides a computer-readable storage medium storing a fault tolerance time interval determination program for a vehicle, which, when executed by a processor, implements the aforementioned fault tolerance time interval determination method for a vehicle.

[0017] To achieve the above objectives, a fourth aspect of this application provides a vehicle, including a memory, a processor, and a vehicle fault tolerance time interval determination program stored in the memory and executable on the processor. When the processor executes the vehicle fault tolerance time interval determination program, it implements the aforementioned vehicle fault tolerance time interval determination method.

[0018] The vehicle and fault tolerance time interval determination method, apparatus, and medium according to embodiments of this application acquire vehicle status information and obstacle recognition results; determine risk scenario categories based on vehicle status information and obstacle recognition results; determine driver target behavior parameters based on a set of driver behavior parameters under the risk scenario category; perform simulation analysis based on the driver's target behavior parameters and a preset road simulation model to determine the collision occurrence time; and determine the fault tolerance time interval based on the collision occurrence time and the fault occurrence time. The fault tolerance time interval determination method of this application determines the driver's target behavior parameters based on a set of driver behavior parameters summarized under the same vehicle model and the same risk scenario category, and performs simulation calculations based on the driver's target behavior parameters and a preset road simulation model to determine the fault tolerance time interval. This improves the accuracy of fault tolerance time interval calculation, thereby avoiding false alarms or missed alarms in functional safety monitoring mechanisms to a certain extent. Attached Figure Description

[0019] Figure 1 This is a flowchart of a method for determining the fault tolerance time interval of a vehicle according to some embodiments of this application;

[0020] Figure 2 This is a flowchart of a method for determining a vehicle fault tolerance time interval according to other embodiments of this application;

[0021] Figure 3 A block diagram of a vehicle fault tolerance time interval determination device according to some embodiments of this application;

[0022] Figure 4 This is a block diagram of a vehicle according to some embodiments of this application. Detailed Implementation

[0023] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0024] The following describes in detail, with reference to the accompanying drawings, the vehicle and its fault tolerance time interval determination method, apparatus and medium according to embodiments of this application.

[0025] Figure 1 This is a flowchart of a method for determining a vehicle's fault tolerance time interval according to some embodiments of this application. (Refer to...) Figure 1 The method for determining the fault tolerance time interval of a vehicle according to embodiments of this application may include the following steps:

[0026] S110 acquires vehicle status information and obstacle recognition results.

[0027] Specifically, vehicle status information includes vehicle position, lateral velocity, and longitudinal velocity. The vehicle position can be obtained through onboard GPS or an inertial measurement unit, while the lateral and longitudinal velocities can be detected by speed sensors. Obstacle recognition results include obstacle position, lateral velocity, and longitudinal velocity. These can be determined based on information collected by onboard radar and cameras. It should be noted that the methods for acquiring vehicle status information and obstacle recognition results are not specifically limited here.

[0028] S120 determines the risk scenario category based on vehicle status information and obstacle recognition results.

[0029] Specifically, the lateral and longitudinal collision distances between the vehicle and the obstacle can be determined based on the vehicle's position and the obstacle's position. The lateral relative velocity can be determined based on the vehicle's lateral velocity and the obstacle's lateral velocity. The longitudinal relative velocity can be determined based on the vehicle's longitudinal velocity and the obstacle's longitudinal velocity. The lateral collision time distance and the longitudinal collision time distance can be determined based on the lateral collision distance and the lateral relative velocity. The risk scenario category can be determined based on the lateral collision time distance. For example, the risk scenario category can be determined as lateral collision risk based on the lateral collision time distance and as longitudinal collision risk based on the longitudinal collision time distance.

[0030] Furthermore, the specific collision risk level can be determined based on the magnitude of the lateral and longitudinal collision distances. For example, a larger lateral collision distance indicates a lower lateral collision risk level, while a smaller lateral collision distance indicates a higher lateral collision risk level. Similarly, a larger longitudinal collision distance indicates a lower longitudinal collision risk level, while a smaller longitudinal collision distance indicates a higher longitudinal collision risk level.

[0031] S130, determine the driver's target behavior parameters based on the set of driver behavior parameters under the risk scenario category.

[0032] Specifically, after determining the risk scenario category, a set of driver behavior parameters for that risk scenario category is determined. This set of driver behavior parameters for a risk scenario category is obtained by aggregating driver behavior parameters from drivers of the same vehicle type and within the same risk scenario category. The set of driver behavior parameters specifically includes a subset of driver reaction time, a subset of braking process time, and a subset of deceleration. The target driver reaction time, target braking process time, and target deceleration are determined based on these subsets, respectively.

[0033] S140 performs simulation analysis based on the driver's target behavior parameters and the preset road simulation model to determine the collision time and the fault tolerance time interval based on the collision time and the fault time.

[0034] Specifically, the preset road simulation model is modified according to the target behavior parameters, and then the simulation is run to determine the collision time. Finally, the fault tolerance time interval is determined based on the collision time and the fault time. For example, the fault tolerance time interval is determined by obtaining the difference between the collision time and the fault time.

[0035] It should be noted that after determining the target behavior parameters, namely the target driver's reaction time, the target braking process time, and the target deceleration, in addition to performing simulation calculations based on the driver's target behavior parameters and the preset road simulation model to determine the fault tolerance time interval, the target behavior parameters can also be input into the fault tolerance time interval calculation formula to determine the fault tolerance time interval. The fault tolerance time interval calculation formula is as follows:

[0036]

[0037] Where FTTI represents the fault tolerance time interval, detV represents the relative speed between the vehicle and the obstacle, S represents the distance between the vehicle and the obstacle, and a dest T represents the target deceleration. react T represents the target driver's reaction time. act Indicates the time of the target braking process.

[0038] The fault tolerance time interval determination method of this application determines the driver's target behavior parameters based on a set of driver behavior parameters summarized under the same vehicle model and the same risk scenario category. The fault tolerance time interval is determined by simulation calculation based on the driver's target behavior parameters and a preset road simulation model. This can improve the accuracy of fault tolerance time interval calculation and thus avoid false alarms or omissions in the functional safety monitoring mechanism to a certain extent.

[0039] In some embodiments, simulation analysis is performed based on the driver's target behavior parameters and a preset road simulation model to determine the collision time, including: obtaining a preset road simulation model corresponding to the risk scenario category; using the fault occurrence time and target behavior parameters as input to the preset road simulation model to output a simulation data time series; obtaining the vehicle position and obstacle position corresponding to each timestamp in the simulation data time series; and if the distance between the vehicle and the obstacle is determined to be less than a preset distance based on the vehicle position and obstacle position, the current timestamp is taken as the collision occurrence time.

[0040] Specifically, by collecting vehicle status information, obstacle recognition results, and road information (including but not limited to road type and road surface adhesion coefficient), a preset road simulation model corresponding to the current risk scenario category is built based on the vehicle status information, obstacle recognition results, and road information. For example, a road simulation model is pre-built, which includes a vehicle model, an obstacle model, and a road model. The vehicle status information, obstacle recognition results, and road information collected by the vehicle are input into the vehicle model, obstacle model, and road model, respectively, to build the preset road simulation model corresponding to the current risk scenario category.

[0041] Furthermore, the fault occurrence time and target behavior parameters are input into a preset road simulation model for simulation calculation, outputting a simulation data time series. Each timestamp in the simulation data time series is traversed to obtain the vehicle position and obstacle position corresponding to each timestamp. The distance between the vehicle and obstacle is determined based on the vehicle and obstacle positions, and whether a collision occurs is determined based on this distance. The timestamp corresponding to the collision occurrence is taken as the collision occurrence time.

[0042] For example, suppose the vehicle's position is (x1, y1), the obstacle's position is (x2, y2), and the distance between the vehicle and the obstacle is... If the distance is less than the preset distance, it is determined that a collision has occurred between the vehicle and the obstacle, and the current timestamp is the time when the collision occurred.

[0043] In some embodiments, determining the fault tolerance time interval based on the collision occurrence time and the fault occurrence time includes: determining the fault tolerance time interval based on the difference between the collision occurrence time and the fault occurrence time.

[0044] Specifically, the difference between the time of the collision and the time of the fault is calculated, and this difference is the fault tolerance time interval.

[0045] In some embodiments, determining the driver's target behavior parameters based on a set of driver behavior parameters under a risk scenario category includes: determining a corresponding set of driver behavior parameters based on the risk scenario category; determining the range of probability density estimation based on the minimum and maximum values ​​of the driver behavior parameter set; determining multiple data points at preset intervals within the range of probability density estimation; determining the probability density estimate value corresponding to each data point based on a preset kernel function; fitting a probability density estimation curve based on the probability density estimate value corresponding to each data point; and determining the target behavior parameters based on the maximum points of the probability density estimation curve. The target behavior parameters include the target driver reaction time, the target braking process time, and the target deceleration. The set of driver behavior parameters includes a subset of driver reaction time, a subset of braking process time, and a subset of deceleration, and the target driver reaction time, target braking process time, and target deceleration are determined based on each subset, respectively. The preset interval can be in milliseconds and can be determined according to actual conditions; no specific limitation is imposed here.

[0046] Specifically, statistical analysis was performed on the driver reaction time subset, the braking process time subset, and the deceleration subset, and the target driver reaction time, target braking process time, and target deceleration were determined by kernel density estimation.

[0047] In the following description, the example of determining the target driver's reaction time using kernel density estimation on a subset of driver reaction times is used, but this is not intended to limit the scope of this application.

[0048] For example, suppose the driver reaction time subset is {1, 2, 3}, where the minimum value is 1 and the maximum value is 3. Then, the probability density estimate is determined to be within the range of 1-3, and multiple data points are determined at preset intervals (e.g., 0.1). For example, data points may include 1, 1.1, 1.2, ..., 2.9, 3. The probability density estimate for each data point is determined based on a preset kernel function (e.g., a Gaussian kernel function), where the expression for the probability density estimate for each data point is as follows:

[0049]

[0050] in, Let x represent the probability density estimate for each data point. i Let represent the sample value in the i-th driver reaction time subset, n represent the number of samples in the driver reaction time subset, and h represent the bandwidth.

[0051] Assuming the bandwidth h is 0.5, the formula for calculating the probability density estimate corresponding to data point 1.1 is as follows:

[0052]

[0053] By analogy, the probability density estimate corresponding to each data point is determined, and the probability density estimate corresponding to each data point is fitted to determine the probability density estimate curve. Then, the derivative of the probability density estimate curve is calculated to obtain the maximum point of the probability density estimate curve. The data point corresponding to the maximum point is the target driver's reaction time.

[0054] In some embodiments, driver behavior parameters include driver reaction time, braking process time, and deceleration. Acquiring driver behavior parameters includes: acquiring vehicle data information for risk scenario categories, wherein the vehicle data information includes the moment of collision risk occurrence, the moment the brake pedal is depressed, and the moment the brake cylinder pressure stabilizes; determining the driver reaction time based on the difference between the moment the brake pedal is depressed and the moment the collision risk occurs; and determining the braking process time based on the difference between the moment the brake cylinder pressure stabilizes and the moment the brake pedal is depressed.

[0055] For example, suppose the collision risk occurs at time T1, the brake pedal is depressed at time T2, the brake cylinder pressure stabilizes at time T3, and the deceleration after stabilization is denoted as 'a'. The collision risk occurrence time T1 is the moment when the Time To Collision (TTC) threshold is met. For instance, the distance between the vehicle and the obstacle is detected by onboard radar or ultrasonic sensors, and the vehicle speed is obtained by a speed sensor. Assuming the obstacle is stationary, the ratio of the distance between the vehicle and the obstacle to the vehicle speed is calculated as the TTC. The timestamp when the TTC falls below the TTC threshold is taken as the collision risk occurrence time T1. The brake pedal is depressed at time T2, which is the timestamp when the brake pedal displacement sensor detects a displacement exceeding a certain threshold (e.g., 0.1 mm). The brake cylinder pressure is obtained by a brake cylinder pressure sensor. The pressure change rate is calculated based on the detected brake cylinder pressure data, and the timestamp when the pressure change rate falls below a certain threshold (e.g., 0.05 bar / s) is taken as the brake cylinder pressure stabilization time T3. The stabilization deceleration 'a' can be determined based on the vehicle speed detected by the vehicle speed sensor and accelerometer, and the vehicle's longitudinal acceleration.

[0056] Furthermore, the driver's reaction time is determined based on the difference between the moment the brake pedal is depressed and the moment the collision risk occurs, i.e., driver's reaction time = moment the brake pedal is depressed T2 - moment the collision risk occurs T1. The braking process time is determined based on the difference between the moment the brake cylinder pressure stabilizes and the moment the brake pedal is depressed, i.e., braking process time = moment the brake cylinder pressure stabilizes T3 - moment the brake pedal is depressed T2.

[0057] In some embodiments, determining the risk scenario category based on vehicle status information and obstacle recognition results includes: determining longitudinal collision distance, lateral collision distance, longitudinal relative speed, and lateral relative speed based on vehicle status information and obstacle recognition results; determining longitudinal collision time interval based on the ratio of longitudinal collision distance to longitudinal relative speed; determining lateral collision time interval based on the ratio of lateral collision distance to lateral relative speed; and determining the risk scenario category based on the longitudinal collision time interval and / or lateral collision time interval.

[0058] In some embodiments, determining the risk scenario category based on the longitudinal collision time distance and / or the lateral collision time distance includes: determining the risk scenario category as longitudinal level one collision risk when the longitudinal collision time distance is within a first preset range; determining the risk scenario category as longitudinal level two collision risk when the longitudinal collision time distance is within a second preset range; wherein the upper limit of the second preset range is less than or equal to the lower limit of the first preset range; determining the risk scenario category as lateral level one collision risk when the lateral collision time distance is within a third preset range; and determining the risk scenario category as lateral level two collision risk when the lateral collision time distance is within a fourth preset range; wherein the upper limit of the fourth preset range is less than or equal to the lower limit of the third preset range.

[0059] Specifically, vehicle status information includes vehicle position, lateral velocity, and longitudinal velocity. Obstacle recognition results include obstacle position, lateral velocity, and longitudinal velocity. Based on the vehicle and obstacle positions, the longitudinal and lateral collision distances can be determined. For example, assuming the vehicle position is (x3, y3) and the obstacle position is (x4, y4), where x represents the longitudinal direction and y represents the lateral direction, then the longitudinal collision distance is the absolute value of the difference between x3 and x4, and the lateral collision distance is the absolute value of the difference between y3 and y4. The longitudinal relative velocity is the absolute value of the difference between the vehicle's longitudinal velocity and the obstacle's longitudinal velocity, and the lateral relative velocity is the absolute value of the difference between the vehicle's lateral velocity and the obstacle's lateral velocity. Then, the longitudinal collision time distance is determined based on the ratio of the longitudinal collision distance to the longitudinal relative velocity, and the lateral collision time distance is determined based on the ratio of the lateral collision distance to the lateral relative velocity. The specific collision risk level can be determined based on the magnitude of the lateral and longitudinal collision time distances.

[0060] For example, when the longitudinal collision time is within a first preset range, such as when the longitudinal collision time is greater than 1.5s and less than 2s, the risk scenario category is determined to be longitudinal level 1 collision risk; when the longitudinal collision time is within a second preset range, such as when the longitudinal collision time is greater than 1s and less than 1.5s, the risk scenario category is determined to be longitudinal level 2 collision risk; when the lateral collision time is within a third preset range, such as when the lateral collision time is greater than 1.5s and less than 2s, the risk scenario category is determined to be lateral level 1 collision risk; when the lateral collision time is within a fourth preset range, such as when the lateral collision time is greater than 1s and less than 1.5s, the risk scenario category is determined to be lateral level 2 collision risk.

[0061] It should be noted that the specific collision risk levels are not limited to Level 1 and Level 2.

[0062] As a concrete example, refer to Figure 2 The method for determining the fault tolerance time interval of a vehicle according to embodiments of this application may include the following steps:

[0063] S201, the vehicle controller at the vehicle end sends data including vehicle status information, obstacle recognition results, etc. to the vehicle communication terminal in real time.

[0064] S202, the vehicle-mounted communication terminal forwards the received data to the cloud server.

[0065] S203, the cloud server processes and statistically analyzes the received data to obtain the driver's target behavior parameters under the risk scenario category.

[0066] S204, the simulation equipment performs simulation analysis based on the driver's target behavior parameters output by the cloud server and a preset road simulation model to obtain a simulation data time series. Based on the simulation data time series, the collision occurrence time is determined, and the fault tolerance time interval is determined based on the collision occurrence time and the fault occurrence time.

[0067] In summary, the fault tolerance time interval determination method of this application determines the driver's target behavior parameters based on a set of driver behavior parameters summarized under the same vehicle model and the same risk scenario category, which is more in line with the actual situation of the project. The fault tolerance time interval obtained by simulation calculation based on the driver's target behavior parameters and the preset road simulation model is more reasonable and accurate, and the safety mechanism designed accordingly is more reliable, which can better ensure the correct operation of the functional safety monitoring scheme in the actual vehicle.

[0068] Corresponding to the above embodiments, this application also proposes a vehicle fault tolerance time interval determination device.

[0069] Reference Figure 3The vehicle fault tolerance time interval determination device 300 includes: a first acquisition module 310, a first determination module 320, a second determination module 330 and a third determination module 340.

[0070] The first acquisition module 310 is used to acquire vehicle status information and obstacle recognition results. The first determination module 320 is used to determine the risk scenario category based on the vehicle status information and obstacle recognition results. The second determination module 330 determines the driver's target behavior parameters based on the set of driver behavior parameters under the risk scenario category. The third determination module 340 is used to perform simulation analysis based on the driver's target behavior parameters and a preset road simulation model to determine the collision occurrence time, and to determine the fault tolerance time interval based on the collision occurrence time and the fault occurrence time.

[0071] According to one embodiment of this application, the third determining module 340 is specifically used to: obtain a preset road simulation model corresponding to the risk scenario category; take the fault occurrence time and target behavior parameters as inputs to the preset road simulation model to output a simulation data time series; obtain the vehicle position and obstacle position corresponding to each timestamp in the simulation data time series; and take the current timestamp as the collision occurrence time when the distance between the vehicle and the obstacle is determined to be less than a preset distance based on the vehicle position and obstacle position.

[0072] According to one embodiment of this application, the third determining module 340 is further configured to determine a fault tolerance time interval based on the difference between the collision occurrence time and the fault occurrence time.

[0073] According to one embodiment of this application, the second determining module 330 is specifically used to: determine a corresponding set of driver behavior parameters based on the risk scenario category; determine the range of probability density estimation based on the minimum and maximum values ​​of the set of driver behavior parameters; determine multiple data points at preset intervals within the range of probability density estimation; determine the probability density estimate value corresponding to each data point based on a preset kernel function; fit a probability density estimation curve based on the probability density estimate value corresponding to each data point; and determine target behavior parameters based on the maximum point of the probability density estimation curve. The target behavior parameters include the target driver reaction time, the target braking process time, and the target deceleration. The set of driver behavior parameters includes a subset of driver reaction time, a subset of braking process time, and a subset of deceleration. The target driver reaction time, the target braking process time, and the target deceleration are determined based on each subset, respectively.

[0074] According to one embodiment of this application, driver behavior parameters include driver reaction time, braking process time, and deceleration. Vehicle data information of risk scenario categories is obtained, wherein the vehicle data information includes the moment of collision risk occurrence, the moment the brake pedal is depressed, and the moment the brake cylinder pressure stabilizes. The driver reaction time is determined based on the difference between the moment the brake pedal is depressed and the moment the collision risk occurs. The braking process time is determined based on the difference between the moment the brake cylinder pressure stabilizes and the moment the brake pedal is depressed.

[0075] According to one embodiment of this application, the first determining module 320 is specifically used to: determine the longitudinal collision distance, the lateral collision distance, the longitudinal relative speed, and the lateral relative speed based on vehicle status information and obstacle recognition results; determine the longitudinal collision time interval based on the ratio of the longitudinal collision distance to the longitudinal relative speed; determine the lateral collision time interval based on the ratio of the lateral collision distance to the lateral relative speed; and determine the risk scenario category based on the longitudinal collision time interval and / or the lateral collision time interval.

[0076] According to one embodiment of this application, the first determining module 320 is further configured to: determine the risk scenario category as longitudinal level one collision risk when the longitudinal collision time distance is within a first preset range; determine the risk scenario category as longitudinal level two collision risk when the longitudinal collision time distance is within a second preset range; wherein the upper limit of the second preset range is less than or equal to the lower limit of the first preset range; determine the risk scenario category as lateral level one collision risk when the lateral collision time distance is within a third preset range; and determine the risk scenario category as lateral level two collision risk when the lateral collision time distance is within a fourth preset range; wherein the upper limit of the fourth preset range is less than or equal to the lower limit of the third preset range.

[0077] It should be noted that the above explanation of the embodiments and beneficial effects of the method for determining the fault tolerance time interval of a vehicle also applies to the vehicle fault tolerance time interval determination device in the embodiments of this application. To avoid redundancy, it will not be elaborated in detail here.

[0078] Corresponding to the above embodiments, this application also proposes a computer-readable storage medium.

[0079] The computer-readable storage medium of this application stores a fault tolerance time interval determination program for a vehicle, which, when executed by a processor, implements the aforementioned fault tolerance time interval determination method for a vehicle.

[0080] It should be noted that the above-described embodiments and explanations of the beneficial effects of the method for determining the fault tolerance time interval of a vehicle are also applicable to the computer-readable storage medium of the embodiments of this application. To avoid redundancy, they will not be elaborated in detail here.

[0081] Corresponding to the above embodiments, this application also proposes a vehicle.

[0082] See Figure 4 As shown, the vehicle 400 of this application includes a memory 410, a processor 420, and a vehicle fault tolerance time interval determination program stored in the memory 410 and executable on the processor 420. When the processor executes the vehicle fault tolerance time interval determination program, it implements the aforementioned vehicle fault tolerance time interval determination method.

[0083] It should be noted that the above-described embodiments and explanations of the beneficial effects of the method for determining the fault tolerance time interval of a vehicle are also applicable to the vehicles in the embodiments of this application. To avoid redundancy, they will not be elaborated in detail here.

[0084] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0085] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0086] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0087] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0088] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "joining," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise expressly limited. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0089] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method of fault-tolerant time interval determination for a vehicle, characterized by, The method includes: Obtain vehicle status information and obstacle recognition results; The risk scenario category is determined based on the vehicle status information and the obstacle recognition result; The driver's target behavior parameters are determined based on the set of driver behavior parameters under the aforementioned risk scenario categories; Simulation analysis is performed based on the driver's target behavior parameters and a preset road simulation model to determine the collision time, and a fault tolerance time interval is determined based on the collision time and the fault time. The driver's target behavior parameters are determined based on the set of driver behavior parameters under the aforementioned risk scenario category, including: Determine the corresponding set of driver behavior parameters based on the aforementioned risk scenario categories; The range of probability density estimation is determined based on the minimum and maximum values ​​of the driver behavior parameter set; Within the range of the probability density estimation, multiple data points are determined at preset intervals; The probability density estimate for each data point is determined based on a preset kernel function. A probability density estimation curve is fitted based on the probability density estimate corresponding to each data point; The target behavior parameters are determined based on the maximum point of the probability density estimation curve, and the target behavior parameters include the target driver's reaction time, the target braking process time, and the target deceleration. The set of driver behavior parameters includes a subset of driver reaction time, a subset of braking process time, and a subset of deceleration. The target driver reaction time, the target braking process time, and the target deceleration are determined based on each subset.

2. The method of fault-tolerant time interval determination for a vehicle according to claim 1, characterized in that, Based on the driver's target behavior parameters and a preset road simulation model, simulation analysis is performed to determine the moment of collision, including: Obtain the preset road simulation model corresponding to the risk scenario category; The time of the fault occurrence and the target behavior parameters are used as inputs to the preset road simulation model to output a simulation data time series. Obtain the vehicle position and obstacle position corresponding to each timestamp in the simulation data time series; If the distance between the vehicle and the obstacle is determined to be less than a preset distance based on the vehicle's position and the obstacle's position, the current timestamp is used as the time when the collision occurred.

3. The method of fault-tolerant time interval determination for a vehicle according to claim 2, characterized in that, Determining the fault tolerance time interval based on the collision occurrence time and the fault occurrence time includes: The fault tolerance time interval is determined based on the difference between the time of the collision and the time of the fault.

4. The vehicle fault-tolerant time interval determination method according to claim 1, characterized by, The driver behavior parameters include driver reaction time, braking process time, and deceleration. Acquiring these driver behavior parameters includes: Obtain vehicle data information for the aforementioned risk scenario category, wherein the vehicle data information includes the time of collision risk occurrence, the time of brake pedal depressing, and the time of brake cylinder pressure stabilization; The driver's reaction time is determined based on the difference between the moment the brake pedal is depressed and the moment the collision risk occurs; The braking process time is determined based on the difference between the moment when the brake cylinder pressure stabilizes and the moment when the brake pedal is depressed.

5. The method of fault-tolerant time interval determination for a vehicle of claim 1, wherein, Based on the vehicle status information and the obstacle recognition results, the risk scenario category is determined, including: Based on the vehicle status information and the obstacle recognition results, determine the longitudinal collision distance, lateral collision distance, longitudinal relative speed, and lateral relative speed; The longitudinal collision time interval is determined based on the ratio of the longitudinal collision distance to the longitudinal relative velocity; The lateral collision time interval is determined based on the ratio of the lateral collision distance to the lateral relative velocity; The risk scenario category is determined based on the longitudinal collision time distance and / or the lateral collision time distance.

6. The method of fault-tolerant time interval determination for a vehicle of claim 5, wherein, The risk scenario category is determined based on the longitudinal collision time distance and / or the lateral collision time distance, including: If the longitudinal collision time distance is within a first preset range, the risk scenario category is determined to be a longitudinal level one collision risk; If the longitudinal collision time distance is within a second preset range, the risk scenario category is determined to be a longitudinal level 2 collision risk; wherein, the upper limit of the second preset range is less than or equal to the lower limit of the first preset range; If the lateral collision time distance is within a third preset range, the risk scenario category is determined to be a level one lateral collision risk. If the lateral collision time distance is within a fourth preset range, the risk scenario category is determined to be a lateral level 2 collision risk; wherein the upper limit of the fourth preset range is less than or equal to the lower limit of the third preset range.

7. A fault-tolerant time interval determination apparatus for a vehicle, characterized by The device includes: The first acquisition module is used to acquire vehicle status information and obstacle recognition results; The first determining module is used to determine the risk scenario category based on the vehicle status information and the obstacle recognition result; The second determining module determines the driver's target behavior parameters based on the set of driver behavior parameters under the risk scenario category. This determination includes: determining a corresponding set of driver behavior parameters based on the risk scenario category; determining a range of probability density estimation based on the minimum and maximum values ​​of the set of driver behavior parameters; determining multiple data points at preset intervals within the range of probability density estimation; determining a probability density estimate value corresponding to each data point based on a preset kernel function; fitting a probability density estimation curve based on the probability density estimate value corresponding to each data point; and determining the target behavior parameters based on the maximum point of the probability density estimation curve. The target behavior parameters include the target driver's reaction time, the target braking process time, and the target deceleration. The set of driver behavior parameters includes a subset of driver reaction time, a subset of braking process time, and a subset of deceleration, and the target driver's reaction time, the target braking process time, and the target deceleration are determined based on each subset, respectively. The third determining module is used to perform simulation analysis based on the driver's target behavior parameters and a preset road simulation model to determine the collision time and to determine the fault tolerance time interval based on the collision time and the fault time.

8. A computer-readable storage medium, characterized in that, It stores a fault tolerance time interval determination program for a vehicle, which, when executed by a processor, implements the fault tolerance time interval determination method for a vehicle according to any one of claims 1-6.

9. A vehicle characterized by comprising: The system includes a memory, a processor, and a vehicle fault tolerance time interval determination program stored in the memory and executable on the processor. When the processor executes the vehicle fault tolerance time interval determination program, it implements the vehicle fault tolerance time interval determination method according to any one of claims 1-6.

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