Vehicle, method and device for determining fault tolerance time interval of vehicle and medium

Abstract

The invention discloses a vehicle, a method and device for determining a fault tolerance time interval of the vehicle and a medium, and relates to the technical field of vehicles. The method comprises the following steps: acquiring vehicle state information and an obstacle recognition result; determining a risk scene category according to the vehicle state information and the obstacle recognition result; determining a target behavior parameter of the driver based on the driver behavior parameter set under the risk scene category; and performing simulation analysis according to the target behavior parameters of the driver and a preset road simulation model, determining a collision occurrence moment, and determining a fault tolerance time interval based on the collision occurrence moment and the fault occurrence moment. According to the fault-tolerant time interval determination method, the target behavior parameter of the driver is determined based on the driver behavior parameter set summarized under the same vehicle type and the same risk scene category, and the fault-tolerant time interval is determined by performing simulation calculation according to the target behavior parameter of the driver and the preset road simulation model. The accuracy of fault tolerance time interval calculation can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of vehicles, and particularly to a method for determining a fault tolerance time interval of a vehicle, a device for determining a fault tolerance time interval of a vehicle, a computer-readable storage medium, and a vehicle. Background Art

[0002] With the rapid development of the automotive industry towards electrification, intelligence, and networking, the complexity of automotive electronic and electrical systems is increasing day by day, and the resulting safety risks are also getting higher and higher. Functional safety is a crucial consideration factor in the design of automotive electronic and electrical systems. When a safety-related fault occurs in the system, it is necessary to take effective treatment measures in a reasonable time interval in a timely manner for fault tolerance to ensure the safe operation or degradation of the system and avoid or reduce possible harm to personnel. Therefore, the confirmation of the fault tolerance time interval is of great significance for the functional safety design of the system.

[0003] In the related art, the method for confirming the fault tolerance time interval is mainly the empirical judgment method, that is, relying on the experience accumulation of development engineers. They roughly estimate the fault tolerance time interval based on the processing experience of previous similar projects, lacking scientific analysis methods and objective data support, resulting in greater uncertainty. And when facing complex systems or unknown working conditions, this method may not be applicable. In order to obtain the fault tolerance time interval more systematically and accurately, the simulation calculation method can also be used, that is, by simulating various system fault conditions, observing the response and fault tolerance ability of the system, and then calculating the shortest time span from the occurrence of the fault to the occurrence of the hazard. However, in the related simulation calculation methods, it is necessary to simulate and calculate the fault tolerance time interval according to the driver behavior parameters, and the accuracy and reliability of the calculation results will be affected by the driver behavior parameters. However, it is often difficult to obtain driver behavior parameters that conform to the actual situation of the project based on empirical estimation or statistical data, resulting in deviations in the calculation results and affecting the design and implementation of the functional safety monitoring mechanism. Summary of the Invention

[0004] This application aims to solve at least one of the technical problems in the related art to some extent. To this end, the first object of this application is to propose a method for determining the fault tolerance time interval of a vehicle. The method includes: obtaining vehicle status information and obstacle recognition results; determining the risk scenario category according to the vehicle status information and obstacle recognition results; determining the target behavior parameters of the driver based on the set of driver behavior parameters under the risk scenario category; performing simulation analysis according to the target behavior parameters of the driver and the 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 method for determining the fault tolerance time interval of this application determines the target behavior parameters of the driver based on the set of driver behavior parameters summarized under the same vehicle model and the same risk scenario category, and performs simulation calculations according to the target behavior parameters of the driver and the preset road simulation model to determine the fault tolerance time interval. In this way, the accuracy of the fault tolerance time interval calculation can be improved, and thus false alarms or missed alarms of the functional safety monitoring mechanism can be avoided to some extent.

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

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

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

[0008] To achieve the above object, an embodiment of the first aspect of this application proposes a method for determining the fault tolerance time interval of a vehicle. The method includes: obtaining vehicle status information and obstacle recognition results; determining the risk scenario category according to the vehicle status information and obstacle recognition results; determining the target behavior parameters of the driver based on the set of driver behavior parameters under the risk scenario category; performing simulation analysis according to the target behavior parameters of the driver and the 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 an embodiment of this application, performing simulation analysis according to the target behavior parameters of the driver and the preset road simulation model to determine the collision occurrence time includes: obtaining the preset road simulation model corresponding to the risk scenario category; using the fault occurrence time and the target behavior parameters as the input of the preset road simulation model to output a time series of simulation data; obtaining the vehicle position and obstacle position corresponding to each timestamp in the time series of simulation data; in the case where the distance between the vehicle and the obstacle is determined to be less than the preset distance according to the vehicle position and the obstacle position, taking the current timestamp as the collision occurrence time. The preset distance can be determined according to the actual situation and is not specifically limited here.

[0010] According to an embodiment of the present application, determining a 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 an embodiment of the present application, determining the target behavior parameters of a driver based on a set of driver behavior parameters under a risk scenario category includes: determining the corresponding set of driver behavior parameters based on the risk scenario category; determining the range of probability density estimation according to the minimum and maximum values of the set of driver behavior parameters; determining a plurality of data points at a preset interval within the range of probability density estimation; determining the probability density estimation value corresponding to each data point based on a preset kernel function; fitting a probability density estimation curve according to the probability density estimation value corresponding to each data point; determining the target behavior parameters based on the maximum value point of the probability density estimation curve, and the target behavior parameters include the target driver reaction time, the target braking process time, and the target deceleration; wherein, the set of driver behavior parameters includes a subset of driver reaction times, a subset of braking process times, and a subset of decelerations, and the target driver reaction time, the target braking process time, and the target deceleration are determined according to each subset respectively.

[0012] According to an embodiment of the present application, the driver behavior parameters include the driver reaction time, the braking process time, and the deceleration. Among them, obtaining the driver behavior parameters includes: obtaining the vehicle data information of the risk scenario category, wherein the vehicle data information includes the collision risk occurrence time, the brake pedal depression time, and the brake cylinder pressure stabilization time; determining the driver reaction time based on the difference between the brake pedal depression time and the collision risk occurrence time; determining the braking process time based on the difference between the brake cylinder pressure stabilization time and the brake pedal depression time.

[0013] According to an embodiment of the present application, determining the risk scenario category according to the vehicle state information and the obstacle recognition result includes: determining the longitudinal collision distance, the lateral collision distance, the longitudinal relative speed, and the lateral relative speed according to the vehicle state information and the obstacle recognition result; determining the longitudinal collision time based on the ratio of the longitudinal collision distance to the longitudinal relative speed; determining the lateral collision time based on the ratio of the lateral collision distance to the lateral relative speed; determining the risk scenario category according to the longitudinal collision time and / or the lateral collision time.

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

[0015] To achieve the above object, an embodiment of the second aspect of the present application provides a device for determining the fault tolerance time interval of a vehicle. The device includes: a first acquisition module, configured to acquire vehicle state information and an obstacle recognition result; a first determination module, configured to determine a risk scenario category based on the vehicle state information and the obstacle recognition result; a second determination module, configured to determine the target behavior parameter of the driver based on the set of driver behavior parameters under the risk scenario category; a third determination module, configured to perform simulation analysis according to the target behavior parameter of the driver 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 object, an embodiment of the third aspect of the present application provides a computer-readable storage medium, on which a program for determining the fault tolerance time interval of a vehicle is stored. When the program for determining the fault tolerance time interval of the vehicle is executed by a processor, the foregoing method for determining the fault tolerance time interval of the vehicle is implemented.

[0017] To achieve the above object, an embodiment of the fourth aspect of the present application provides a vehicle, including a memory, a processor, and a program for determining the fault tolerance time interval of the vehicle stored on the memory and executable on the processor. When the processor executes the program for determining the fault tolerance time interval of the vehicle, the foregoing method for determining the fault tolerance time interval of the vehicle is implemented.

[0018] A vehicle, a method, a device, and a medium for determining a fault tolerance time interval according to an embodiment of the present application. Obtain vehicle state information and an obstacle recognition result; determine a risk scenario category according to the vehicle state information and the obstacle recognition result; determine a target behavior parameter of a driver based on a set of driver behavior parameters under the risk scenario category; perform simulation analysis according to the target behavior parameter of the driver and a preset road simulation model to determine a collision occurrence time, and determine a fault tolerance time interval based on the collision occurrence time and a fault occurrence time. The method for determining a fault tolerance time interval of the present application determines a target behavior parameter of a driver based on a set of driver behavior parameters summarized under the same vehicle model and the same risk scenario category, and performs simulation calculation according to the target behavior parameter of the driver and a preset road simulation model to determine a fault tolerance time interval, so as to improve the accuracy of calculating the fault tolerance time interval, and further avoid false alarms or missed alarms of a functional safety monitoring mechanism to a certain extent. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0021] Figure 3 is a block diagram of a device for determining a fault tolerance time interval of a vehicle according to some embodiments of the present application;

[0022] Figure 4 is a block diagram of a vehicle according to some embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] Embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the drawings, where the same or similar reference numerals indicate the same or similar elements or elements with the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present application, but should not be construed as limiting the present application.

[0024] The vehicle, the method, the device, and the medium for determining a fault tolerance time interval of the embodiments of the present application will be described in detail below with reference to the drawings.

[0025] Figure 1 is a flowchart of a method for determining a fault tolerance time interval of a vehicle according to some embodiments of the present application. Referring to Figure 1 , the method for determining a fault tolerance time interval of a vehicle according to an embodiment of the present application may include the following steps:

[0026] S110, obtain vehicle state information and an obstacle recognition result.

[0027] Specifically, the vehicle state information includes the vehicle position, the vehicle lateral speed, and the vehicle longitudinal speed. Among them, the position of the vehicle can be obtained through an on-vehicle GPS or an inertial measurement unit, and the vehicle lateral speed and the vehicle longitudinal speed can be detected and obtained through speed sensors. The obstacle recognition result includes the obstacle position, the obstacle lateral speed, and the obstacle longitudinal speed. Among them, the obstacle position, the obstacle lateral speed, and the obstacle longitudinal speed can be determined according to the information collected by the on-vehicle radar and camera. It should be noted that the acquisition methods of the vehicle state information and the obstacle recognition result are not specifically limited here.

[0028] S120. Determine the risk scenario category according to the vehicle state information and the obstacle recognition result.

[0029] Specifically, according to the vehicle position and the obstacle position, the lateral collision distance and the longitudinal collision distance between the vehicle and the obstacle can be determined. According to the vehicle lateral speed and the obstacle lateral speed, the lateral relative speed can be determined. According to the vehicle longitudinal speed and the obstacle longitudinal speed, the longitudinal relative speed can be determined. According to the lateral collision distance and the lateral relative speed between the vehicle and the obstacle, the lateral collision time can be determined. According to the longitudinal collision distance and the longitudinal relative speed between the vehicle and the obstacle, the longitudinal collision time can be determined. According to the lateral collision time and the longitudinal collision time, the risk scenario category can be determined. For example, according to the lateral collision time, the risk scenario category is determined as the lateral collision risk, and according to the longitudinal collision time, the risk scenario category is determined as the longitudinal collision risk.

[0030] Furthermore, the specific collision risk level can be determined according to the magnitudes of the lateral collision time and the longitudinal collision time. For example, if the lateral collision time is relatively large, it is determined that the lateral collision risk level is relatively low. If the lateral collision time is relatively small, it is determined that the lateral collision risk level is relatively high. If the longitudinal collision time is relatively large, it is determined that the longitudinal collision risk level is relatively low. If the longitudinal collision time is relatively small, it is determined that the longitudinal collision risk level is relatively high.

[0031] S130. Determine the target behavior parameters of the driver based on the set of driver behavior parameters under the risk scenario category.

[0032] Specifically, after determining the risk scenario category, determine the set of driver behavior parameters under this risk scenario category. Among them, the set of driver behavior parameters under the risk scenario category is obtained by summarizing the driver behavior parameters of the same vehicle model and 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, the target braking process time, and the target deceleration are determined based on the subset of driver reaction time, the subset of braking process time, and the subset of deceleration, respectively.

[0033] S140. Perform simulation analysis based on the driver's target behavior parameters and the 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.

[0034] Specifically, modify the preset road simulation model according to the target behavior parameters, then perform simulation runs to determine the collision occurrence time, and finally determine the fault tolerance time interval based on the collision occurrence time and the fault occurrence time. For example, obtain the difference between the collision occurrence time and the fault occurrence time to determine the fault tolerance time interval.

[0035] It should be noted that after determining the target behavior parameters, that is, after determining the target driver 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] Among them, 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, a dest represents the target deceleration, T react represents the target driver reaction time, T act represents the target braking process time.

[0038] The method for determining the fault tolerance time interval of this application determines the driver's target behavior parameters based on the set of driver behavior parameters aggregated under the same vehicle model and the same risk scenario category, and performs simulation calculations based on the driver's target behavior parameters and the preset road simulation model to determine the fault tolerance time interval. In this way, the accuracy of the fault tolerance time interval calculation can be improved, and thus false alarms or missed alarms of the functional safety monitoring mechanism can be avoided to a certain extent.

[0039] In some embodiments, performing simulation analysis based on the driver's target behavior parameters and the preset road simulation model to determine the collision occurrence time includes: obtaining the preset road simulation model corresponding to the risk scenario category; using the fault occurrence time and the target behavior parameters as the input of the preset road simulation model to output a time series of simulation data; obtaining the vehicle position and the obstacle position corresponding to each time stamp in the time series of simulation data; and in the case where it is determined that the distance between the vehicle and the obstacle is less than the preset distance according to the vehicle position and the obstacle position, taking the current time stamp as the collision occurrence time.

[0040] Specifically, vehicle state information, obstacle recognition results, and road information (including but not limited to road type and road surface adhesion coefficient) are collected by the vehicle, and a preset road simulation model corresponding to the current risk scenario category is built based on the vehicle state 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. By inputting the vehicle state information, obstacle recognition results, and road information collected by the vehicle into the vehicle model, obstacle model, and road model respectively, a preset road simulation model corresponding to the current risk scenario category can be built.

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

[0042] Exemplarily, assume the position of the vehicle is (x1, y1), the position of the obstacle is (x2, y2), and the distance between the vehicle and the obstacle is If this distance is less than the preset distance, it is determined that a collision has occurred between the vehicle and the obstacle, and then the current time stamp is the collision occurrence time.

[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, calculate the difference between the collision occurrence time and the fault occurrence time, and this difference is the fault tolerance time interval.

[0045] In some embodiments, determining the target driver behavior parameters based on the set of driver behavior parameters under the risk scenario category includes: determining the corresponding set of driver behavior parameters based on the risk scenario category; determining the range of probability density estimation according to the minimum and maximum values of the set of driver behavior parameters; determining a plurality of data points at a preset interval within the range of probability density estimation; determining the probability density estimation value corresponding to each data point based on a preset kernel function; fitting a probability density estimation curve according to the probability density estimation value corresponding to each data point; determining the target behavior parameters based on the maximum value point of the probability density estimation curve, where the target behavior parameters include the target driver reaction time, the target braking process time, and the target deceleration; wherein, the set of driver behavior parameters includes a subset of driver reaction times, a subset of braking process times, and a subset of decelerations, and the target driver reaction time, the target braking process time, and the target deceleration are determined respectively according to each subset. Wherein, the preset interval can be in milliseconds and can be specifically determined according to the actual situation, and no specific limitation is made here.

[0046] Specifically, statistical analysis is respectively performed on the subset of driver reaction times, the subset of braking process times, and the subset of decelerations, and the target driver reaction time, the target braking process time, and the target deceleration are determined through kernel density estimation.

[0047] In the following description, taking the determination of the target driver reaction time for the subset of driver reaction times through kernel density estimation as an example for illustration, but not as a limitation to this application.

[0048] Exemplarily, assume that the subset of driver reaction times is {1, 2, 3}, where the minimum value in the subset of driver reaction times is 1 and the maximum value is 3. Then, the range of probability density estimation is determined to be 1 - 3, and a plurality of data points are determined at a preset interval (for example, 0.1), such as the data points may include 1, 1.1, 1.2, …, 2.9, 3. The probability density estimation value corresponding to each data point is determined based on a preset kernel function (such as a Gaussian kernel function), and the expression of the probability density estimation value corresponding to each data point is as follows:

[0049]

[0050] Wherein, represents the probability density estimation value corresponding to each data point, x represents the data point, and x i represents the sample value in the i-th subset of driver reaction times, n represents the number of samples in the subset of driver reaction times, and h represents the bandwidth.

[0051] Assume that the bandwidth h is 0.5, and the calculation formula for the probability density estimation value corresponding to the data point 1.1 is as follows:

[0052]

[0053] And so on, determine the probability density estimation value corresponding to each data point, fit the probability density estimation values corresponding to each data point, determine the probability density estimation curve, and then take the derivative of the probability density estimation curve to calculate the maximum point of the probability density estimation curve. The data point corresponding to this maximum point is the target driver reaction time.

[0054] In some embodiments, the driver behavior parameters include the driver reaction time, the braking process time, and the deceleration. Among them, obtaining the driver behavior parameters includes: obtaining the vehicle data information of the risk scenario category, where the vehicle data information includes the moment of collision risk occurrence, the moment when the brake pedal is depressed, and the moment when the brake cylinder pressure stabilizes; determining the driver reaction time based on the difference between the moment when the brake pedal is depressed and the moment of collision risk occurrence; and determining the braking process time based on the difference between the moment when the brake cylinder pressure stabilizes and the moment when the brake pedal is depressed.

[0055] Exemplarily, assume that the moment of collision risk occurrence is T1, the moment when the brake pedal is depressed is T2, the moment when the brake cylinder pressure stabilizes is T3, and the deceleration after stabilization is a. Among them, the moment of collision risk occurrence T1 is the moment that satisfies the TTC (Time To Collision) threshold. For example, detect the distance between the vehicle and the obstacle through an in-vehicle radar or ultrasonic sensor, obtain the speed of the vehicle through a speed sensor. Assume that the obstacle is stationary, calculate the ratio of the distance between the vehicle and the obstacle to the speed of the vehicle as TTC, and use the timestamp when TTC is lower than the TTC threshold as the moment of collision risk occurrence T1. The moment when the brake pedal is depressed T2 is the timestamp when the brake pedal displacement sensor detects that the displacement exceeds a certain threshold (such as 0.1 mm). The brake cylinder pressure is detected and obtained through a brake cylinder pressure sensor. Calculate the pressure change rate according to the detected brake cylinder pressure data, and use the timestamp when the pressure change rate is less than a certain threshold (such as 0.05 bar / s) as the moment when the brake cylinder pressure stabilizes T3. The deceleration a after stabilization can be determined by calculating based on the vehicle speed detected by the vehicle speed sensor and the longitudinal acceleration of the vehicle.

[0056] Further, determine the driver reaction time based on the difference between the moment when the brake pedal is depressed and the moment of collision risk occurrence, that is, the driver reaction time = the moment when the brake pedal is depressed T2 - the moment of collision risk occurrence T1. Determine the braking process time based on the difference between the moment when the brake cylinder pressure stabilizes and the moment when the brake pedal is depressed, that is, the braking process time = the moment when the brake cylinder pressure stabilizes T3 - the moment when the brake pedal is depressed T2.

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

[0058] In some embodiments, determining the risk scenario category according to the longitudinal collision time and / or the lateral collision time includes: when the longitudinal collision time is within the first preset range, determining that the risk scenario category is the longitudinal primary collision risk; when the longitudinal collision time is within the second preset range, determining that the risk scenario category is the longitudinal secondary collision risk; wherein, the upper limit value of the second preset range is less than or equal to the lower limit value of the first preset range; when the lateral collision time is within the third preset range, determining that the risk scenario category is the lateral primary collision risk; when the lateral collision time is within the fourth preset range, determining that the risk scenario category is the lateral secondary collision risk; wherein, the upper limit value of the fourth preset range is less than or equal to the lower limit value of the third preset range.

[0059] Specifically, the vehicle state information includes the vehicle position, the vehicle lateral speed, and the vehicle longitudinal speed, and the obstacle recognition result includes the obstacle position, the obstacle lateral speed, and the obstacle longitudinal speed. The longitudinal collision distance and the lateral collision distance can be determined according to the vehicle position and the obstacle position. 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 speed is the absolute value of the difference between the vehicle longitudinal speed and the obstacle longitudinal speed, and the lateral relative speed is the absolute value of the difference between the vehicle lateral speed and the obstacle lateral speed. Then, the longitudinal collision time is determined based on the ratio of the longitudinal collision distance to the longitudinal relative speed, the lateral collision time is determined based on the ratio of the lateral collision distance to the lateral relative speed, and the specific collision risk level can be determined according to the magnitudes of the lateral collision time and the longitudinal collision time.

[0060] Exemplarily, when the longitudinal collision time interval is within the first preset range, for example, when the longitudinal collision time interval is greater than 1.5 s and less than 2 s, the risk scenario category is determined as the first-level longitudinal collision risk; when the longitudinal collision time interval is within the second preset range, for example, when the longitudinal collision time interval is greater than 1 s and less than 1.5 s, the risk scenario category is determined as the second-level longitudinal collision risk; when the lateral collision time interval is within the third preset range, for example, when the lateral collision time interval is greater than 1.5 s and less than 2 s, the risk scenario category is determined as the first-level lateral collision risk; when the lateral collision time interval is within the fourth preset range, for example, when the lateral collision time interval is greater than 1 s and less than 1.5 s, the risk scenario category is determined as the second-level lateral collision risk.

[0061] It should be noted that the specific collision risk levels are not limited to the first level and the second level.

[0062] As a specific example, referring to Figure 2 , the method for determining the fault tolerance time interval of the vehicle in the embodiment of the present application may include the following steps:

[0063] S201, the vehicle controller at the vehicle end sends the data including vehicle state information, obstacle recognition results, etc. to the vehicle-mounted 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 target behavior parameters of the driver under the risk scenario category.

[0066] S204, the simulation device performs simulation analysis according to the target behavior parameters of the driver output by the cloud server, combines the preset road simulation model to obtain the simulation data time series, determines the collision occurrence time according to the simulation data time series, and determines the fault tolerance time interval based on the collision occurrence time and the fault occurrence time.

[0067] In summary, the method for determining the fault tolerance time interval of the present application determines the target behavior parameters of the driver based on the set of driver behavior parameters aggregated 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 according to the target behavior parameters of the driver and the preset road simulation model is more reasonable and accurate, and the safety mechanism designed accordingly is also more reliable, which can better ensure the correct operation of the functional safety monitoring scheme in the real vehicle.

[0068] Corresponding to the above embodiment, the present application also proposes a device for determining the fault tolerance time interval of a vehicle.

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

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

[0071] According to an embodiment of the present application, the third determination module 340 is specifically used to: acquire the preset road simulation model corresponding to the risk scenario category; use the fault occurrence time and the target behavior parameters as the input of the preset road simulation model to output a time series of simulation data; acquire the vehicle position and the obstacle position corresponding to each timestamp in the time series of simulation data; and when it is determined according to the vehicle position and the obstacle position that the distance between the vehicle and the obstacle is less than the preset distance, use the current timestamp as the collision occurrence time.

[0072] According to an embodiment of the present application, the third determination module 340 is further used to determine the fault tolerance time interval based on the difference between the collision occurrence time and the fault occurrence time.

[0073] According to an embodiment of the present application, the second determination module 330 is specifically used to: determine the corresponding set of driver behavior parameters based on the risk scenario category; determine the range of probability density estimation according to the minimum value and the maximum value of the set of driver behavior parameters; determine a plurality of data points at preset intervals within the range of probability density estimation; determine the probability density estimation value corresponding to each data point based on a preset kernel function; fit a probability density estimation curve according to the probability density estimation value corresponding to each data point; and determine the target behavior parameters based on the maximum value 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; wherein, the set of driver behavior parameters includes a subset of driver reaction times, a subset of braking process times, and a subset of decelerations, and the target driver reaction time, the target braking process time, and the target deceleration are determined respectively according to each subset.

[0074] According to an embodiment of the present application, the driver behavior parameters include the driver reaction time, the braking process time, and the deceleration. Vehicle data information of the risk scenario category is obtained, where the vehicle data information includes the moment of collision risk occurrence, the moment when the brake pedal is depressed, and the moment when the brake cylinder pressure stabilizes; the driver reaction time is determined based on the difference between the moment when the brake pedal is depressed and the moment of collision risk occurrence; 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.

[0075] According to an embodiment of the present application, the first determination module 320 is specifically configured to determine the longitudinal collision distance, the lateral collision distance, the longitudinal relative speed, and the lateral relative speed according to the vehicle state information and the obstacle recognition result; determine the longitudinal collision time based on the ratio of the longitudinal collision distance to the longitudinal relative speed; determine the lateral collision time based on the ratio of the lateral collision distance to the lateral relative speed; determine the risk scenario category according to the longitudinal collision time and / or the lateral collision time.

[0076] According to an embodiment of the present application, the first determination module 320 is further configured to, when the longitudinal collision time is within the first preset range, determine that the risk scenario category is a first-level longitudinal collision risk; when the longitudinal collision time is within the second preset range, determine that the risk scenario category is a second-level longitudinal collision risk; where the upper limit value of the second preset range is less than or equal to the lower limit value of the first preset range; when the lateral collision time is within the third preset range, determine that the risk scenario category is a first-level lateral collision risk; when the lateral collision time is within the fourth preset range, determine that the risk scenario category is a second-level lateral collision risk; where the upper limit value of the fourth preset range is less than or equal to the lower limit value of the third preset range.

[0077] It should be noted that the above explanations of the embodiments and beneficial effects of the method for determining the fault tolerance time interval of the vehicle are also applicable to the device for determining the fault tolerance time interval of the vehicle in the embodiments of the present application. To avoid redundancy, no detailed elaboration will be made here.

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

[0079] The computer-readable storage medium of the present application stores a program for determining the fault tolerance time interval of the vehicle. When the program for determining the fault tolerance time interval of the vehicle is executed by a processor, the foregoing method for determining the fault tolerance time interval of the vehicle is implemented.

[0080] It should be noted that the above explanations of the embodiments and beneficial effects of the method for determining the fault tolerance time interval of the vehicle are also applicable to the computer-readable storage medium in the embodiments of the present application. To avoid redundancy, no detailed elaboration will be made here.

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

[0082] Referring to Figure 4 As shown, the vehicle 400 of the present application includes a memory 410, a processor 420, and a program for determining the fault tolerance time interval of the vehicle stored on the memory 410 and executable on the processor 420. When the processor executes the program for determining the fault tolerance time interval of the vehicle, the foregoing method for determining the fault tolerance time interval of the vehicle is implemented.

[0083] It should be noted that the above explanations of the embodiments and beneficial effects of the method for determining the fault tolerance time interval of the vehicle also apply to the vehicle of the embodiments of the present application. To avoid redundancy, no detailed elaboration will be made here.

[0084] It should be noted that the logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion having one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

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

[0086] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0087] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of the present application, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically and clearly defined.

[0088] In the present application, unless otherwise clearly specified and limited, the terms "mounted", "connected", "coupled", "fixed", etc. should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal connection or the interaction relationship between two components, unless otherwise clearly limited. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

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

Claims

1. A method for determining a fault tolerance time interval of a vehicle, characterized in that: The method comprises: Obtain vehicle status information and obstacle recognition results; Determining a risk scenario category according to the vehicle state information and the obstacle recognition result; Determining a target behavior parameter of the driver based on a set of driver behavior parameters under the risk scenario category; A simulation analysis is performed according to the target behavior parameters of the driver and a preset road simulation model to determine the time when a collision occurs, and a fault tolerance time interval is determined based on the time when the collision occurs and the time when a fault occurs.

2. The method for determining the fault tolerance time interval of a vehicle according to claim 1, characterized in that: Performing simulation analysis based on the target behavior parameters of the driver and a preset road simulation model to determine the collision occurrence time includes: Obtaining a preset road simulation model corresponding to the risk scenario category; Using the fault occurrence time and the target behavior parameter as inputs of 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; When it is determined according to the vehicle position and the obstacle position that the distance between the vehicle and the obstacle is less than a preset distance, the current timestamp is used as the collision occurrence time.

3. The method for determining the fault tolerance time interval of a vehicle according to claim 2, characterized in that: Determining a 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 collision occurrence time and the fault occurrence time.

4. The method for determining the fault tolerance time interval of a vehicle according to claim 1, characterized in that: Determining the target behavior parameter of the driver based on the driver behavior parameter set under the risk scenario category includes: Determining a corresponding set of driver behavior parameters based on the risk scenario category; Determining a range of probability density estimation according to a minimum value and a maximum value of the driver behavior parameter set; Determining a plurality of data points at predetermined intervals within the range of the probability density estimate; Determine a probability density estimate corresponding to each of the data points based on a preset kernel function; Fitting a probability density estimation curve according to the probability density estimation value corresponding to each of the data points; Determining target behavior parameters based on the maximum point of the probability density estimation curve, wherein the target behavior parameters include target driver reaction time, target braking process time and target deceleration; The driver behavior parameter set includes a driver reaction time subset, a braking process time subset and a deceleration subset, and the target driver reaction time, the target braking process time and the target deceleration are determined according to each subset respectively.

5. The method for determining the fault tolerance time interval of a vehicle according to claim 4, characterized in that: The driver behavior parameters include driver reaction time, braking process time and deceleration, wherein obtaining the driver behavior parameters includes: Acquiring vehicle data information of the risk scenario category, wherein the vehicle data information includes a collision risk occurrence time, a brake pedal depression time, and a brake cylinder pressure stabilization time; determining the driver reaction time based on the difference between the moment when the brake pedal is depressed and the moment when the collision risk occurs; The braking process time is determined based on a difference between the brake cylinder pressure stabilization time and the brake pedal depression time.

6. The method for determining the fault tolerance time interval of a vehicle according to claim 1, characterized in that: Determining a risk scenario category according to the vehicle state information and the obstacle recognition result includes: Determining a longitudinal collision distance, a lateral collision distance, a longitudinal relative speed, and a lateral relative speed according to the vehicle state information and the obstacle recognition result; determining a longitudinal collision time interval based on a ratio of the longitudinal collision distance to the longitudinal relative speed; determining a lateral collision time interval based on a ratio of the lateral collision distance to the lateral relative speed; The risk scenario category is determined according to the longitudinal collision time interval and / or the lateral collision time interval.

7. The method for determining the fault tolerance time interval of a vehicle according to claim 6, characterized in that: Determining the risk scenario category according to the longitudinal collision time interval and / or the lateral collision time interval includes: When the longitudinal collision time interval is within a first preset range, determining the risk scenario category as a longitudinal level 1 collision risk; When the longitudinal collision time interval is within a second preset range, determining that the risk scenario category is a longitudinal secondary collision risk; wherein the upper limit value of the second preset range is less than or equal to the lower limit value of the first preset range; When the lateral collision time interval is within a third preset range, determining the risk scenario category as a lateral level one collision risk; When the lateral collision time interval is within a fourth preset range, the risk scenario category is determined to be a lateral secondary collision risk; wherein an upper limit value of the fourth preset range is less than or equal to a lower limit value of the third preset range.

8. A device for determining a fault tolerance time interval of a vehicle, characterized in that: The device comprises: A first acquisition module is used to acquire vehicle status information and obstacle recognition results; A first determination module, configured to determine a risk scenario category according to the vehicle state information and the obstacle recognition result; A second determination module determines a target behavior parameter of the driver based on a set of driver behavior parameters under the risk scenario category; The third determination module is used to perform simulation analysis according to the target behavior parameters of the driver and a preset road simulation model to determine the time when the collision occurs, and to determine the fault tolerance time interval based on the time when the collision occurs and the time when the fault occurs.

9. A computer-readable storage medium, characterized in that: A vehicle fault-tolerant time interval determination program is stored thereon, and when the vehicle fault-tolerant time interval determination program is executed by a processor, a vehicle fault-tolerant time interval determination method according to any one of claims 1-7 is implemented.

10. A vehicle, characterized in that: The invention comprises a memory, a processor and a vehicle fault-tolerant time interval determination program stored in the memory and executable on the processor. When the processor executes the vehicle fault-tolerant time interval determination program, the vehicle fault-tolerant time interval determination method according to any one of claims 1 to 7 is implemented.

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