Fault monitoring methods, devices, vehicles, and readable storage media

Abstract

This application provides a fault monitoring method, device, vehicle, and readable storage medium, relating to the field of vehicle technology. The method includes: acquiring the vehicle's current steering speed signal and steering wheel angle value; determining an estimated steering angle value for the vehicle based on the steering speed signal and a preset state estimation algorithm; determining a probability density value based on the difference between the estimated steering angle value and the steering wheel angle value; and outputting a fault alarm signal when the probability density value is less than a preset density threshold. The preset density threshold represents the probability density value corresponding to a situation where the deviation between the vehicle's steering wheel angle and wheel angle is not greater than a preset deviation threshold. The fault alarm signal is used to indicate that the deviation between the vehicle's steering wheel angle and wheel angle is greater than the preset deviation threshold. This allows users to promptly handle faults, eliminate them, reduce safety risks during driving, and improve vehicle driving safety.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a fault monitoring method, device, vehicle, and readable storage medium. Background Technology

[0002] In vehicle steer-by-wire systems, to prevent the motor belt from slipping between itself and the ball screw during torque transmission, and to prevent excessive reverse force transmitted from the road surface through the wheels, steering knuckles, tie rods, and racks to the ball screw, which could cause the motor belt to break, existing technologies reduce the risk of motor belt slippage and breakage by adding a tolerance ring between the motor belt and the ball screw.

[0003] However, in existing technologies, when the reverse force transmitted from the road surface to the ball screw via the wheel and rack is greater than the maximum static friction between the tolerance ring and the ball screw, relative slippage will occur between the tolerance ring and the ball screw. This causes a shift in the relationship between the steering wheel angle measured by the sensor at the steering wheel and the actual displacement of the rack in the steer-by-wire system. As a result, there is a deviation between the steering wheel angle and the wheel angle, but the user cannot perceive the deviation, which poses a safety risk during vehicle driving. Summary of the Invention

[0004] In view of this, this application aims to provide a fault monitoring method, device, vehicle, and readable storage medium to monitor faults in which the steering wheel angle and wheel angle of a vehicle deviate from each other.

[0005] To achieve the above objectives, the technical solution of this application is implemented as follows:

[0006] A fault monitoring method, the method comprising:

[0007] Obtain the vehicle's current steering speed signal and steering wheel angle value;

[0008] Based on the steering speed signal and a preset state estimation algorithm, the estimated steering angle of the vehicle is determined;

[0009] The probability density value is determined based on the difference between the estimated steering angle and the steering wheel angle value.

[0010] When the probability density value is less than a preset density threshold, a fault alarm signal is output; the preset density threshold represents the probability density value corresponding to the case where the deviation between the steering wheel angle and the wheel angle of the vehicle is not greater than a preset deviation threshold, and the fault alarm signal is used to indicate that the deviation between the steering wheel angle and the wheel angle of the vehicle is greater than the preset deviation threshold.

[0011] Optionally, the steering speed signal includes the vehicle's yaw rate and lateral acceleration; the steering angle estimate includes a first steering angle estimate corresponding to the yaw rate and a second steering angle estimate corresponding to the lateral acceleration; determining the probability density value based on the difference between the steering angle estimate and the steering wheel angle value includes:

[0012] The difference between the first estimated steering angle and the steering wheel angle value is determined as the first target difference.

[0013] The difference between the second estimated steering angle and the steering wheel angle value is determined as the second target difference.

[0014] The probability density value is determined based on the first target difference, the second target difference, and the preset probability density function.

[0015] Optionally, determining the probability density value based on the first target difference, the second target difference, and a preset probability density function includes:

[0016] Calculate the target mean based on the first target difference and the second target difference;

[0017] The first target difference, the second target difference, and the target mean are used as inputs to the probability density function, and the output value of the probability density function is determined as the probability density value.

[0018] Optionally, determining the estimated steering angle of the vehicle based on the steering speed signal and a preset state estimation algorithm includes:

[0019] The first observation variable of the state estimation algorithm is determined based on the yaw rate, and the second observation variable of the state estimation algorithm is determined based on the lateral acceleration.

[0020] The first observed variable is input into the state estimation algorithm, and the first angle estimate corresponding to the yaw rate is determined according to the first output of the state estimation algorithm. The second observed variable is input into the state estimation algorithm, and the second angle estimate corresponding to the lateral acceleration is determined according to the second output of the state estimation algorithm.

[0021] Optionally, before outputting the fault alarm signal, the method further includes:

[0022] The preset fault count parameters are updated to obtain the updated fault count parameters;

[0023] The output fault alarm signal includes:

[0024] If the updated fault counting parameter value is not less than the preset counting threshold, the fault alarm signal is output.

[0025] Optionally, the preset density threshold is obtained in the following way:

[0026] When the vehicle's steering wheel is at the zero point and the vehicle's wheel angle is at a preset calibration angle, the vehicle's steering calibration speed signal and steering wheel angle calibration value are acquired.

[0027] The steering angle calibration value of the vehicle is determined based on the steering calibration speed signal and the state estimation algorithm.

[0028] The probability density calibration value is determined based on the difference between the angle calibration value and the steering wheel angle calibration value.

[0029] The preset density threshold is determined based on the probability density calibration value.

[0030] Another object of this application is to provide a fault monitoring device, the device comprising:

[0031] The acquisition module is used to acquire the vehicle's current steering speed signal and steering wheel angle value;

[0032] The first determining module is used to determine the estimated steering angle of the vehicle based on the steering speed signal and a preset state estimation algorithm.

[0033] The second determining module is used to determine the probability density value based on the difference between the estimated turning angle and the steering wheel turning angle value;

[0034] The output module is used to output a fault alarm signal when the probability density value is less than a preset density threshold; the preset density threshold represents the probability density value corresponding to the case where the deviation between the steering wheel angle and the wheel angle of the vehicle is not greater than a preset deviation threshold, and the fault alarm signal is used to represent the case where the deviation between the steering wheel angle and the wheel angle of the vehicle is greater than the preset deviation threshold.

[0035] Optionally, the steering speed signal includes the vehicle's yaw rate and lateral acceleration; the steering angle estimate includes a first steering angle estimate corresponding to the yaw rate and a second steering angle estimate corresponding to the lateral acceleration; the second determining module is specifically used for:

[0036] The difference between the first estimated steering angle and the steering wheel angle value is determined as the first target difference.

[0037] The difference between the second estimated steering angle and the steering wheel angle value is determined as the second target difference.

[0038] The probability density value is determined based on the first target difference, the second target difference, and the preset probability density function.

[0039] Optionally, the second determining module is further configured to:

[0040] Calculate the target mean based on the first target difference and the second target difference;

[0041] The first target difference, the second target difference, and the target mean are used as inputs to the probability density function, and the output value of the probability density function is determined as the probability density value.

[0042] Optionally, the first determining module is specifically used for:

[0043] The first observation variable of the state estimation algorithm is determined based on the yaw rate, and the second observation variable of the state estimation algorithm is determined based on the lateral acceleration.

[0044] The first observed variable is input into the state estimation algorithm, and the first angle estimate corresponding to the yaw rate is determined according to the first output of the state estimation algorithm. The second observed variable is input into the state estimation algorithm, and the second angle estimate corresponding to the lateral acceleration is determined according to the second output of the state estimation algorithm.

[0045] Optionally, the device further includes:

[0046] The update module is used to update the parameter value of the preset fault count parameter before the output module outputs the fault alarm signal, so as to obtain the updated fault count parameter.

[0047] The output module is specifically used for:

[0048] If the updated fault counting parameter value is not less than the preset counting threshold, the fault alarm signal is output.

[0049] Another object of this application is to provide a vehicle equipped with a fault monitoring device as described in any of the above descriptions, for performing a fault monitoring method as described in any of the above descriptions.

[0050] Another object of this application is to provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the fault monitoring method as described above.

[0051] This application acquires the vehicle's current steering speed signal and steering wheel angle value; determines the vehicle's estimated steering angle value based on the steering speed signal and a preset state estimation algorithm; determines a probability density value based on the difference between the estimated steering angle value and the steering wheel angle value; and outputs a fault alarm signal when the probability density value is less than a preset density threshold. The preset density threshold represents the probability density value corresponding to the condition that the deviation between the vehicle's steering wheel angle and wheel angle is not greater than a preset deviation threshold, and the fault alarm signal is used to indicate that the deviation between the vehicle's steering wheel angle and wheel angle is greater than the preset deviation threshold. In this way, since the preset density threshold represents the probability density value corresponding to the condition that the deviation between the vehicle's steering wheel angle and wheel angle is not greater than the preset deviation threshold, and the probability density value is determined based on the difference between the estimated angle value and the obtained steering wheel angle value, it can be determined that when the probability density value is less than the preset density threshold, the deviation between the estimated angle value and the steering wheel angle value is greater than the preset deviation threshold. It can be understood that the estimated angle value of the vehicle can represent the estimated angle value of the vehicle's wheels. Furthermore, it can be determined that the deviation between the estimated wheel angle value and the steering wheel angle is greater than the preset deviation threshold, thereby realizing fault monitoring for the deviation between the vehicle's steering wheel angle and wheel angle. By outputting a fault alarm signal, users can conveniently and promptly know that the deviation between the vehicle's steering wheel angle and wheel angle is greater than the preset deviation threshold. This allows users to promptly handle the fault of the deviation between the vehicle's steering wheel angle and wheel angle, eliminate the fault, reduce safety risks during driving, and improve vehicle driving safety. Attached Figure Description

[0052] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0053] Figure 1 This is a flowchart illustrating the steps of a fault monitoring method according to an embodiment of this application.

[0054] Figure 2 This is a schematic diagram of the two-dimensional probability density function described in the embodiments of this application;

[0055] Figure 3 This is a flowchart illustrating another fault monitoring method described in an embodiment of this application;

[0056] Figure 4 This is a schematic diagram of the composition of a fault monitoring device according to an embodiment of this application;

[0057] Figure 5 This is a schematic diagram of another fault monitoring device described in an embodiment of this application. Detailed Implementation

[0058] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0059] The present application will now be described in detail with reference to the accompanying drawings and embodiments.

[0060] This application provides a fault monitoring method, such as... Figure 1 As shown, the fault monitoring method provided in this application embodiment may include the following steps:

[0061] Step 101: Obtain the vehicle's current steering speed signal and steering wheel angle value.

[0062] In this embodiment, the vehicle's current steering speed signal can be a signal related to the vehicle's current steering speed state, such as the vehicle's yaw rate and lateral acceleration. Yaw rate represents the angular velocity of the vehicle's circular motion around an axis perpendicular to the ground, and is a key parameter for lateral stability when the vehicle is turning. Lateral acceleration represents the acceleration of the vehicle in the left-to-right or right-to-left direction; for example, when a vehicle is turning on a level surface, lateral friction provides acceleration. The steering wheel angle value can be a measurement of the steering wheel angle obtained from an angle sensor at the vehicle's steering wheel.

[0063] In this embodiment, steering speed signals such as yaw rate and lateral acceleration, as well as steering wheel angle values, can be directly obtained from the vehicle's braking system via the vehicle's bus network. For specific methods of obtaining steering speed signals and steering wheel angle values ​​in the vehicle's braking system, please refer to the relevant descriptions in the prior art. This embodiment does not limit these methods.

[0064] Step 102: Determine the estimated turning angle of the vehicle based on the steering speed signal and the preset state estimation algorithm.

[0065] In this embodiment, the preset state estimation algorithm can be a state estimation algorithm based on the Kalman filter algorithm. Specifically, in the prior art, the Discrete Kalman Filter (KF) algorithm uses feedback control to estimate the process state. First, there is a prediction process, which uses the state equation of the discrete system to calculate the values ​​of the current state variables and the error covariance matrix to construct a prior state estimate for the state at the next moment. The prediction equation is shown in the following formulas (1) and (2). Then there is an update process, which combines the prior state estimate with the new measurement variables to form a posterior state estimate and feeds it back. The update equation is shown in the following formulas (3) to (5):

[0066]

[0067]

[0068]

[0069]

[0070]

[0071] in, This represents the state variables of the prediction process at time k. The state variables representing the update process at time k-1 are... This represents the state variable representing the update process at time k. Let P represent the n×n prior state estimation error covariance matrix at time k. k-1 Let P be the n×n posterior state estimation error covariance matrix at time k-1. k Z represents the n×n posterior state estimation error covariance matrix at time k. k Let A represent the observed variable, and A represent the variable that acts on the updated state variable. The n×n state transition matrix on the control vector U, where B represents the state transition matrix acting on the control vector U. k The input control matrix is ​​an n×1 matrix, Q represents the n×n process noise covariance matrix, and K represents the input control matrix. k Let H represent the n×m Kalman gain (or mixing factor), H represent the n×m observation matrix, R represent the m×m measurement noise covariance matrix, and I represent the n×n identity matrix.

[0072] It should be noted that the Kalman filter algorithm is a state-optimal estimation method that uses the prediction equation and the update equation to optimally estimate the state of the system. Here, the state variables at time k are input to the prediction equation. and the posterior state estimation error covariance matrix P k-1 , is the state variable obtained from the update equation at time k-1. and the posterior state estimation error covariance matrix P k-1 The state variable output by the prediction equation at time k is... and the prior state estimation error covariance matrix As input to the update equation at time k, the update process at time k is obtained. and the posterior state estimation error covariance matrix P k .

[0073] In the prior art, for the yaw rate of a vehicle, based on the prediction equation and update equation of the Kalman filter algorithm, the system dynamics model of the steer-by-wire system and the vehicle kinematics model, the system discrete equation and the first observation equation corresponding to the steer-by-wire system of the vehicle are obtained as shown in the following formulas (6) and (7):

[0074]

[0075]

[0076] in, This indicates the yaw rate of the vehicle. The corresponding observed variable in the Kalman filter algorithm, δ k+1 δ represents the estimated first turning angle at time k+1. k δ represents the first rotation angle measurement at time k. k Corresponding to the state variables in the Kalman filter algorithm, K represents the derivative corresponding to the first angle measurement, Δt represents the time difference, and K represents the time difference. T The torque coefficient of the motor is represented by 'a', and the system inertia of the steer-by-wire system equivalent to the column is represented by 'a', where 'a' = J. eq / k,J eq I represents the equivalent moment of inertia of the steering system, K represents the stability factor, and I represents the stability factor. k Indicates the motor current, W k Indicates process noise, u k Represents the control vector, l represents the wheelbase, and V 1k This represents the noise from the yaw rate measurement. For the vehicle's lateral acceleration, based on the prediction and update equations of the Kalman filter algorithm, the system dynamics model of the steer-by-wire system, and the two-degree-of-freedom vehicle model, the second observation equation is obtained as shown in formula (8):

[0077]

[0078] Among them, a yk a represents the lateral acceleration of the vehicle. yk The corresponding observed variable in the Kalman filter algorithm, δ′ k u represents the second rotation angle measurement at time k. k Let l represent the control vector, l represent the wheelbase, and K represent the control vector. k V represents the n×m order Kalman gain (or mixing factor). 2k This indicates the noise level in the lateral acceleration measurement.

[0079] In this embodiment, the steering speed signal can be the yaw rate of the vehicle, and the state estimation algorithm can include formulas (6) and (7). The current yaw rate of the vehicle can be used as the input of the state estimation algorithm, that is, the yaw rate... Input formula (7) to obtain the yaw rate. The corresponding first rotation angle measurement value δ k Then measure the first turning angle δ k Input formula (6) to obtain the yaw rate. The corresponding first turning angle estimate δ 1k .

[0080] In another feasible implementation, the steering speed signal can be the lateral acceleration of the vehicle, and the state estimation algorithm can include formulas (6) and (8), which can use the current lateral acceleration of the vehicle as the input of the state estimation algorithm, that is, the lateral acceleration a yk Input formula (8) to obtain the lateral acceleration a. yk The corresponding second turning angle measurement value δ′ k Then measure the second turning angle δ′ k Input formula (6) to obtain the lateral acceleration a. yk The corresponding second turning angle estimate δ 2k .

[0081] Step 103: Determine the probability density value based on the difference between the estimated steering angle and the steering wheel angle value.

[0082] In this embodiment, the probability density value can be determined based on the difference between the estimated steering angle and the steering wheel angle, and a preset probability density function. Specifically, the difference between the estimated steering angle and the steering wheel angle can be calculated, and then the difference between the estimated steering angle and the steering wheel angle can be used as the input of the probability density function, and the output value of the probability density function can be determined as the probability density value. The probability density function can be a multidimensional probability density function, and the dimension of the multidimensional probability density function can be determined based on the number of signal types included in the steering speed signal. For example, the steering speed signal can include yaw rate and lateral acceleration, so the probability density function can be a two-dimensional probability density function. Specifically, the multidimensional probability density function can refer to the following formula (9):

[0083]

[0084] Where X represents the difference between the estimated steering angle and the steering wheel angle, d represents the dimension of X, μ represents the mean of each dimension of the variables included in X, and ∑ represents the covariance matrix used to describe the correlation between the variables in X. ∑ can be composed of the variance of each variable in X and the covariance of each variable.

[0085] Step 104: When the probability density value is less than the preset density threshold, output a fault alarm signal; the preset density threshold represents the probability density value corresponding to the case where the deviation between the steering wheel angle and the wheel angle of the vehicle is not greater than the preset deviation threshold, and the fault alarm signal is used to represent the case where the deviation between the steering wheel angle and the wheel angle of the vehicle is greater than the preset deviation threshold.

[0086] In this embodiment, the preset density threshold can be determined by acquiring the vehicle's steering calibration speed signal and steering wheel angle calibration value when the deviation between the vehicle's steering wheel angle and wheel angle is not greater than a preset deviation threshold. Based on the steering calibration speed signal and a state estimation algorithm, the vehicle's angle calibration value is determined. Then, based on the difference between the angle calibration value and the steering wheel angle calibration value, a probability density calibration value is determined. Finally, the probability density threshold is determined based on the probability density calibration value. Specifically, for any vehicle, the preset density threshold can be pre-calibrated through a whole-vehicle test. By setting the vehicle's steering wheel to a zero position and the vehicle's wheel angles to multiple preset calibration angles, the vehicle's steering wheel angle calibration value and multiple steering calibration speed signals are acquired. Multiple angle calibration values ​​are determined based on the multiple steering calibration speed signals and a state estimation algorithm. Then, multiple probability density calibration values ​​are determined based on the difference between each angle calibration value and the steering wheel angle calibration value. Finally, the preset density threshold is determined based on the multiple probability density calibration values. Specifically, the probability density calibration value when the wheel angle is 0° can be determined as the preset density threshold. The preset deviation threshold is the difference between the steering wheel angle calibration value and the wheel angle of 0°. It represents that as long as there is a deviation between the steering wheel angle and the wheel angle, a fault is considered to have occurred, and a fault alarm signal is output.

[0087] Alternatively, the preset calibration angle can include 0° and ±1°. Using 0° as a baseline, probability density calibration values ​​are determined for the three cases of 0° and ±1°. Then, the first probability density calibration values ​​corresponding to 0° and 1°, and the second probability density calibration values ​​corresponding to 0° and -1° are calculated respectively. Further, the mean of the first and second probability density calibration values ​​is used as a preset density threshold. The preset deviation threshold can be based on the difference between the steering wheel angle calibration value and 0°, plus the standard deviation of the differences between the steering wheel angle calibration value and the three preset calibration angles of 0° and ±1°. This represents the allowable deviation between the vehicle's steering wheel angle and wheel angle not exceeding the preset deviation threshold. This is merely an example, and the embodiments of this application do not impose limitations. In this way, when there is a deviation between the vehicle's steering wheel angle and wheel angle, but the deviation is not greater than the preset deviation threshold and will not cause danger, unnecessary fault alarm signals can be avoided, thus improving the practicality of the fault monitoring method of this application.

[0088] In this embodiment, the probability density value can be compared with a preset density threshold. When the probability density value is less than the preset density threshold, a fault alarm signal can be generated based on the difference between the estimated steering angle and the steering wheel angle value. The fault alarm signal can include fault alarm information, which may be that the deviation between the steering wheel angle and the wheel angle of the vehicle is greater than a preset deviation threshold. Then, the fault alarm signal is output through the vehicle's display, voice, and other systems to remind the vehicle user to handle the fault, such as taking the vehicle to a repair shop to check and handle the deviation between the steering wheel angle and the wheel angle, so as to eliminate the fault and improve vehicle driving safety.

[0089] In this embodiment, the vehicle's current steering speed signal and steering wheel angle value are acquired; based on the steering speed signal and a preset state estimation algorithm, the estimated steering angle value of the vehicle is determined; based on the difference between the estimated steering angle value and the steering wheel angle value, a probability density value is determined; when the probability density value is less than a preset density threshold, a fault alarm signal is output; the preset density threshold represents the probability density value corresponding to the condition that the deviation between the vehicle's steering wheel angle and wheel angle is not greater than a preset deviation threshold, and the fault alarm signal is used to indicate that the deviation between the vehicle's steering wheel angle and wheel angle is greater than the preset deviation threshold. In this way, since the preset density threshold represents the probability density value corresponding to the condition that the deviation between the vehicle's steering wheel angle and wheel angle is not greater than the preset deviation threshold, and the probability density value is determined based on the difference between the estimated angle value and the obtained steering wheel angle value, it can be determined that when the probability density value is less than the preset density threshold, the deviation between the estimated angle value and the steering wheel angle value is greater than the preset deviation threshold. It can be understood that the estimated angle value of the vehicle can represent the estimated angle value of the vehicle's wheels. Furthermore, it can be determined that the deviation between the estimated wheel angle value and the steering wheel angle is greater than the preset deviation threshold, thereby realizing fault monitoring for the deviation between the vehicle's steering wheel angle and wheel angle. By outputting a fault alarm signal, users can conveniently and promptly know that the deviation between the vehicle's steering wheel angle and wheel angle is greater than the preset deviation threshold. This allows users to promptly handle the fault of the deviation between the vehicle's steering wheel angle and wheel angle, eliminate the fault, reduce safety risks during driving, and improve vehicle driving safety.

[0090] Optionally, the steering speed signal includes the vehicle's yaw rate and lateral acceleration; the steering angle estimate includes a first steering angle estimate corresponding to the yaw rate and a second steering angle estimate corresponding to the lateral acceleration; step 103 may include the following steps:

[0091] Step 1031: The difference between the first estimated steering angle and the steering wheel angle value is determined as the first target difference.

[0092] Step 1032: The difference between the second estimated turning angle and the steering wheel turning angle value is determined as the second target difference.

[0093] Step 1033: Determine the probability density value based on the first target difference, the second target difference, and the preset probability density function.

[0094] In this embodiment, the yaw rate and lateral acceleration can be directly obtained from the vehicle's braking system via the vehicle's bus network. For specific methods of obtaining the yaw rate and lateral acceleration of the vehicle's braking system, please refer to the relevant descriptions in the prior art. This embodiment does not limit these methods.

[0095] In this embodiment, the estimated first rotation angle corresponding to the yaw rate can be referred to the relevant description in step 102, that is, based on the yaw rate. Formulas (6) and (7) yield the yaw rate. The corresponding first turning angle estimate δ 1k The estimated value of the second turning angle corresponding to the lateral acceleration can be found in the relevant description in step 102, i.e., based on the lateral acceleration a. yk From formulas (6) and (8), we obtain the lateral acceleration a. yk The corresponding second turning angle estimate δ 2k .

[0096] In this embodiment, the preset probability density function can be a multidimensional probability density function, and the dimension of the multidimensional probability density function can be determined according to the number of signal types included in the steering speed signal. In this step, the steering speed signal includes yaw rate and lateral acceleration, so the dimension of the multidimensional probability density function in this step is 2.

[0097] In this embodiment of the application, the difference between the first estimated steering angle and the steering wheel angle value can be determined as the first target difference, and the difference between the second estimated steering angle and the steering wheel angle value can be determined as the second target difference. Specifically, as shown in the following formulas (10) and (11),

[0098] x1=δ 1k -δ (10)

[0099] x2=δ 2k -δ (11)

[0100] Where x1 represents the first target difference, x2 represents the second target difference, and δ 1k δ represents the estimated value of the first turning angle. 2k This represents the estimated value of the second turning angle, and δ represents the steering wheel angle value.

[0101] Furthermore, the dimension of the preset multidimensional probability density function can be set to 2 to obtain a two-dimensional probability density function. Then, the first target difference x1 and the second target difference x2 are substituted into the two-dimensional probability density function to obtain the output value of the two-dimensional probability density function, and the output value of the two-dimensional probability density function is determined as the probability density value. Specifically, in the above formula (9), d = 2, and the two-dimensional probability density function can be obtained by referring to the following formula (12):

[0102]

[0103] Where X includes the first target difference and the second target difference, i.e., X = (x1, x2), and μ represents the mean of the first target difference and the second target difference. ∑ represents the covariance matrix. For any vehicle, ∑ can be obtained in advance through experiments with multiple yaw rates and multiple lateral accelerations of the vehicle. The variances of the multiple yaw rates, the variances of the multiple lateral accelerations, and the covariance between the multiple yaw rates and the multiple lateral accelerations are calculated respectively. The obtained variances and covariances are used to form the covariance matrix ∑. The specific calculation process can refer to the relevant descriptions of covariance matrices in the prior art. This application does not limit this process.

[0104] In this embodiment, the steering speed signal includes the vehicle's yaw rate and lateral acceleration; the steering angle estimate includes a first steering angle estimate corresponding to the yaw rate and a second steering angle estimate corresponding to the lateral acceleration; the difference between the first steering angle estimate and the steering wheel angle value is determined as a first target difference; the difference between the second steering angle estimate and the steering wheel angle value is determined as a second target difference; and a probability density value is determined based on the first target difference, the second target difference, and a preset probability density function. Thus, the difference between the steering angle estimate and the steering wheel angle value includes both the first and second target differences, increasing the sample size of the difference between the steering angle estimate and the steering wheel angle value. Furthermore, by determining the probability density value based on the first target difference, the second target difference, and the preset probability density function, the accuracy of the probability density value can be improved through more difference samples. This further makes the fault alarm signal output when the probability density value is less than a preset density threshold more accurate, thereby improving the accuracy of the fault monitoring method in this embodiment to a certain extent.

[0105] Optionally, step 1033 may include the following steps:

[0106] Step 1033a: Calculate the target mean based on the first target difference and the second target difference.

[0107] Step 1033b: The first target difference, the second target difference, and the target mean are used as inputs to the probability density function, and the output value of the probability density function is determined as the probability density value.

[0108] In this embodiment, the mean of the first target difference and the second target difference can be calculated, and the calculated mean is used as the target mean. Then, the first target difference, the second target difference, and the target mean are used as inputs to a two-dimensional probability density function, and the output value of the two-dimensional probability density function is determined as the probability density value. Specifically, the two-dimensional probability density function can be referred to as formula (12), where x1 represents the first target difference, x2 represents the second target difference, This represents the target mean.

[0109] Figure 2 This is a schematic diagram of the two-dimensional probability density function provided in the embodiments of this application, as shown below. Figure 2 As shown, Figure 2 In the diagram, the x-axis and y-axis represent the values ​​of two variables, x1 and x2, respectively, while the z-axis represents the output value of the two-dimensional probability density function, i.e., the probability density value.

[0110] In this embodiment, a target mean is calculated based on the first target difference and the second target difference; the first target difference, the second target difference, and the target mean are used as inputs to the probability density function, and the output value of the probability density function is determined as the probability density value. This allows for direct solution of the probability density function based on the first target difference, the second target difference, and the target mean, thereby determining the probability density value by its output value, which improves the efficiency of obtaining the probability density value to some extent.

[0111] Optionally, step 102 may include the following steps:

[0112] Step 1021: Determine the first observation variable of the state estimation algorithm based on the yaw rate, and determine the second observation variable of the state estimation algorithm based on the lateral acceleration.

[0113] Step 1022: Input the first observed variable into the state estimation algorithm and determine the first angle estimate corresponding to the yaw rate based on the first output of the state estimation algorithm; input the second observed variable into the state estimation algorithm and determine the second angle estimate corresponding to the lateral acceleration based on the second output of the state estimation algorithm.

[0114] In this embodiment of the application, the state estimation algorithm may include formulas (6) to (8), and the yaw rate can be determined as the first observed variable of the state estimation algorithm. Lateral acceleration can be determined as the second observation variable 'a' in the state estimation algorithm. yk .

[0115] In this embodiment of the application, the current yaw rate of the vehicle can be used as the input to the state estimation algorithm, that is, the yaw rate... Input formula (7) to obtain the yaw rate. The corresponding first rotation angle measurement value δ k Then measure the first turning angle δ k Input formula (6) to obtain the yaw rate. The corresponding first turning angle estimate δ 1k Furthermore, the vehicle's current lateral acceleration can be used as input to the state estimation algorithm, i.e., the lateral acceleration a... yk Input formula (8) to obtain the lateral acceleration a. yk The corresponding second turning angle measurement value δ′ k Then measure the second turning angle δ′ k Input formula (6) to obtain the lateral acceleration a. yk The corresponding second turning angle estimate δ 2k .

[0116] In this embodiment, the state estimation algorithm determines a first observation variable based on the yaw rate and a second observation variable based on the lateral acceleration. The first observation variable is input into the state estimation algorithm, and a first steering angle estimate corresponding to the yaw rate is determined based on the first output of the algorithm. Similarly, the second observation variable is input into the state estimation algorithm, and a second steering angle estimate corresponding to the yaw rate is determined based on the second output of the algorithm. This ensures that the first observation variable matches the yaw rate, and the second observation variable matches the lateral acceleration. Furthermore, inputting the first and second observation variables into the state estimation algorithm and determining the first steering angle estimate corresponding to the yaw rate and the second steering angle estimate corresponding to the lateral acceleration based on the algorithm's output increases the sample size of the steering angle estimates. Additionally, determining the probability density value based on the difference between the steering angle estimate and the steering wheel angle value improves the accuracy of the probability density value with more steering angle estimate samples.

[0117] Optionally, before outputting the fault alarm signal, the method further includes:

[0118] Step 201: Update the preset fault counting parameters to obtain the updated fault counting parameters.

[0119] Step 104 may include the following steps:

[0120] Step 1041: If the updated fault count parameter value is not less than the preset count threshold, output the fault alarm signal.

[0121] In this embodiment of the application, the preset fault counting parameter is used to count the number of times the vehicle experiences a fault where the steering wheel angle and wheel angle deviate. The parameter value of the fault counting parameter can be initialized to 0. When the probability density value is less than the preset density threshold, the parameter value of the fault counting parameter can be updated by adding 1 to the current parameter value of the fault counting parameter to obtain the updated fault counting parameter.

[0122] In this embodiment, the preset counting threshold can be the upper limit of the number of times a vehicle is allowed to experience a deviation between the steering wheel angle and the wheel angle. The preset counting threshold can be set according to the actual application scenario. For example, the preset counting threshold can be set to 5 times. This is just an example and this embodiment does not limit it.

[0123] In this embodiment, if the updated fault counting parameter value is not less than a preset counting threshold, the preset fault status flag can be set to true, and the value of the fault status flag can be sent to the vehicle's fault alarm module via the vehicle's bus network. The fault alarm module then outputs a fault alarm signal to remind the driver to promptly address faults related to steering wheel angle and wheel angle deviation. The fault alarm signal can be displayed on the vehicle's central control screen, activated by the vehicle's lighting system, or output as a fault alarm voice message via the vehicle's voice system, etc. These are merely illustrative examples, and this embodiment does not impose any limitations on the method used.

[0124] In this embodiment, before outputting the fault alarm signal, the parameter values ​​of a preset fault counting parameter are updated to obtain an updated fault counting parameter. The fault alarm signal is output only if the updated fault counting parameter value is not less than a preset counting threshold. This allows for flexible adjustment of the fault alarm signal output using the preset fault counting parameter. Outputting the fault alarm signal only when the updated fault counting parameter value is not less than the preset counting threshold eliminates the influence of measurement errors in the steering speed signal and steering wheel angle on the probability density value, making the output fault alarm signal more accurate and improving the accuracy of the fault monitoring device in this embodiment to some extent.

[0125] Optionally, the preset density threshold can be obtained in the following way:

[0126] Step 301: When the steering wheel of the vehicle is at the zero point and the wheel angle of the vehicle is at a preset calibration angle, acquire the steering calibration speed signal and the steering wheel angle calibration value of the vehicle.

[0127] Step 302: Determine the steering angle calibration value of the vehicle based on the steering calibration speed signal and the state estimation algorithm.

[0128] Step 303: Determine the probability density calibration value based on the difference between the angle calibration value and the steering wheel angle calibration value.

[0129] Step 304: Determine the preset density threshold based on the probability density calibration value.

[0130] In this embodiment, for any vehicle, a preset density threshold can be determined in advance through whole-vehicle testing. The zero-point position of the vehicle's steering wheel represents the position when the steering wheel is not turning. The vehicle parameters can be adjusted to place the steering wheel at the zero-point position and set the wheel angles to preset calibration angles to simulate the steer-by-wire system state when the vehicle's tolerance loop and ball screw slip during whole-vehicle testing. The preset calibration angles can include multiple angles to the left and right, such as 0°, ±1°, ±3°, ±5°, ±7°, and ±10°, etc. These are merely illustrative examples and are not limited in this embodiment. Methods for adjusting vehicle parameters can be found in existing descriptions and are not limited in this embodiment.

[0131] In this embodiment, the steering calibration speed signal may include: steering speed signals corresponding to each preset calibration angle when the vehicle is in the zero position and the vehicle's wheel angles are at each preset calibration angle, such as yaw rate, lateral acceleration, and other steering speed signals corresponding to each preset calibration angle. The steering wheel angle calibration value may include: the measured values ​​of the steering wheel angle obtained by the steering angle sensor at the steering wheel of the vehicle when the vehicle is in the zero position and the vehicle's wheel angles are at each preset calibration angle.

[0132] In this embodiment of the application, during the vehicle testing, the vehicle's steering calibration speed signal can be acquired and input into a state estimation algorithm. The output of the state estimation algorithm is then determined as the vehicle's steering angle calibration value. For example, the steering calibration speed signal may include yaw rate calibration value and lateral acceleration calibration value. Further, based on the yaw rate calibration value, formula (6), and formula (7), the first steering angle calibration value corresponding to the yaw rate calibration value can be obtained. Also, based on the lateral acceleration calibration value, formula (6), and formula (8), the second steering angle calibration value corresponding to the lateral acceleration calibration value can be obtained. The first and second steering angle calibration values ​​are then determined as the vehicle's steering angle calibration value. The specific calculation process can be found in the relevant description in step 1022. It will not be repeated here.

[0133] In this embodiment of the application, during vehicle testing, a steering wheel angle calibration value can be obtained, and a probability density calibration value can be determined based on the difference between the two calibration values ​​and a preset probability density function. Specifically, the difference between the two calibration values ​​can be calculated, and then used as the input to the probability density function, with the output value of the probability density function determined as the probability density calibration value.

[0134] In this embodiment of the application, during the whole vehicle test, the probability density calibration value can include multiple values. For example, the probability density calibration values ​​corresponding to preset calibration angles of 0°, ±1°, ±3°, ±5°, ±7°, and ±10° can be used as a benchmark. Then, a preset density threshold can be determined based on the actual application scenario. For example, the probability density calibration value when the wheel rotation angle is 0° can be directly determined as the preset density threshold. This is merely an example, and this embodiment of the application does not impose any limitations on it.

[0135] In this embodiment of the application, when conducting a whole vehicle test, the measured parameters can be stored using the following whole vehicle test record table for the steer-by-wire system, as shown in Table 1:

[0136] Table 1 Vehicle Test Record of Steer-by-Wire System

[0137]

[0138] Among them, operating conditions 1 to n indicate that the vehicle's steering wheel is at the zero point position and the vehicle's wheel turning angle is a different preset calibration angle, which may include 0°, ±1°, ±3°, ±5°, ±7°, ±10°...±N°.

[0139] In this embodiment of the application, when the vehicle's steering wheel is at zero position and the vehicle's wheel angle is a preset calibration angle, the vehicle's steering calibration speed signal and steering wheel angle calibration value are obtained; the vehicle's angle calibration value is determined based on the steering calibration speed signal and the state estimation algorithm; a probability density calibration value is determined based on the difference between the angle calibration value and the steering wheel angle calibration value; and a preset density threshold is determined based on the probability density calibration value. In this way, since the steering calibration speed signal and steering wheel angle calibration value are obtained when the vehicle's steering wheel is at zero position and the vehicle's wheel angle is at a preset calibration angle, the steering calibration speed signal and steering wheel angle calibration value are matched with the zero position of the steering wheel and the wheel angle at the preset calibration angle. Furthermore, the vehicle's angle calibration value is determined based on the steering calibration speed signal and the state estimation algorithm, so that the angle calibration value matches the steering calibration speed signal. Therefore, the difference between the angle calibration value and the steering wheel angle calibration value can represent the deviation between the vehicle's steering wheel angle and wheel angle when the steering wheel is at zero position. Then, the probability density calibration value is determined based on the difference between the angle calibration value and the steering wheel angle calibration value, so that the probability density calibration value can characterize the deviation between the vehicle's steering wheel angle and wheel angle. In addition, the preset density threshold is determined based on the probability density calibration value, so that the preset density threshold matches the zero position of the steering wheel and the wheel angle at the preset calibration angle, which can improve the accuracy of the preset density threshold to a certain extent.

[0140] Figure 3 This is a flowchart illustrating another fault monitoring method provided in an embodiment of this application, such as... Figure 3 As shown, the vehicle's yaw rate and lateral acceleration signals are acquired. Based on the Kalman filter state estimation algorithm, a first steering angle estimate is obtained from the yaw rate, and a second steering angle estimate is obtained from the lateral acceleration. The vehicle's steering wheel angle is acquired, and angle processing is performed based on the first and second steering angle estimates and the steering wheel angle value. Specifically, the difference between the first and second steering angle estimates is determined as the first target difference, and the difference between the second and third steering angle estimates is determined as the second target difference. Further, a probability density value is determined based on the first and second target differences and a preset probability density function. Then, it is determined whether the probability density value is less than a preset density threshold. When the probability density value is less than the preset density threshold, the fault status flag is set to true, and the value of the fault status flag is sent to the vehicle's fault alarm module. The fault alarm module outputs a fault alarm signal to remind the driver to promptly check and address the steering wheel angle and wheel angle deviation faults.

[0141] This application provides a fault monitoring device, such as... Figure 4As shown, the device 40 includes:

[0142] The acquisition module 401 is used to acquire the vehicle's current steering speed signal and steering wheel angle value;

[0143] The first determining module 402 is used to determine the estimated turning angle of the vehicle based on the steering speed signal and a preset state estimation algorithm.

[0144] The second determining module 403 is used to determine a probability density value based on the difference between the estimated angle value and the steering wheel angle value.

[0145] The output module 404 is used to output a fault alarm signal when the probability density value is less than a preset density threshold; the preset density threshold represents the probability density value corresponding to the case where the deviation between the steering wheel angle and the wheel angle of the vehicle is not greater than a preset deviation threshold, and the fault alarm signal is used to represent the case where the deviation between the steering wheel angle and the wheel angle of the vehicle is greater than the preset deviation threshold.

[0146] Optionally, the steering speed signal includes the vehicle's yaw rate and lateral acceleration; the steering angle estimate includes a first steering angle estimate corresponding to the yaw rate and a second steering angle estimate corresponding to the lateral acceleration; the second determining module 403 is specifically used for:

[0147] The difference between the first estimated steering angle and the steering wheel angle value is determined as the first target difference.

[0148] The difference between the second estimated steering angle and the steering wheel angle value is determined as the second target difference.

[0149] The probability density value is determined based on the first target difference, the second target difference, and the preset probability density function.

[0150] Optionally, the second determining module 403 is further configured to:

[0151] Calculate the target mean based on the first target difference and the second target difference;

[0152] The first target difference, the second target difference, and the target mean are used as inputs to the probability density function, and the output value of the probability density function is determined as the probability density value.

[0153] Optionally, the first determining module 402 is specifically used for:

[0154] The first observation variable of the state estimation algorithm is determined based on the yaw rate, and the second observation variable of the state estimation algorithm is determined based on the lateral acceleration.

[0155] The first observed variable is input into the state estimation algorithm, and the first angle estimate corresponding to the yaw rate is determined according to the first output of the state estimation algorithm. The second observed variable is input into the state estimation algorithm, and the second angle estimate corresponding to the lateral acceleration is determined according to the second output of the state estimation algorithm.

[0156] Optionally, the device 40 further includes:

[0157] The update module is used to update the parameter value of the preset fault count parameter before the output module outputs the fault alarm signal, so as to obtain the updated fault count parameter.

[0158] The output module is specifically used for:

[0159] If the updated fault count parameter value is not less than the preset count threshold, the fault alarm signal is output.

[0160] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0161] The fault monitoring device and the fault monitoring method described in the foregoing embodiments have the same advantages over the prior art, and will not be repeated here.

[0162] Figure 5 This is a schematic diagram of another fault monitoring device provided in this application, such as... Figure 5As shown, the fault monitoring device 50 includes: a steering wheel angle prediction module 501, a tolerance slippage state processing module 502, and a fault alarm module 503. The steering wheel angle prediction module 501 includes a Kalman filter state estimation component 5011 based on yaw rate and a Kalman filter state estimation component 5012 based on lateral acceleration. Specifically, the Kalman filter state estimation component 5011 acquires the vehicle's yaw rate signal and determines a first angle estimate based on the yaw rate signal and the Kalman filter state estimation algorithm. The Kalman filter state estimation component 5012 acquires the vehicle's lateral acceleration signal and determines a second angle estimate based on the lateral acceleration signal and the Kalman filter state estimation algorithm. The tolerance slippage state processing module 502 includes an angle processing component 5021 and a fault state processing component 5022. The steering angle processing component 5021 is used to determine the difference between the first estimated steering angle and the steering wheel angle value as the first target difference, and the difference between the second estimated steering angle and the steering wheel angle value as the second target difference. The fault status processing component 5022 is used to determine a probability density value based on the first target difference, the second target difference, and a preset probability density function. Then, it determines whether the probability density value is less than a preset density threshold. When the probability density value is less than the preset density threshold, it sets the fault status flag to true and sends the value of the fault status flag to the vehicle's fault alarm module 503. The fault alarm module 503 includes a fault alarm component 5031. The fault alarm component 5031 receives the fault status flag and, when the fault status flag is true, calls the Application Programming Interface (API) to send a control signal to the fault alarm execution device to control the fault alarm execution device to output a fault alarm signal, thereby reminding the driver to promptly check and address any deviations in the steering wheel angle and wheel angle.

[0163] This application provides a vehicle including the fault monitoring device as described above, for performing the fault monitoring method as described above.

[0164] The vehicle described above has the same advantages over the prior art as the fault monitoring method described in the foregoing embodiments, and will not be repeated here.

[0165] This application provides a readable storage medium that, when the instructions in the storage medium are executed by the processor of an electronic device, enables the electronic device to perform the fault monitoring method described in the foregoing embodiments.

[0166] The readable storage medium has the same advantages over the prior art as the fault monitoring method described in the foregoing embodiments, and will not be repeated here.

[0167] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. The structure required to construct such a system is readily apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0168] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0169] Similarly, it should be understood that, in order to simplify the invention and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, this disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into this detailed description, wherein each claim itself is a separate embodiment of the invention.

[0170] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0171] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the sorting device according to the present invention. The present invention can also be implemented as a device or apparatus program for performing part or all of the methods described herein. Such a program implementing the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0172] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0173] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0174] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0175] It should be noted that the various data-related processes in the embodiments of this application are carried out in compliance with the relevant data protection laws and policies of the country where the location is located, and with the authorization granted by the owner of the corresponding device.

Claims

1. A fault monitoring method, characterized in that, The method includes: Obtain the vehicle's current steering speed signal and steering wheel angle value; Based on the steering speed signal and a preset state estimation algorithm, the estimated steering angle of the vehicle is determined; The probability density value is determined based on the difference between the estimated steering angle and the steering wheel angle value. When the probability density value is less than a preset density threshold, a fault alarm signal is output; the preset density threshold represents the probability density value corresponding to the condition that the deviation between the steering wheel angle and the wheel angle of the vehicle is not greater than a preset deviation threshold, and the fault alarm signal is used to indicate that the deviation between the steering wheel angle and the wheel angle of the vehicle is greater than the preset deviation threshold; wherein, the preset density threshold is obtained in the following way: When the vehicle's steering wheel is at the zero point and the vehicle's wheel angle is at a preset calibration angle, the vehicle's steering calibration speed signal and steering wheel angle calibration value are acquired. The steering angle calibration value of the vehicle is determined based on the steering calibration speed signal and the state estimation algorithm. The probability density calibration value is determined based on the difference between the angle calibration value and the steering wheel angle calibration value. The preset density threshold is determined based on the probability density calibration value.

2. The method according to claim 1, characterized in that, The steering speed signal includes the vehicle's yaw rate and lateral acceleration; the steering angle estimate includes a first steering angle estimate corresponding to the yaw rate and a second steering angle estimate corresponding to the lateral acceleration; determining the probability density value based on the difference between the steering angle estimate and the steering wheel angle value includes: The difference between the first estimated steering angle and the steering wheel angle value is determined as the first target difference. The difference between the second estimated steering angle and the steering wheel angle value is determined as the second target difference. The probability density value is determined based on the first target difference, the second target difference, and the preset probability density function.

3. The method according to claim 2, characterized in that, The step of determining the probability density value based on the first target difference, the second target difference, and a preset probability density function includes: Calculate the target mean based on the first target difference and the second target difference; The first target difference, the second target difference, and the target mean are used as inputs to the probability density function, and the output value of the probability density function is determined as the probability density value.

4. The method according to claim 2, characterized in that, The step of determining the estimated steering angle of the vehicle based on the steering speed signal and a preset state estimation algorithm includes: The first observation variable of the state estimation algorithm is determined based on the yaw rate, and the second observation variable of the state estimation algorithm is determined based on the lateral acceleration. The first observed variable is input into the state estimation algorithm, and the first angle estimate corresponding to the yaw rate is determined according to the first output of the state estimation algorithm. The second observed variable is input into the state estimation algorithm, and the second angle estimate corresponding to the lateral acceleration is determined according to the second output of the state estimation algorithm.

5. The method according to claim 1, characterized in that, Before outputting the fault alarm signal, the method further includes: The preset fault count parameters are updated to obtain the updated fault count parameters; The output fault alarm signal includes: If the updated fault counting parameter value is not less than the preset counting threshold, the fault alarm signal is output.

6. A fault monitoring device, characterized in that, The device includes: The acquisition module is used to acquire the vehicle's current steering speed signal and steering wheel angle value; The first determining module is used to determine the estimated steering angle of the vehicle based on the steering speed signal and a preset state estimation algorithm. The second determining module is used to determine the probability density value based on the difference between the estimated turning angle and the steering wheel turning angle value; An output module is used to output a fault alarm signal when the probability density value is less than a preset density threshold. The preset density threshold represents the probability density value corresponding to the condition that the deviation between the vehicle's steering wheel angle and wheel angle is not greater than a preset deviation threshold. The fault alarm signal is used to represent the condition that the deviation between the vehicle's steering wheel angle and wheel angle is greater than the preset deviation threshold. The preset density threshold is obtained as follows: when the vehicle's steering wheel is at zero and the vehicle's wheel angle is at a preset calibration angle, the vehicle's steering calibration speed signal and steering wheel angle calibration value are obtained; the vehicle's angle calibration value is determined based on the steering calibration speed signal and the state estimation algorithm; the probability density calibration value is determined based on the difference between the angle calibration value and the steering wheel angle calibration value; and the preset density threshold is determined based on the probability density calibration value.

7. The apparatus according to claim 6, characterized in that, The steering speed signal includes the vehicle's yaw rate and lateral acceleration; the steering angle estimate includes a first steering angle estimate corresponding to the yaw rate and a second steering angle estimate corresponding to the lateral acceleration; the second determining module is specifically used for: The difference between the first estimated steering angle and the steering wheel angle value is determined as the first target difference. The difference between the second estimated steering angle and the steering wheel angle value is determined as the second target difference. The probability density value is determined based on the first target difference, the second target difference, and the preset probability density function.

8. A vehicle, characterized in that, The vehicle is equipped with a fault monitoring device as described in claim 6 or 7, for performing the fault monitoring method as described in any one of claims 1-5.

9. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the fault monitoring method as described in any one of claims 1-5.

Images