Vehicle transverse response compensation method and device, electronic equipment and storage medium

Abstract

The invention discloses a vehicle transverse response compensation method and device, electronic equipment and a storage medium. The method comprises the steps that based on the longitudinal vehicle speed and the steering wheel rotation angle of a vehicle under the current working condition, an ideal reference model is used for obtaining the expected yaw velocity, based on the actual yaw velocity of the vehicle under the current working condition and the newly obtained expected yaw velocity, a compensation rotation angle is obtained, and the transverse response of the vehicle is compensated according to the compensation rotation angle. According to the method, the transverse response of the vehicle is compensated according to the actual state and the actual response quantity of the vehicle under the current working condition, the error between the actual response and the expected response can be more directly and accurately eliminated, and the response compensation efficiency and the compensation effect are improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent vehicle technology, and in particular to a vehicle lateral response compensation method, device, electronic device and storage medium. Background Art

[0002] During the vehicle assembly process, the misalignment between the gear and the rack can easily lead to the risk of lateral response of the vehicle, specifically, the inconsistency of left and right responses. At the same time, the various mechanisms of the steering system may be loosely assembled, resulting in steering clearance. After a long period of use, the parts will wear out and the steering clearance will increase. At this time, the inconsistency and insufficiency of the left and right responses of the vehicle will be further increased. For vehicles under the control of the ADAS system, the above phenomenon will reduce the control performance of the ADAS, thereby affecting the safety of the vehicle.

[0003] In the prior art, for the situation where the left and right responses of the vehicle are inconsistent or the lateral response is insufficient, solutions are generally proposed from the internal level of the steering system. For example, the compensation amount of the steering wheel angle is obtained by identifying the difference in left and right responses, steering clearance, etc. This method of response compensation based on the internal structure of the system is inefficient and the compensation effect is poor. Summary of the invention

[0004] Based on this, it is necessary to provide a vehicle lateral response compensation method, device, electronic device and storage medium to address the above technical problems, so as to improve the response compensation efficiency and compensation effect.

[0005] A vehicle lateral response compensation method, comprising:

[0006] Obtain the longitudinal speed, actual yaw rate and steering wheel angle of the vehicle under the current working condition;

[0007] calculating a desired yaw rate according to the longitudinal vehicle speed and the steering wheel angle based on an ideal reference model;

[0008] A compensation angle is determined according to the desired yaw rate and the actual yaw rate, so as to compensate for a lateral response of the vehicle according to the compensation angle.

[0009] In the embodiment of the present application, determining the compensation angle according to the expected yaw angular velocity and the actual yaw angular velocity includes:

[0010] Calculating a value of a sliding mode equivalent control term and a value of a sliding mode switching control term according to the desired yaw angular velocity and the actual yaw angular velocity;

[0011] The value of the sliding mode equivalent control item and the value of the sliding mode switching control item are summed, and the sum value obtained after the summation is determined as the compensation angle.

[0012] In the embodiment of the present application, the sliding mode equivalent control term is determined by the following process:

[0013] The difference between the actual yaw rate and the desired yaw rate is determined as a tracking error for tracking the desired yaw rate signal based on the sliding mode control algorithm;

[0014] The sliding mode equivalent control term is derived based on the tracking error, the sliding mode function and the actual yaw acceleration expression, wherein the actual yaw acceleration expression is determined based on a two-degree-of-freedom vehicle model.

[0015] In the embodiment of the present application, the calculating the expected yaw rate based on the ideal reference model according to the longitudinal vehicle speed and the steering wheel angle includes:

[0016] Based on the Kalman filter, the distance from the center of mass of the vehicle to the front axle and the distance from the center of mass of the vehicle to the rear axle under the current working condition are corrected to correct the ideal reference model;

[0017] The expected yaw rate is calculated according to the longitudinal vehicle speed and the steering wheel angle based on the modified ideal reference model.

[0018] In the embodiment of the present application, the method of correcting the distance from the center of mass of the vehicle to the front axle and the distance from the center of mass of the vehicle to the rear axle under the current working condition based on Kalman filtering to correct the ideal reference model includes:

[0019] Based on Kalman filtering, obtain an estimated value of the distance from the center of mass of the vehicle to the front axle at the current moment and an estimated value of the distance from the center of mass of the vehicle to the rear axle at the current moment;

[0020] The estimated value of the distance from the center of mass of the vehicle to the front axle is used as the value of the distance from the center of mass of the vehicle to the front axle in the ideal reference model, and the estimated value of the distance from the center of mass of the vehicle to the rear axle is used as the value of the distance from the center of mass of the vehicle to the rear axle in the ideal reference model.

[0021] In the embodiment of the present application, the Kalman filter is used to obtain an estimated value of the distance from the center of mass of the vehicle to the front axle at the current moment and an estimated value of the distance from the center of mass of the vehicle to the rear axle at the current moment, including:

[0022] Determining parameters of each matrix corresponding to the Kalman filter algorithm according to the road surface input model, the suspended mass motion equation corresponding to the vehicle, and the non-suspension mass motion equation corresponding to the vehicle, wherein the parameters include system state variables;

[0023] Execute a Kalman filter algorithm based on each matrix to obtain a current system state value estimated by the Kalman filter algorithm;

[0024] Calculating an estimated value of the vehicle suspension force at the current moment according to the system state value at the current moment estimated by the Kalman filter algorithm and the non-suspension mass motion equation;

[0025] According to the suspended mass motion equation and the estimated value of the vehicle suspension force at the current moment, an estimated value of the distance from the vehicle center of mass to the front axle and an estimated value of the distance from the vehicle center of mass to the rear axle are calculated.

[0026] In the embodiment of the present application, the ideal reference model is a two-degree-of-freedom vehicle model.

[0027] A vehicle lateral response compensation device, comprising:

[0028] An acquisition module is used to acquire the longitudinal speed, actual yaw rate and steering wheel angle of the vehicle under the current working condition;

[0029] a calculation module, configured to calculate a desired yaw rate according to the longitudinal vehicle speed and the steering wheel angle based on an ideal reference model;

[0030] The response compensation module is used to determine a compensation angle according to the desired yaw rate and the actual yaw rate, so as to compensate for the lateral response of the vehicle according to the compensation angle.

[0031] In the embodiment of the present application, the response compensation module is further used to:

[0032] Calculating a value of a sliding mode equivalent control term and a value of a sliding mode switching control term according to the desired yaw angular velocity and the actual yaw angular velocity;

[0033] The value of the sliding mode equivalent control item and the value of the sliding mode switching control item are summed, and the sum value obtained after the summation is determined as the compensation angle.

[0034] In the embodiment of the present application, the sliding mode equivalent control term is determined by the following process:

[0035] The difference between the actual yaw rate and the desired yaw rate is determined as a tracking error for tracking the desired yaw rate signal based on the sliding mode control algorithm;

[0036] The sliding mode equivalent control term is derived based on the tracking error, the sliding mode function and the actual yaw acceleration expression, wherein the actual yaw acceleration expression is determined based on a two-degree-of-freedom vehicle model.

[0037] In the embodiment of the present application, the calculation module is further used for:

[0038] Based on the Kalman filter, the distance from the center of mass of the vehicle to the front axle and the distance from the center of mass of the vehicle to the rear axle under the current working condition are corrected to correct the ideal reference model;

[0039] The expected yaw rate is calculated according to the longitudinal vehicle speed and the steering wheel angle based on the modified ideal reference model.

[0040] In the embodiment of the present application, the calculation module is further used for:

[0041] Based on Kalman filtering, obtain an estimated value of the distance from the center of mass of the vehicle to the front axle at the current moment and an estimated value of the distance from the center of mass of the vehicle to the rear axle at the current moment;

[0042] The estimated value of the distance from the center of mass of the vehicle to the front axle is used as the value of the distance from the center of mass of the vehicle to the front axle in the ideal reference model, and the estimated value of the distance from the center of mass of the vehicle to the rear axle is used as the value of the distance from the center of mass of the vehicle to the rear axle in the ideal reference model.

[0043] In the embodiment of the present application, the calculation module is further used for:

[0044] Determining parameters of each matrix corresponding to the Kalman filter algorithm according to the road surface input model, the suspended mass motion equation corresponding to the vehicle, and the non-suspension mass motion equation corresponding to the vehicle, wherein the parameters include system state variables;

[0045] Execute a Kalman filter algorithm based on each matrix to obtain a current system state value estimated by the Kalman filter algorithm;

[0046] Calculating an estimated value of the vehicle suspension force at the current moment according to the system state value at the current moment estimated by the Kalman filter algorithm and the non-suspension mass motion equation;

[0047] According to the suspended mass motion equation and the estimated value of the vehicle suspension force at the current moment, an estimated value of the distance from the vehicle center of mass to the front axle and an estimated value of the distance from the vehicle center of mass to the rear axle are calculated.

[0048] In the embodiment of the present application, the ideal reference model is a two-degree-of-freedom vehicle model.

[0049] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the vehicle lateral response compensation method as described above are implemented.

[0050] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the vehicle lateral response compensation method as described above.

[0051] In summary, the present application proposes a vehicle lateral response compensation method, device, electronic device and storage medium. The present application obtains the expected yaw rate based on the longitudinal speed and steering wheel angle of the vehicle under the current working condition using an ideal reference model, obtains the compensation angle based on the actual yaw rate of the vehicle under the current working condition and the newly obtained expected yaw rate, and compensates the vehicle lateral response according to the compensation angle. The present application compensates for the lateral response of the vehicle based on the actual state and actual response of the vehicle under the current working condition, which can more directly and accurately eliminate the error between the actual response and the expected response, and improve the response compensation efficiency and compensation effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0053] Figure 1 is a flow chart of a vehicle lateral response compensation method according to an exemplary embodiment of the present application;

[0054] Figure 2 is a flow chart of a vehicle lateral response compensation method according to another exemplary embodiment of the present application;

[0055] Figure 3 is a flow chart of a vehicle lateral response compensation method according to another exemplary embodiment of the present application;

[0056] Figure 4 is a flow chart of a vehicle lateral response compensation method according to another exemplary embodiment of the present application;

[0057] Figure 5 is a flow chart of a vehicle lateral response compensation method according to another exemplary embodiment of the present application;

[0058] Figure 6 is an overall flow chart of a vehicle lateral response compensation method according to an exemplary embodiment of the present application;

[0059] Figure 7 It is a flowchart of estimating the center of mass position based on Kalman filtering in a vehicle lateral response compensation method according to an exemplary embodiment of the present application;

[0060] Figure 8 is a block diagram of a vehicle lateral response compensation device according to an exemplary embodiment of the present application;

[0061] Fig. 9 It is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. The embodiments described by reference to the drawings are exemplary and are intended to be used to explain the present application, and cannot be understood as limiting the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0063] Figure 1 is a flow chart of a vehicle lateral response compensation method according to an exemplary embodiment of the present application. Figure 1 As shown, the vehicle lateral response compensation method includes the following steps:

[0064] S101, obtaining the longitudinal speed, actual yaw angular velocity and steering wheel angle of the vehicle under the current working condition.

[0065] Under the current working conditions, obtain the vehicle's longitudinal speed, actual yaw rate and steering wheel angle at the current moment.

[0066] In some embodiments, a longitudinal speed signal, an actual yaw rate signal, and a steering wheel angle signal of the vehicle may be acquired through sensors.

[0067] For vehicles equipped with an ADAS system, a steering wheel angle signal can be obtained from the ADAS system.

[0068] S102, based on the ideal reference model, calculating the expected yaw rate according to the longitudinal vehicle speed and the steering wheel angle.

[0069] In some embodiments, the ideal reference model is a two-degree-of-freedom vehicle model, and the calculation formula for the expected yaw angular velocity is determined based on the two-degree-of-freedom vehicle model.

[0070] The differential equation of motion for a two-degree-of-freedom vehicle is given by:

[0071]

[0072]

[0073] Where m is the vehicle mass; v x and v y are the longitudinal speed and lateral speed of the vehicle respectively; l f and l rare the distances from the vehicle's center of mass to the front and rear axles respectively; C f and C r are the cornering stiffness of the front and rear wheels of the vehicle respectively; I z is the vehicle's moment of inertia; ω is the vehicle's yaw rate; δ f is the front wheel turning angle; β is the center of mass side slip angle, where β = v y / v x .

[0074] In this application, the steering wheel angle output by the ADAS system is calculated through the steering gear ratio to obtain the front wheel angle, which is expressed as δ a , the final compensation angle is expressed as Δδ, then the front wheel angle δ in the above two-degree-of-freedom vehicle motion differential equation is f It can be understood as the desired front wheel steering angle for lateral response:

[0075] δ f =δ a +Δδ (3)

[0076] From equations (1) and (2), the calculation formula for the desired yaw rate can be obtained as follows:

[0077]

[0078] Where: K is the vehicle stability factor; L is the vehicle wheelbase, L = l f +l r .

[0079] In the specific implementation, the parameter values ​​other than the longitudinal vehicle speed and the front wheel turning angle under the current working condition in the calculation formula of the expected yaw rate can be obtained in advance, such as the vehicle mass m; the distance from the center of mass of the vehicle to the front l f and the distance from the vehicle's center of mass to the rear axle l r ; Vehicle front wheel cornering stiffness C f and the vehicle rear wheel cornering stiffness C r wait.

[0080] S103: determining a compensation angle according to the expected yaw rate and the actual yaw rate, so as to compensate for the lateral response of the vehicle according to the compensation angle.

[0081] In some embodiments, the compensation angle can be determined based on the expected yaw rate and the actual yaw rate based on a sliding mode controller or a sliding mode control algorithm. The lateral response of the vehicle is compensated according to the compensation angle, such as compensating the actual front wheel angle under the current working condition by formula (3) to obtain the expected front wheel angle, and the front wheel rotation is controlled based on the expected front wheel angle to eliminate the error between the actual response and the expected response, effectively eliminate the insufficiency and inconsistency of the left and right responses of the vehicle, improve the accuracy of the ADAS lateral control, and ensure the safety of vehicle driving.

[0082] In summary, the vehicle lateral response compensation method provided in the embodiment of the present application is based on the longitudinal speed and steering wheel angle of the vehicle under the current working condition, uses the ideal reference model to obtain the expected yaw rate, obtains the compensation angle based on the actual yaw rate of the vehicle under the current working condition and the newly obtained expected yaw rate, and compensates the vehicle lateral response according to the compensation angle. The present application compensates for the lateral response of the vehicle based on the actual state and actual response of the vehicle under the current working condition, which can more directly and accurately eliminate the error between the actual response and the expected response, and provides a convenient and efficient lateral response compensation method.

[0083] Based on the above embodiments, Figure 2 As shown, in the above step S103 in the embodiment of the present application, “determining a compensation angle according to the expected yaw rate and the actual yaw rate, so as to compensate the lateral response of the vehicle according to the compensation angle” further includes the following steps:

[0084] S201, calculating the value of the sliding mode equivalent control item and the value of the sliding mode switching control item according to the desired yaw angular velocity and the actual yaw angular velocity.

[0085] In some embodiments, based on the sliding mode control algorithm, an expression corresponding to the sliding mode equivalent control term is determined, and the sliding mode control term can be understood as the control quantity of the sliding mode equivalent control law.

[0086] In some embodiments, the sliding mode equivalent control term may be determined by the following process:

[0087] The difference between the actual yaw rate and the desired yaw rate is determined as the tracking error for tracking the desired yaw rate signal based on the sliding mode control algorithm; the sliding mode equivalent control term is derived based on the tracking error, the sliding mode function and the actual yaw acceleration expression, wherein the actual yaw rate expression is determined based on a two-degree-of-freedom vehicle model.

[0088] For example, suppose the tracking error is:

[0089] e=ω-ω des (5) Based on the above formula (2), the expression of yaw angular acceleration can be deduced as:

[0090]

[0091] Using the sliding mode control method, the sliding mode function is designed as:

[0092]

[0093] Where c is the control gain of the sliding mode controller, satisfying c>0.

[0094] set up From equation (3), equation (5) and equation (6), we can find the derivatives on both sides of the equation:

[0095]

[0096] Select The control quantity of the sliding mode equivalent control rate (i.e., the sliding mode equivalent control term) u can be obtained 1 as follows:

[0097]

[0098] In the embodiment of the present application, the expression based on the sliding mode equivalent control term, that is, equation (9), is calculated according to the actual yaw angular velocity ω under the current working condition and the obtained desired yaw angular velocity ω. des , calculate the value of the sliding mode equivalent control term. Among them, the values ​​of other parameters except the actual yaw rate and the expected yaw rate in equation (9) can be obtained in advance.

[0099] In practice, vehicles are inevitably subject to external interference during actual operation, and there is uncertainty in the system. This application adds a switching control amount on the basis of the control amount, and combines the two as the total control output of the sliding mode controller. The switching control amount is the above-mentioned sliding mode switching control item, which can make the sliding mode control system move to the "sliding mode surface" under the action of the switching function to ensure the stability of the system.

[0100] In some embodiments, to obtain the switching control quantity (ie, the sliding mode switching control term) u 2 , this application selects the sigmoid function as the switching function, then we can get

[0101] u 2 =ηsig(s) (10)

[0102]

[0103] Where η is the control parameter.

[0104] Based on equations (5), (7), (10) and (11), the value of the sliding mode switching control term is obtained according to the actual yaw rate and the expected yaw rate.

[0105] S202, summing the value of the sliding mode equivalent control item and the value of the sliding mode switching control item, and determining the sum value obtained after the summing as the compensation angle.

[0106] In the embodiment of the present application, a sliding mode switching control term is added on the basis of the sliding mode equivalent control term, the value of the equivalent control term and the value of the sliding mode switching control term are summed, and the obtained sum is used as the total control output u output by the sliding mode controller or the sliding mode control algorithm, which is used as the compensation angle. 1 +u 2 The value of is used as the new Δδ: u=Δδ=u 1 +u 2 .

[0107] This application generates a compensation angle based on the actual yaw rate response through a sliding mode control algorithm to compensate for the vehicle response. A sliding mode controller is used to track the desired yaw rate, and the sliding mode equivalent control term and the sliding mode switching control term are combined as the total output of the sliding mode control. It has good robustness to the uncertainty of the system model and external disturbances, and can respond to response compensation under different working conditions and improve the effect of lateral response compensation.

[0108] Based on the above embodiments, Figure 3 As shown, the above step S102 of "calculating the expected yaw rate based on the ideal reference model according to the longitudinal vehicle speed and the steering wheel angle" further includes the following steps:

[0109] S301, based on Kalman filtering, correct the distance from the center of mass of the vehicle to the front axle and the distance from the center of mass of the vehicle to the rear axle under the current working condition to correct the ideal reference model.

[0110] In practice, during vehicle operation, different driving conditions will cause changes in parameters such as the center of mass position of the vehicle. Therefore, in order to obtain accurate compensation angles and reduce response compensation errors, it is necessary to correct the parameters related to the center of mass position used in the ideal reference model, such as the distance l from the center of mass of the vehicle to the front axle. f and the distance from the vehicle's center of mass to the rear axle l r .

[0111] In the embodiment of the present application, Kalman filtering is used to calculate the distance l from the center of mass of the vehicle to the front axle. f and the distance from the vehicle's center of mass to the rear axle l r An estimate is made, and the ideal reference model is corrected based on the estimated value of the distance from the center of mass to the front and rear axles, so as to determine the expected yaw rate based on the actual operating state of the vehicle, enhance the accuracy of the expected yaw rate, and subsequently obtain an accurate compensation angle, thereby reducing the response compensation error and improving the effect of response compensation.

[0112] S302, based on the corrected ideal reference model, calculating the expected yaw rate according to the longitudinal vehicle speed and the steering wheel angle.

[0113] After the parameters of the ideal reference model are corrected, the longitudinal vehicle speed and the front wheel angle corresponding to the steering wheel angle are input into the corrected ideal reference model to obtain the desired yaw rate by calculation. The specific calculation process is the same as the calculation process of the desired yaw rate in the above embodiment, which will not be repeated here.

[0114] The Kalman filter algorithm used in this application has the advantages of less calculation, fast operation speed, high accuracy, etc., and is easy to apply in engineering.

[0115] The present application uses Kalman filtering to estimate the center of mass position of the vehicle during operation in real time, thereby making real-time corrections to the ideal reference model, reducing the impact of model errors on the expected yaw angular velocity and compensation angle, eliminating the errors between the steady-state characteristics and dynamic characteristics of the vehicle, making the control more precise, improving the accuracy of response compensation, and enhancing the response compensation effect.

[0116] Based on the above embodiments, Figure 4 As shown, the above step S301 of "correcting the distance from the center of mass of the vehicle to the front axle and the distance from the center of mass of the vehicle to the rear axle under the current working condition based on the Kalman filter to correct the ideal reference model" further includes the following steps:

[0117] S401, based on Kalman filtering, obtaining an estimated value of the distance from the center of mass of the vehicle to the front axle at the current moment and an estimated value of the distance from the center of mass of the vehicle to the rear axle at the current moment.

[0118] S402, using the estimated value of the distance from the center of mass of the vehicle to the front axle as the value of the distance from the center of mass of the vehicle to the front axle in the ideal reference model, and using the estimated value of the distance from the center of mass of the vehicle to the rear axle as the value of the distance from the center of mass of the vehicle to the rear axle in the ideal reference model.

[0119] The embodiment of the present application designs a Kalman filter and uses the Kalman filter algorithm corresponding to the Kalman filter to estimate the system state value at each moment, so as to obtain the estimated value of the distance from the center of mass of the vehicle to the front axle and the estimated value of the distance from the center of mass of the vehicle to the rear axle at the current moment.

[0120] In some embodiments, the ideal reference model can be modified by using the estimated value of the distance from the center of mass of the vehicle to the front and rear axles at the current moment as the distance from the center of mass of the vehicle to the front and rear axles in the ideal reference model. f and l r The values ​​of these two parameters are implemented.

[0121] Based on the above embodiments, Figure 5As shown, the above step S401 "obtaining an estimated value of the distance from the center of mass of the vehicle to the front axle and an estimated value of the distance from the center of mass of the vehicle to the rear axle at the current moment based on Kalman filtering" further includes the following steps:

[0122] S501, determining parameters of each matrix corresponding to the Kalman filter algorithm according to a road surface input model, a suspended mass motion equation corresponding to the vehicle, and a non-suspension mass motion equation corresponding to the vehicle, wherein the parameters include system state variables.

[0123] Before designing the Kalman filter, it is necessary to establish a vehicle vertical motion model. First, make the following assumptions about the vehicle vertical motion:

[0124] 1) The parameters of each suspension of the vehicle and the road input are consistent;

[0125] 2) The vehicle rolls slightly and the longitudinal speed does not change much.

[0126] When establishing a vertical motion model for a vehicle, it is necessary to perform dynamic analysis on the suspended mass and the non-suspended mass separately. The suspended mass motion equation is expressed as:

[0127]

[0128]

[0129] Among them, m s is the vehicle suspension mass, z c is the vertical displacement of the center of mass, F f is the sum of the left and right suspension forces of the front axle, F r is the sum of the left and right suspension forces of the rear axle; I y is the moment of inertia of the center of mass around the y-axis; φ is the vehicle pitch angle; h p is the distance from the center of mass to the pitch center; a x is the vehicle longitudinal acceleration.

[0130] The equation of motion for the unsprung mass is expressed as:

[0131]

[0132] in

[0133]

[0134] In the formula, m fj and m rj are the unsprung masses of the front and rear axles, respectively; z ufj and z urj are the vertical displacements of the non-suspension masses of the front and rear axles, respectively; z fj and z rj are the road excitation input;sfj and z srj are the displacements of the upper support points of the front and rear axle suspensions respectively; k sfj , k srj 、c sfj With c srj They are

[0135] Front and rear axle suspension stiffness and damping.

[0136] The following is a road input model:

[0137]

[0138] In the formula, f 0 is the lower cutoff frequency; G 0 is the road roughness coefficient; w i is Gaussian white noise with zero mean; v is the vehicle speed.

[0139] In some embodiments, based on equations (13) to (17), the parameters of each matrix corresponding to the Kalman filter algorithm are determined, such as selecting the system state variable as System Output System control input u=[0,0,a x ] T .

[0140] The state space expression of the system equation is:

[0141]

[0142] Combining equations (13) to (18), we can obtain:

[0143]

[0144]

[0145]

[0146]

[0147] S502, executing a Kalman filter algorithm based on each matrix to obtain a current system state value estimated by the Kalman filter algorithm.

[0148] Using the Euler method to discretize equation (18) we can get

[0149]

[0150] The application of Kalman filtering requires that the process noise w(k) and the measurement noise v(k) are zero-mean Gaussian white noise, and there is no correlation between w(k) and v(k), then

[0151]

[0152]

[0153] E[w(k)v T (k)]=0 (22)

[0154] Where Q(k) and R(k) are the covariance matrices of process noise and observation noise, respectively.

[0155] Assuming that the current state of the system is k, the Kalman filter algorithm is mainly divided into the following steps:

[0156] 1) Forecast and estimation

[0157] x(k|k-1)=A(k)x(k-1|k-1)+B(k)u(k-1) (23)

[0158] 2) Update the prediction estimate covariance matrix

[0159] P(k|k-1)=A(k)P(k-1|k-1)A T (k)+Q(k) (24)

[0160] 3) Kalman filter gain matrix

[0161]

[0162] 4) Kalman filter estimation

[0163] x(k|k)=x(k|k-1)+K(k)(y(k)-C(k)x(k|k-1) (26)

[0164] 5) Kalman filter estimation covariance matrix

[0165] P(k|k)=(IK(k)C(k))P(k|k-1) (27)

[0166] By inputting the system's initial values ​​x(0) and P(0), the Kalman filter algorithm can obtain the system's state value at each moment, that is, it can estimate the system's state value at the current moment.

[0167] S503, calculating an estimated value of the vehicle suspension force at the current moment according to the system state value at the current moment estimated by the Kalman filter algorithm and the motion equation of the non-suspension mass.

[0168] Based on the current system state value, combined with the above formula (16), the estimated value of the vehicle suspension force at the current moment can be obtained. The estimated value of the vehicle suspension force can be specifically the estimated value of the front axle right suspension force Ffr , estimated value of the front axle left suspension force F fl , estimated value of rear axle right suspension force F rr , estimated value of rear axle left suspension force F rl .

[0169] S504, calculating an estimated value of the distance from the center of mass of the vehicle to the front axle and an estimated value of the distance from the center of mass of the vehicle to the rear axle according to the suspended mass motion equation and the estimated value of the vehicle suspension force at the current moment.

[0170] In some embodiments, based on the estimated value of the vehicle suspension force: the estimated value of the front axle right suspension force F fr , estimated value of the front axle left suspension force F fl , estimated value of rear axle right suspension force F rr , estimated value of rear axle left suspension force F rl , and the sum of the left and right suspension forces of the front axle is obtained as F f , the sum of the rear axle left and right suspension forces F r .

[0171] The sum of the front axle left and right suspension forces F f , the sum of the rear axle left and right suspension forces F r Substituting into equation (14) in the equation of motion of the suspended mass, the distance l from the center of mass of the vehicle to the front axle is calculated f The estimated value of and the distance from the vehicle center of mass to the rear axle l r The estimated value of .

[0172] In order to clearly explain the vehicle lateral response compensation method proposed in this application, Figure 6-7 An example description of the overall process is given as follows: Figure 6 As shown, the ideal reference model receives the longitudinal vehicle speed signal and the actual yaw rate signal from the sensor, converts the steering wheel angle signal obtained from the ADAS control module in the ADAS system into the front wheel angle through the steering gear ratio, and inputs the front wheel angle into the ideal reference model, and generates the expected yaw rate through calculation; wherein, before calculating the expected yaw rate, considering that the center of mass position of the vehicle will change during operation, thereby causing errors in the ideal reference model, the present application designs a center of mass position estimation module based on Kalman filtering, estimates the center of mass position of the vehicle during operation, corrects the ideal reference model in real time, and outputs a suitable expected yaw rate; the difference between the expected and actual yaw rates are respectively input into the sliding mode controller module, and the sliding mode control algorithm generates a compensation angle based on the yaw rate response to compensate for the vehicle response. wherein, as Figure 7As shown, the center of mass position estimation module based on Kalman filtering estimates the center of mass position at each moment in real time through the following process: the distance from the center of mass of the vehicle to the front and rear axles in the stationary state is provided to the Kalman filter in the center of mass position estimation module based on Kalman filtering, so that the Kalman filter estimates the system state value at each moment, and at the current moment, the estimated system state value is used to calculate the sum of the left and right suspension forces of the front axle at the current moment according to formula (16): fk , the sum of the left and right suspension forces of the rear axle F rk , according to the sum of the left and right suspension forces of the front axle at the current moment F fk , the sum of the left and right suspension forces of the rear axle F rk , equation (14) in the suspended mass motion equation, and the parameter values ​​in the suspended mass motion equation except the distance from the vehicle center of mass to the front axle and the distance from the vehicle center of mass to the rear axle (such as longitudinal acceleration and pitch angular acceleration) to calculate the distance l from the vehicle center of mass to the front axle at the current moment fk and the distance from the vehicle's center of mass to the rear axle l rk .

[0173] In summary, the vehicle lateral response compensation method provided in the embodiment of the present application is based on the longitudinal speed and steering wheel angle of the vehicle under the current working condition, and uses the ideal reference model to obtain the expected yaw rate, and based on the actual yaw rate of the vehicle under the current working condition and the newly obtained expected yaw rate, obtains the compensation angle, and compensates the vehicle lateral response according to the compensation angle. The present application compensates for the lateral response of the vehicle based on the actual state and actual response of the vehicle under the current working condition, and can more directly and accurately eliminate the error between the actual response and the expected response. The present application considers the error of the ideal reference model and proposes a center of mass position estimation algorithm based on Kalman filtering to correct the ideal model, so that the lateral response compensation method has good adaptability under different working conditions. At the same time, the vehicle lateral response compensation method proposed in the present application can achieve the expected response of the vehicle without replacing the system structure components, without re-disassembling and assembling the components, and without changing the ADAS control logic, saving costs and reducing development time, and providing a convenient and efficient lateral response compensation method.

[0174] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0175] Figure 8 is a block diagram of a vehicle lateral response compensation device according to an exemplary embodiment of the present application. Figure 8 As shown, the device 800 includes: an acquisition module 801, a calculation module 802 and a response compensation module 803.

[0176] An acquisition module 801 is used to acquire the longitudinal speed, actual yaw rate and steering wheel angle of the vehicle under the current working condition;

[0177] A calculation module 802, configured to calculate an expected yaw rate according to the longitudinal vehicle speed and the steering wheel angle based on an ideal reference model;

[0178] The response compensation module 803 is used to determine a compensation angle according to the desired yaw rate and the actual yaw rate, so as to compensate for the lateral response of the vehicle according to the compensation angle.

[0179] In the embodiment of the present application, the response compensation module is further used to:

[0180] Calculating a value of a sliding mode equivalent control term and a value of a sliding mode switching control term according to the desired yaw angular velocity and the actual yaw angular velocity;

[0181] The value of the sliding mode equivalent control item and the value of the sliding mode switching control item are summed, and the sum value obtained after the summation is determined as the compensation angle.

[0182] In the embodiment of the present application, the sliding mode equivalent control term is determined by the following process:

[0183] The difference between the actual yaw rate and the desired yaw rate is determined as a tracking error for tracking the desired yaw rate signal based on the sliding mode control algorithm;

[0184] The sliding mode equivalent control term is derived based on the tracking error, the sliding mode function and the actual yaw acceleration expression, wherein the actual yaw acceleration expression is determined based on a two-degree-of-freedom vehicle model.

[0185] In the embodiment of the present application, the calculation module is further used for:

[0186] Based on the Kalman filter, the distance from the center of mass of the vehicle to the front axle and the distance from the center of mass of the vehicle to the rear axle under the current working condition are corrected to correct the ideal reference model;

[0187] The expected yaw rate is calculated according to the longitudinal vehicle speed and the steering wheel angle based on the modified ideal reference model.

[0188] In the embodiment of the present application, the calculation module is further used for:

[0189] Based on Kalman filtering, obtain an estimated value of the distance from the center of mass of the vehicle to the front axle at the current moment and an estimated value of the distance from the center of mass of the vehicle to the rear axle at the current moment;

[0190] The estimated value of the distance from the center of mass of the vehicle to the front axle is used as the value of the distance from the center of mass of the vehicle to the front axle in the ideal reference model, and the estimated value of the distance from the center of mass of the vehicle to the rear axle is used as the value of the distance from the center of mass of the vehicle to the rear axle in the ideal reference model.

[0191] In the embodiment of the present application, the calculation module is further used for:

[0192] Determining parameters of each matrix corresponding to the Kalman filter algorithm according to the road surface input model, the suspended mass motion equation corresponding to the vehicle, and the non-suspension mass motion equation corresponding to the vehicle, wherein the parameters include system state variables;

[0193] Execute a Kalman filter algorithm based on each matrix to obtain a current system state value estimated by the Kalman filter algorithm;

[0194] Calculating an estimated value of the vehicle suspension force at the current moment according to the system state value at the current moment estimated by the Kalman filter algorithm and the non-suspension mass motion equation;

[0195] According to the suspended mass motion equation and the estimated value of the vehicle suspension force at the current moment, an estimated value of the distance from the vehicle center of mass to the front axle and an estimated value of the distance from the vehicle center of mass to the rear axle are calculated.

[0196] In the embodiment of the present application, the ideal reference model is a two-degree-of-freedom vehicle model.

[0197] It should be noted that the above explanation of the embodiment of the vehicle lateral response compensation method is also applicable to the vehicle lateral response compensation device of the embodiment of the present application, and the specific process will not be repeated here.

[0198] Each module in the above-mentioned vehicle lateral response compensation device can be implemented in whole or in part by software, hardware or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0199] In summary, the vehicle lateral response compensation device provided in the embodiment of the present application obtains the expected yaw rate using the ideal reference model based on the longitudinal speed and steering wheel angle of the vehicle under the current working condition, obtains the compensation angle based on the actual yaw rate of the vehicle under the current working condition and the newly obtained expected yaw rate, and compensates the vehicle lateral response according to the compensation angle. The present application compensates for the lateral response of the vehicle based on the actual state and actual response of the vehicle under the current working condition, and can eliminate the error between the actual response and the expected response more directly and accurately. The present application considers the error of the ideal reference model and proposes a center of mass position estimation algorithm based on Kalman filtering to correct the ideal model, so that the lateral response compensation method has good adaptability under different working conditions. At the same time, the vehicle lateral response compensation device proposed in the present application can achieve the expected response of the vehicle without replacing the system structural components, without re-disassembling and assembling the components, and without changing the ADAS control logic, saving costs and reducing development time, and providing a convenient and efficient lateral response compensation device.

[0200] In order to implement the above embodiment, the present application embodiment also proposes an electronic device 900, such as Fig. 9 As shown, the electronic device 900 may specifically include: a memory 901, a processor 902, and a computer program stored in the memory 901 and executable on the processor 902. When the processor 902 executes the program, the steps of the vehicle lateral response compensation method shown in the above embodiment are implemented.

[0201] In summary, the present application proposes an electronic device, which uses an ideal reference model to obtain an expected yaw rate based on the longitudinal speed and steering wheel angle of the vehicle under the current working condition, obtains a compensation angle based on the actual yaw rate of the vehicle under the current working condition and the newly obtained expected yaw rate, and compensates the lateral response of the vehicle according to the compensation angle. The present application compensates for the lateral response of the vehicle based on the actual state and actual response of the vehicle under the current working condition, and can more directly and accurately eliminate the error between the actual response and the expected response. The present application takes into account the error of the ideal reference model and proposes a center of mass position estimation algorithm based on Kalman filtering to correct the ideal model, so that the lateral response compensation method has good adaptability under different working conditions. At the same time, the present application can achieve the expected response of the vehicle without replacing the system structure components, disassembling and assembling the components, and changing the ADAS control logic, saving costs and reducing development time.

[0202] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0203] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0204] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A vehicle lateral response compensation method, characterized in that: include: Obtain the longitudinal speed, actual yaw rate and steering wheel angle of the vehicle under the current working condition; calculating a desired yaw rate according to the longitudinal vehicle speed and the steering wheel angle based on an ideal reference model; A compensation angle is determined according to the desired yaw rate and the actual yaw rate, so as to compensate for a lateral response of the vehicle according to the compensation angle.

2. The method according to claim 1, characterized in that The step of determining a compensation angle according to the desired yaw angular velocity and the actual yaw angular velocity comprises: Calculating a value of a sliding mode equivalent control term and a value of a sliding mode switching control term according to the desired yaw angular velocity and the actual yaw angular velocity; The value of the sliding mode equivalent control item and the value of the sliding mode switching control item are summed, and the sum value obtained after the summation is determined as the compensation angle.

3. The method according to claim 2, characterized in that The sliding mode equivalent control term is determined by the following process: The difference between the actual yaw rate and the desired yaw rate is determined as a tracking error for tracking the desired yaw rate signal based on the sliding mode control algorithm; The sliding mode equivalent control term is derived based on the tracking error, the sliding mode function and the actual yaw angular acceleration expression.

4. The method according to claim 1, characterized in that: The calculating the expected yaw rate based on the ideal reference model according to the longitudinal vehicle speed and the steering wheel angle includes: Based on the Kalman filter, the distance from the center of mass of the vehicle to the front axle and the distance from the center of mass of the vehicle to the rear axle under the current working condition are corrected to correct the ideal reference model; The expected yaw rate is calculated according to the longitudinal vehicle speed and the steering wheel angle based on the modified ideal reference model.

5. The method according to claim 4, characterized in that The method of correcting the distance from the center of mass of the vehicle to the front axle and the distance from the center of mass of the vehicle to the rear axle under the current working condition based on Kalman filtering to correct the ideal reference model includes: Based on Kalman filtering, obtain an estimated value of the distance from the center of mass of the vehicle to the front axle at the current moment and an estimated value of the distance from the center of mass of the vehicle to the rear axle at the current moment; The estimated value of the distance from the center of mass of the vehicle to the front axle is used as the value of the distance from the center of mass of the vehicle to the front axle in the ideal reference model, and the estimated value of the distance from the center of mass of the vehicle to the rear axle is used as the value of the distance from the center of mass of the vehicle to the rear axle in the ideal reference model.

6. The method according to claim 5, characterized in that The method of obtaining an estimated value of the distance from the center of mass of the vehicle to the front axle and an estimated value of the distance from the center of mass of the vehicle to the rear axle at the current moment based on Kalman filtering includes: Determining parameters of each matrix corresponding to the Kalman filter algorithm according to the road surface input model, the suspended mass motion equation corresponding to the vehicle, and the non-suspension mass motion equation corresponding to the vehicle, wherein the parameters include system state variables; Execute a Kalman filter algorithm based on each matrix to obtain a current system state value estimated by the Kalman filter algorithm; Calculating an estimated value of the vehicle suspension force at the current moment according to the system state value at the current moment estimated by the Kalman filter algorithm and the non-suspension mass motion equation; According to the suspended mass motion equation and the estimated value of the vehicle suspension force at the current moment, an estimated value of the distance from the vehicle center of mass to the front axle and an estimated value of the distance from the vehicle center of mass to the rear axle are calculated.

7. The method according to claim 1, characterized in that The ideal reference model is a two-degree-of-freedom vehicle model.

8. A vehicle lateral response compensation device, characterized in that: include: An acquisition module is used to acquire the longitudinal speed, actual yaw rate and steering wheel angle of the vehicle under the current working condition; a calculation module, configured to calculate a desired yaw rate according to the longitudinal vehicle speed and the steering wheel angle based on an ideal reference model; The response compensation module is used to determine a compensation angle according to the desired yaw rate and the actual yaw rate, so as to compensate for the lateral response of the vehicle according to the compensation angle.

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

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the vehicle lateral response compensation method as described in any one of claims 1 to 7 are implemented.

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