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

Abstract

The application discloses a vehicle lateral response compensation method and device, electronic equipment and a storage medium. The method comprises the following steps: based on the longitudinal vehicle speed and the steering wheel rotation angle of the vehicle under the current working condition, the expected yaw rate is obtained by using an ideal reference model; based on the actual yaw rate of the vehicle under the current working condition and the newly obtained expected yaw rate, the compensation rotation angle is obtained; and the lateral response of the vehicle is compensated according to the compensation rotation angle. The application compensates the lateral response of the vehicle from the actual state and the actual response of the vehicle under the current working condition, can more directly and accurately eliminate the error between the actual response and the expected response, and improves the response compensation efficiency and the compensation effect.

Description

Technical Field

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

[0002] During vehicle assembly, misalignment between gears and racks can lead to risks in lateral response, specifically inconsistent left and right responses. Simultaneously, loose assembly may exist between various components of the steering system, resulting in steering play. Over time, wear and tear on these components increases steering play, further exacerbating the inconsistency and inadequacy in left and right responses. For vehicles controlled by ADAS systems, these issues reduce the performance of ADAS, thus impacting vehicle safety.

[0003] In existing technologies, solutions for inconsistent left and right responses or insufficient lateral response of vehicles are generally proposed from the perspective of the internal steering system. For example, the amount of compensation for steering wheel angle is obtained by identifying the difference in left and right responses and steering clearance. This method of response compensation based on the internal structure of the system is inefficient and has poor compensation effect. Summary of the Invention

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

[0005] A method for compensating for the lateral response of a vehicle includes:

[0006] Obtain the vehicle's longitudinal speed, actual yaw rate, and steering wheel angle under the current operating conditions;

[0007] Based on an ideal reference model, the desired yaw rate is calculated according to the longitudinal vehicle speed and the steering wheel angle.

[0008] Based on the desired yaw rate and the actual yaw rate, a compensation angle is determined to compensate for the vehicle's lateral response.

[0009] In this embodiment of the application, determining the compensation angle based on the desired yaw rate and the actual yaw rate includes:

[0010] Based on the desired yaw rate and the actual yaw rate, calculate the values ​​of the sliding mode equivalent control term and the sliding mode switching control term;

[0011] The values ​​of the sliding mode equivalent control term and the sliding mode switching control term are summed, and the sum is determined as the compensation angle.

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

[0013] 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.

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

[0015] In this embodiment of the application, the step of calculating the desired yaw rate based on the longitudinal vehicle speed and the steering wheel angle using an ideal reference model includes:

[0016] The distances from the vehicle's center of gravity to the front axle and the distances from the vehicle's center of gravity to the rear axle under the current operating conditions are corrected using Kalman filtering to correct the ideal reference model.

[0017] Based on the corrected ideal reference model, the desired yaw rate is calculated according to the longitudinal vehicle speed and the steering wheel angle.

[0018] In this embodiment of the application, the step of correcting the distance from the vehicle's center of gravity to the front axle and the distance from the vehicle's center of gravity to the rear axle under the current operating condition based on Kalman filtering to correct the ideal reference model includes:

[0019] Based on Kalman filtering, the estimated distance from the vehicle's center of gravity to the front axle and the estimated distance from the vehicle's center of gravity to the rear axle at the current moment are obtained.

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

[0021] In this embodiment of the application, obtaining the estimated distance from the vehicle's center of gravity to the front axle and the estimated distance from the vehicle's center of gravity to the rear axle at the current moment based on Kalman filtering includes:

[0022] The parameters of each matrix corresponding to the Kalman filter algorithm are determined based on the road input model, the motion equation of the suspended mass corresponding to the vehicle, and the motion equation of the unsuspended mass corresponding to the vehicle. The parameters include system state variables.

[0023] The Kalman filter algorithm is executed based on the matrices to obtain the system state value at the current time estimated by the Kalman filter algorithm;

[0024] Based on the system state value estimated at the current moment by the Kalman filter algorithm and the equation of motion of the unsustainable mass, calculate the estimated value of the vehicle suspension force at the current moment;

[0025] Based on the suspension mass motion equation and the estimated value of the vehicle suspension force at the current moment, calculate the estimated values ​​of the distance from the vehicle's center of gravity to the front axle and the distance from the vehicle's center of gravity to the rear axle.

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

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

[0028] The acquisition module is used to acquire the vehicle's longitudinal speed, actual yaw rate, and steering wheel angle under the current operating conditions.

[0029] The calculation module is used to calculate the desired yaw rate based on the longitudinal vehicle speed and the steering wheel angle, using an ideal reference model.

[0030] The response compensation module is used to determine a compensation angle based on the desired yaw rate and the actual yaw rate, so as to compensate the vehicle's lateral response based on the compensation angle.

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

[0032] Based on the desired yaw rate and the actual yaw rate, calculate the values ​​of the sliding mode equivalent control term and the sliding mode switching control term;

[0033] The values ​​of the sliding mode equivalent control term and the sliding mode switching control term are summed, and the sum is determined as the compensation angle.

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

[0035] 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.

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

[0037] In this embodiment of the application, the computing module is further used for:

[0038] The distances from the vehicle's center of gravity to the front axle and the distances from the vehicle's center of gravity to the rear axle under the current operating conditions are corrected using Kalman filtering to correct the ideal reference model.

[0039] Based on the corrected ideal reference model, the desired yaw rate is calculated according to the longitudinal vehicle speed and the steering wheel angle.

[0040] In this embodiment of the application, the computing module is further used for:

[0041] Based on Kalman filtering, the estimated distance from the vehicle's center of gravity to the front axle and the estimated distance from the vehicle's center of gravity to the rear axle at the current moment are obtained.

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

[0043] In this embodiment of the application, the computing module is further used for:

[0044] The parameters of each matrix corresponding to the Kalman filter algorithm are determined based on the road input model, the motion equation of the suspended mass corresponding to the vehicle, and the motion equation of the unsuspended mass corresponding to the vehicle. The parameters include system state variables.

[0045] The Kalman filter algorithm is executed based on the matrices to obtain the system state value at the current time estimated by the Kalman filter algorithm;

[0046] Based on the system state value estimated at the current moment by the Kalman filter algorithm and the equation of motion of the unsustainable mass, calculate the estimated value of the vehicle suspension force at the current moment;

[0047] Based on the suspension mass motion equation and the estimated value of the vehicle suspension force at the current moment, calculate the estimated values ​​of the distance from the vehicle's center of gravity to the front axle and the distance from the vehicle's center of gravity to the rear axle.

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

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

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

[0051] In summary, this application proposes a vehicle lateral response compensation method, device, electronic device, and storage medium. Based on the vehicle's longitudinal speed and steering wheel angle under current operating conditions, this application uses an ideal reference model to obtain the desired yaw rate. Based on the vehicle's actual yaw rate under current operating conditions and the newly obtained desired yaw rate, a compensation angle is obtained, and the vehicle's lateral response is compensated according to the compensation angle. This application compensates for the vehicle's lateral response by starting from the vehicle's actual state and actual response quantity under current operating conditions, which can more directly and accurately eliminate the error between the actual response and the desired response, improving response compensation efficiency and effectiveness. Attached Figure Description

[0052] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a flowchart illustrating a vehicle lateral response compensation method according to an exemplary embodiment of this application;

[0054] Figure 2 This is a flowchart illustrating a vehicle lateral response compensation method according to another exemplary embodiment of this application;

[0055] Figure 3 This is a flowchart illustrating a vehicle lateral response compensation method according to another exemplary embodiment of this application;

[0056] Figure 4 This is a flowchart illustrating a vehicle lateral response compensation method according to another exemplary embodiment of this application;

[0057] Figure 5 This is a flowchart illustrating a vehicle lateral response compensation method according to another exemplary embodiment of this application;

[0058] Figure 6 This is an overall flowchart illustrating a vehicle lateral response compensation method according to an exemplary embodiment of this application;

[0059] Figure 7 This is a flowchart illustrating a vehicle lateral response compensation method based on Kalman filtering for centroid position estimation according to an exemplary embodiment of this application.

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

[0061] Figure 9 This is a schematic diagram of an electronic device structure according to an exemplary embodiment of this application. Detailed Implementation

[0062] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. The embodiments described with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0063] Figure 1 This is a flowchart illustrating a vehicle lateral response compensation method according to an exemplary embodiment of this application, such as... Figure 1 As shown, the vehicle lateral response compensation method includes the following steps:

[0064] S101, obtain the vehicle's longitudinal speed, actual yaw rate, and steering wheel angle under the current operating conditions.

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

[0066] In some embodiments, the vehicle's longitudinal speed signal, actual yaw rate signal, and steering wheel angle signal can be acquired through sensors.

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

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

[0069] In some embodiments, the ideal reference model is a two-degree-of-freedom vehicle model, and the formula for calculating the desired yaw rate is determined based on the two-degree-of-freedom vehicle model.

[0070] The differential equations of motion for a two-degree-of-freedom vehicle are shown below:

[0071]

[0072]

[0073] In the formula, m is the total vehicle mass; v x and v y These are the vehicle's longitudinal speed and lateral speed, respectively; f and l rThese are the distances from the vehicle's center of gravity to the front and rear axles, respectively; C f and C r These are the lateral stiffness of the front and rear wheels of the vehicle, respectively; I z ω is the vehicle's moment of inertia; ω is the vehicle's yaw rate; δ f β is the front wheel steering angle; β is the sideslip angle, where β = v y / v x .

[0074] This application calculates the front wheel angle by using the steering ratio from the steering wheel angle output by the ADAS system, and expresses this front wheel angle as δ. a The final compensated steering angle is denoted as Δδ. Therefore, the front wheel steering angle δ in the above two-degree-of-freedom vehicle's equation of motion is... f This can be understood as the expected front wheel steering angle for lateral response:

[0075] δ f =δ a +Δδ (3)

[0076] From equations (1) and (2), the formula for calculating the desired yaw rate is:

[0077]

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

[0079] In practice, the parameter values ​​other than the longitudinal vehicle speed and front wheel steering angle under the current operating conditions in the formula for calculating the desired yaw rate can be obtained in advance, such as the vehicle mass m and the distance l from the vehicle's center of gravity to the front. f and the distance l from the vehicle's center of gravity to the rear axle r ; Vehicle front wheel lateral stiffness C f and the rear wheel lateral stiffness C r wait.

[0080] S103, determine the compensation angle based on the desired yaw rate and the actual yaw rate, so as to compensate for the vehicle's lateral response based on the compensation angle.

[0081] In some embodiments, a compensation angle can be determined based on a sliding mode controller or sliding mode control algorithm, according to the desired yaw rate and the actual yaw rate. The lateral response of the vehicle is compensated based on the compensation angle. For example, the actual front wheel angle under the current operating condition is compensated using formula (3) to obtain the desired front wheel angle. The front wheel rotation is controlled based on the desired front wheel angle to eliminate the error between the actual response and the desired response, effectively eliminating the insufficiency and inconsistency of the vehicle's left and right responses, improving the accuracy of ADAS lateral control, and ensuring the safety of vehicle driving.

[0082] In summary, the vehicle lateral response compensation method provided in this application, based on the vehicle's longitudinal speed and steering wheel angle under the current operating conditions, uses an ideal reference model to obtain the desired yaw rate. Based on the vehicle's actual yaw rate under the current operating conditions and the newly obtained desired yaw rate, a compensation angle is obtained, and the vehicle's lateral response is compensated according to the compensation angle. This application compensates for the vehicle's lateral response by starting from the vehicle's actual state and actual response quantity under the current operating conditions, which can more directly and accurately eliminate the error between the actual response and the desired response, providing a convenient and efficient lateral response compensation method.

[0083] Based on the above embodiments, such as Figure 2 As shown, in step S103 of this application embodiment, "determining the compensation angle based on the desired yaw rate and the actual yaw rate, so as to compensate the vehicle's lateral response based on the compensation angle" further includes the following steps:

[0084] S201, Calculate the values ​​of the sliding mode equivalent control term and the sliding mode switching control term based on the desired yaw rate and the actual yaw rate.

[0085] In some embodiments, based on the sliding mode control algorithm, the expression corresponding to the sliding mode equivalent control term is determined. 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 can be determined through 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. Based on the tracking error, the sliding mode function, and the expression for the actual yaw rate acceleration, the equivalent sliding mode control term is derived, where the expression for the actual yaw rate is determined based on a two-degree-of-freedom vehicle model.

[0088] For example: Let the tracking error be:

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

[0090]

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

[0092]

[0093] In the formula, c is the control gain of the sliding mode controller, which satisfies c>0.

[0094] set up Taking the derivatives of both sides of equations (3), (5), and (6), we have:

[0095]

[0096] Select The control quantity (i.e., sliding mode equivalent control term) u1 of the sliding mode equivalent control law can be obtained as follows:

[0097]

[0098] In this embodiment, the expression for the sliding mode equivalent control term is Equation (9), based on the actual yaw rate ω under the current operating condition and the obtained desired yaw rate ω. des Calculate the value of the sliding mode equivalent control term. Among them, the values ​​of other parameters in equation (9) besides the actual yaw rate and the desired yaw rate can be obtained in advance.

[0099] In practice, vehicles inevitably encounter external disturbances during operation, leading to system uncertainties. This application adds a switching control variable to the existing control variable, combining the two as the overall control output of the sliding mode controller. The switching control variable is the aforementioned sliding mode switching control term, which enables the sliding mode control system to move onto the "sliding surface" under the action of the switching function, thereby ensuring system stability.

[0100] In some embodiments, to obtain the switching control quantity (i.e., the sliding mode switching control term) u2, this application selects the sigmoid function as the switching function, which yields the following results:

[0101] u2=ηsig(s) (10)

[0102]

[0103] In the formula, η is the control parameter.

[0104] Based on equations (5), (7), (10), and (11), the values ​​of the sliding mode switching control terms are obtained according to the actual yaw rate and the desired yaw rate.

[0105] S202, sum the values ​​of the sliding mode equivalent control term and the sliding mode switching control term, and determine the summed value as the compensation angle.

[0106] In this embodiment, a sliding mode switching control term is added to the equivalent sliding mode control term. The values ​​of the equivalent control term and the sliding mode switching control term are summed, and the sum is used as the total control output u of the sliding mode controller or sliding mode control algorithm, which is then used as the compensation angle. That is, the value of u1 + u2 is used as the new Δδ: u = Δδ = u1 + u2.

[0107] This application uses a sliding mode control algorithm to generate a compensated steering angle based on the actual yaw rate response, thereby compensating for the vehicle response. A sliding mode controller tracks the desired yaw rate, and the combined sliding mode equivalent control term and sliding mode switching control term serve as the total output of the sliding mode control. This approach exhibits good robustness to uncertainties in the system model and external disturbances, effectively compensating for responses under different operating conditions and improving the effectiveness of lateral response compensation.

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

[0109] S301 corrects the distances from the vehicle's center of gravity to the front axle and the rear axle under the current operating conditions using Kalman filtering, in order to correct the ideal reference model.

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

[0111] In this embodiment, Kalman filtering is used to measure the distance l from the vehicle's center of gravity to the front axle. f and the distance l from the vehicle's center of gravity to the rear axle r An estimate is made, and the ideal reference model is corrected based on the estimated distance from the center of gravity to the front and rear axles, so as to determine the expected yaw rate based on the actual operating state of the vehicle, thereby enhancing the accuracy of the expected yaw rate and obtaining the accurate compensation angle in the future, thus reducing the response compensation error and improving the response compensation effect.

[0112] S302, based on a modified ideal reference model, calculates the desired yaw rate according to the longitudinal vehicle speed and steering wheel angle.

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

[0114] The Kalman filtering algorithm used in this application has the advantages of low computational cost, fast operation speed, and high accuracy, and is easy to apply in engineering.

[0115] This application uses Kalman filtering to estimate the center of mass position of the vehicle in real time during operation, thereby correcting the ideal reference model in real time, reducing the impact of model error on the desired yaw rate and compensated steering angle, eliminating the error between the vehicle's steady-state and dynamic characteristics, making the control more precise, improving the accuracy of response compensation, and enhancing the response compensation effect.

[0116] Based on the above embodiments, such as Figure 4 As shown, step S301 above, "correcting the distance from the vehicle's center of gravity to the front axle and the distance from the vehicle's center of gravity to the rear axle under the current operating condition based on Kalman filtering, in order to correct the ideal reference model," further includes the following steps:

[0117] S401, based on Kalman filtering, obtains the estimated distance from the vehicle's center of gravity to the front axle and the estimated distance from the vehicle's center of gravity to the rear axle at the current moment.

[0118] S402, the estimated distance from the vehicle's center of gravity to the front axle is used as the value of the distance from the vehicle's center of gravity to the front axle in the ideal reference model, and the estimated distance from the vehicle's center of gravity to the rear axle is used as the value of the distance from the vehicle's center of gravity to the rear axle in the ideal reference model.

[0119] This application embodiment designs a Kalman filter and uses the Kalman filtering algorithm corresponding to the Kalman filter to estimate the system state value at each moment, thereby obtaining the estimated value of the distance from the vehicle's center of gravity to the front axle and the estimated value of the distance from the vehicle's center of gravity to the rear axle at the current moment.

[0120] In some embodiments, the ideal reference model can be corrected by using the estimated distances from the vehicle's center of gravity to the front and rear axles at the current moment as the distances l from the vehicle's center of gravity to the front and rear axles in the ideal reference model. f and l r The values ​​of these two parameters are used to achieve this.

[0121] Based on the above embodiments, such as Figure 5 As shown, step S401 above, "based on Kalman filtering, obtain the estimated distance from the vehicle's center of gravity to the front axle and the estimated distance from the vehicle's center of gravity to the rear axle at the current moment," further includes the following steps:

[0122] S501 determines the parameters of each matrix corresponding to the Kalman filter algorithm based on the road input model, the motion equation of the suspended mass corresponding to the vehicle, and the motion equation of the unsuspended mass corresponding to the vehicle. The parameters include system state variables.

[0123] Before designing the Kalman filter, a vertical motion model of the vehicle needs to be established. First, the following assumptions are made regarding the vertical motion of the vehicle:

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

[0125] 2) The vehicle has minimal lateral tilt and little change in longitudinal speed.

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

[0127]

[0128]

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

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

[0131]

[0132] in

[0133]

[0134] In the formula, m fj and m rj These are the unsustainable masses of the front and rear axles, respectively; z ufj and z urj These represent the vertical displacements of the unsustainable masses of the front and rear axles, respectively; z fj and z rj These are the road surface excitation inputs; z sfj and z srj These represent the displacements at the upper support points of the front and rear axle suspensions, respectively; k sfj k srj c sfj With csrj They are respectively

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

[0136] The following is a model of the road surface input:

[0137]

[0138] In the formula, f0 is the lower cutoff frequency; G0 is the road surface roughness coefficient; w i The noise is Gaussian white noise with zero mean; v is the vehicle speed.

[0139] In some embodiments, the parameters of each matrix corresponding to the Kalman filter algorithm are determined based on equations (13)-(17), 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 equations is:

[0141]

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

[0143]

[0144]

[0145]

[0146]

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

[0148] Discretizing equation (18) using the Euler method yields the following result.

[0149]

[0150] For Kalman filtering to be applied, the process noise w(k) and measurement noise v(k) must be zero-mean Gaussian white noise, and w(k) and v(k) must be uncorrelated. Then we have:

[0151]

[0152]

[0153] E[w(k)vT [(k)]=0 (22)

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

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

[0156] 1) Prediction and estimation

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

[0158] 2) Update the predicted 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 filtering for estimating the 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 time step. In other words, it can estimate the system's state value at the current time step.

[0167] S503, based on the current system state value and the equation of motion of the unsprung mass estimated by the Kalman filter algorithm, calculate the estimated value of the vehicle suspension force at the current moment.

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

[0169] S504, based on the suspension mass motion equation and the estimated value of the vehicle suspension force at the current moment, calculate the estimated value of the distance from the vehicle's center of gravity to the front axle and the estimated value of the distance from the vehicle's center of gravity to the rear axle.

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

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

[0172] To clearly illustrate the vehicle lateral response compensation method proposed in this application, the following is combined with... Figure 6-7 An exemplary description of the overall process is provided, such as... Figure 6 As shown, the ideal reference model receives longitudinal vehicle speed signals and actual yaw rate signals from sensors. It converts the steering wheel angle signal obtained from the ADAS control module in the ADAS system into the front wheel angle through steering ratio conversion. This front wheel angle is then input into the ideal reference model, and the desired yaw rate is calculated. Before calculating the desired yaw rate, considering that the vehicle's center of gravity position changes during operation, leading to errors in the ideal reference model, this application designs a center of gravity position estimation module based on Kalman filtering. This module estimates the vehicle's center of gravity position during operation, corrects the ideal reference model in real time, and outputs a suitable desired yaw rate. The difference between the desired and actual yaw rates is input into the sliding mode controller module, where the sliding mode control algorithm generates a compensation angle based on the yaw rate response to compensate for the vehicle's response. For example... Figure 7 As shown, the center of mass position estimation module based on Kalman filtering estimates the center of mass position in real time at each moment through the following process: the distance from the vehicle's center of mass 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. At the current moment, using the estimated system state value at the current moment, the sum of the left and right suspension forces F of the front axle at the current moment is calculated according to Equation (16). fk The sum of the left and right suspension forces on the rear axle, F rkBased on the sum of the left and right suspension forces F of the front axle at the current moment fk The sum of the left and right suspension forces on the rear axle, F rk The equation of motion of the suspended mass, equation (14), and the parameter values ​​(such as longitudinal acceleration and pitch acceleration) in the suspended mass motion equation other than the distance from the vehicle's center of gravity to the front axle and the distance from the vehicle's center of gravity to the rear axle are used to calculate the distance l from the vehicle's center of gravity to the front axle at the current moment. fk and the distance l from the vehicle's center of gravity to the rear axle rk .

[0173] In summary, the vehicle lateral response compensation method provided in this application, based on the vehicle's longitudinal speed and steering wheel angle under the current operating conditions, uses an ideal reference model to obtain the desired yaw rate. Based on the vehicle's actual yaw rate under the current operating conditions and the newly obtained desired yaw rate, a compensation angle is obtained, and the vehicle's lateral response is compensated according to the compensation angle. This application compensates for the vehicle's lateral response by starting from the vehicle's actual state and actual response quantity under the current operating conditions, which can more directly and accurately eliminate the error between the actual response and the desired response. This application considers the error of the ideal reference model and proposes a centroid position estimation algorithm based on Kalman filtering to correct the ideal model, making the lateral response compensation method adaptable to different operating conditions. Furthermore, the vehicle lateral response compensation method proposed in this application can achieve the vehicle's desired response without replacing system structural components, disassembling and reassembling parts, or changing ADAS control logic, saving costs and reducing development time, providing a convenient and efficient lateral response compensation method.

[0174] It should be understood that the sequence number of each step in the above embodiments does not imply 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 this application.

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

[0176] The acquisition module 801 is used to acquire the vehicle's longitudinal speed, actual yaw rate, and steering wheel angle under the current operating conditions.

[0177] Calculation module 802 is used to calculate the desired yaw rate based on the longitudinal vehicle speed and the steering wheel angle, according to an ideal reference model.

[0178] The response compensation module 803 is used to determine a compensation angle based on the desired yaw rate and the actual yaw rate, so as to compensate the vehicle's lateral response based on the compensation angle.

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

[0180] Based on the desired yaw rate and the actual yaw rate, calculate the values ​​of the sliding mode equivalent control term and the sliding mode switching control term;

[0181] The values ​​of the sliding mode equivalent control term and the sliding mode switching control term are summed, and the sum is determined as the compensation angle.

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

[0183] 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.

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

[0185] In this embodiment of the application, the computing module is further used for:

[0186] The distances from the vehicle's center of gravity to the front axle and the distances from the vehicle's center of gravity to the rear axle under the current operating conditions are corrected using Kalman filtering to correct the ideal reference model.

[0187] Based on the corrected ideal reference model, the desired yaw rate is calculated according to the longitudinal vehicle speed and the steering wheel angle.

[0188] In this embodiment of the application, the computing module is further used for:

[0189] Based on Kalman filtering, the estimated distance from the vehicle's center of gravity to the front axle and the estimated distance from the vehicle's center of gravity to the rear axle at the current moment are obtained.

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

[0191] In this embodiment of the application, the computing module is further used for:

[0192] The parameters of each matrix corresponding to the Kalman filter algorithm are determined based on the road input model, the motion equation of the suspended mass corresponding to the vehicle, and the motion equation of the unsuspended mass corresponding to the vehicle. The parameters include system state variables.

[0193] The Kalman filter algorithm is executed based on the matrices to obtain the system state value at the current time estimated by the Kalman filter algorithm;

[0194] Based on the system state value estimated at the current moment by the Kalman filter algorithm and the equation of motion of the unsustainable mass, calculate the estimated value of the vehicle suspension force at the current moment;

[0195] Based on the suspension mass motion equation and the estimated value of the vehicle suspension force at the current moment, calculate the estimated values ​​of the distance from the vehicle's center of gravity to the front axle and the distance from the vehicle's center of gravity to the rear axle.

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

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

[0198] Each module in the aforementioned vehicle lateral response compensation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0199] In summary, the vehicle lateral response compensation device provided in this application, based on the vehicle's longitudinal speed and steering wheel angle under the current operating conditions, obtains the desired yaw rate using an ideal reference model. Based on the vehicle's actual yaw rate under the current operating conditions and the newly obtained desired yaw rate, a compensation angle is obtained, and the vehicle's lateral response is compensated according to the compensation angle. This application compensates for the vehicle's lateral response by starting from the vehicle's actual state and actual response quantity under the current operating conditions, which can more directly and accurately eliminate the error between the actual response and the desired response. This application considers the error of the ideal reference model and proposes a centroid position estimation algorithm based on Kalman filtering to correct the ideal model, making the lateral response compensation method well adaptable to different operating conditions. Furthermore, the vehicle lateral response compensation device proposed in this application can achieve the vehicle's desired response without replacing system structural components, disassembling and reassembling parts, or changing ADAS control logic, saving costs and reducing development time, thus providing a convenient and efficient lateral response compensation device.

[0200] To implement the above embodiments, this application also proposes an electronic device 900, such as... Figure 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, it implements the steps of the vehicle lateral response compensation method as described in the above embodiment.

[0201] In summary, this application proposes an electronic device that, based on the vehicle's longitudinal speed and steering wheel angle under current operating conditions, obtains the desired yaw rate using an ideal reference model. Based on the vehicle's actual yaw rate under current operating conditions and the newly obtained desired yaw rate, a compensation angle is obtained, and the vehicle's lateral response is compensated according to the compensation angle. This application compensates for the vehicle's lateral response by starting from the vehicle's actual state and actual response quantity under current operating conditions, which can more directly and accurately eliminate the error between the actual response and the desired response. This application considers the error of the ideal reference model and proposes a centroid position estimation algorithm based on Kalman filtering to correct the ideal model, making this lateral response compensation method well adaptable to different operating conditions. Furthermore, this application can achieve the vehicle's desired response without replacing system structural components, disassembling and reassembling parts, or changing ADAS control logic, saving costs and reducing development time.

[0202] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, 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. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0203] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to 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 above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for compensating for the lateral response of a vehicle, characterized in that, include: Obtain the vehicle's longitudinal speed, actual yaw rate, and steering wheel angle under the current operating conditions; Based on an ideal reference model, the desired yaw rate is calculated according to the longitudinal vehicle speed and the steering wheel angle. Based on the desired yaw rate and the actual yaw rate, a compensation angle is determined to compensate for the vehicle's lateral response. The calculation of the desired yaw rate based on the ideal reference model, according to the longitudinal vehicle speed and the steering wheel angle, includes: The distances from the vehicle's center of gravity to the front axle and the distances from the vehicle's center of gravity to the rear axle under the current operating conditions are corrected using Kalman filtering to correct the ideal reference model. Based on the corrected ideal reference model, the desired yaw rate is calculated according to the longitudinal vehicle speed and the steering wheel angle.

2. The method according to claim 1, characterized in that, The step of determining the compensation angle based on the desired yaw rate and the actual yaw rate includes: Based on the desired yaw rate and the actual yaw rate, calculate the values ​​of the sliding mode equivalent control term and the sliding mode switching control term; The values ​​of the sliding mode equivalent control term and the sliding mode switching control term are summed, and the sum is determined as the compensation angle.

3. The method according to claim 2, characterized in that, The sliding mode equivalent control term is determined through the following procedure: 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. Based on the tracking error, sliding mode function, and actual yaw acceleration expression, the equivalent sliding mode control term is derived.

4. The method according to claim 1, characterized in that, The method of correcting the distances from the vehicle's center of gravity to the front axle and the rear axle under the current operating conditions based on Kalman filtering to correct the ideal reference model includes: Based on Kalman filtering, the estimated distance from the vehicle's center of gravity to the front axle and the estimated distance from the vehicle's center of gravity to the rear axle at the current moment are obtained. The estimated value of the distance from the vehicle's center of gravity to the front axle is used as the value of the distance from the vehicle's center of gravity to the front axle in the ideal reference model, and the estimated value of the distance from the vehicle's center of gravity to the rear axle is used as the value of the distance from the vehicle's center of gravity to the rear axle in the ideal reference model.

5. The method according to claim 4, characterized in that, The process of obtaining estimates of the distance from the vehicle's center of gravity to the front axle and the distance from the vehicle's center of gravity to the rear axle at the current moment, based on Kalman filtering, includes: The parameters of each matrix corresponding to the Kalman filter algorithm are determined based on the road input model, the motion equation of the suspended mass corresponding to the vehicle, and the motion equation of the unsuspended mass corresponding to the vehicle. The parameters include system state variables. The Kalman filter algorithm is executed based on the matrices to obtain the system state value at the current time estimated by the Kalman filter algorithm; Based on the system state value estimated at the current moment by the Kalman filter algorithm and the equation of motion of the unsustainable mass, calculate the estimated value of the vehicle suspension force at the current moment; Based on the suspension mass motion equation and the estimated value of the vehicle suspension force at the current moment, calculate the estimated values ​​of the distance from the vehicle's center of gravity to the front axle and the distance from the vehicle's center of gravity to the rear axle.

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

7. A vehicle lateral response compensation device, characterized in that, include: The acquisition module is used to acquire the vehicle's longitudinal speed, actual yaw rate, and steering wheel angle under the current operating conditions. The calculation module is used to calculate the desired yaw rate based on the longitudinal vehicle speed and the steering wheel angle, using an ideal reference model. The response compensation module is used to determine the compensation angle based on the desired yaw rate and the actual yaw rate, so as to compensate the vehicle's lateral response based on the compensation angle. The calculation of the desired yaw rate based on the ideal reference model, according to the longitudinal vehicle speed and the steering wheel angle, includes: The distances from the vehicle's center of gravity to the front axle and the distances from the vehicle's center of gravity to the rear axle under the current operating conditions are corrected using Kalman filtering to correct the ideal reference model. Based on the corrected ideal reference model, the desired yaw rate is calculated according to the longitudinal vehicle speed and the steering wheel angle.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the vehicle lateral response compensation method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the steps of the vehicle lateral response compensation method as described in any one of claims 1-6.

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