Transverse control method for self-driving automobile with self-adaptive time domain parameters at different automobile speeds

Abstract

The invention discloses a transverse control method for an automatic driving automobile with self-adaptive time domain parameters at different automobile speeds, and belongs to the technical field of transverse control of the automatic driving automobile. Constructing a nonlinear state equation based on the transverse dynamic model; performing first-order Taylor expansion on the starting point of the nonlinear state equation to construct a linear state space expression; performing discretization processing on the linear state space expression by using a forward Euler method to obtain a discretization model, and constructing a target function; optimizing the objective function by introducing a relaxation factor; obtaining a quadratic programming problem through the discretization model and the optimized target function, and setting the constraint of the quadratic programming problem; and a time domain parameter adaptive controller is constructed, and an optimal control time domain and an optimal prediction time domain are obtained by using a multivariable control strategy. According to the invention, the accuracy of the self-driving automobile at a low speed is ensured, the stability at a high speed is also improved, and the driving safety of the automobile at different speeds is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of lateral control of autonomous vehicles, and particularly to a method for lateral control of autonomous vehicles with adaptive time-domain parameters at different vehicle speeds. Background Art

[0002] Currently, the lateral control of autonomous vehicles widely adopts the model predictive control (MPC) algorithm based on the vehicle dynamics model. Compared with the traditional linear quadratic regulator (LQR), MPC has the capabilities of multi-step prediction, rolling optimization, and handling multiple constraints, and can significantly improve the path tracking accuracy. A typical MPC controller uses fixed prediction horizon (N p ) and control horizon (N c ) parameters, realizes lateral control by optimizing the front wheel steering angle and vehicle speed, and performs well in low-speed scenarios.

[0003] However, the MPC with fixed time-domain parameters is difficult to meet the requirements of different vehicle speed conditions. At low speeds, a short prediction horizon may be sufficient to ensure control accuracy, but in high-speed conditions, the fixed parameters will cause large oscillations in the lateral movement of the vehicle and even instability. In addition, the traditional MPC does not consider the influence of vehicle speed changes on the time-domain parameters, resulting in limited control performance in a dynamic vehicle speed environment, and it cannot balance low-speed accuracy and high-speed stability, seriously affecting the safety and comfort of autonomous driving. Therefore, it is necessary to design a control scheme that automatically adjusts the time-domain parameters under different vehicle speed conditions and selects the optimal set of parameters to improve the stability and safety of vehicles driving at different speeds. Summary of the Invention

[0004] To solve the above problems, the present invention provides a method for lateral control of autonomous vehicles with adaptive time-domain parameters at different vehicle speeds.

[0005] To achieve the above technology, the steps are as follows:

[0006] S1. Establish a lateral dynamics model with the vehicle mass, derivatives, speed, acceleration along a preset direction, yaw angle, yaw rate of the vehicle, lateral stiffness, longitudinal stiffness, slip ratio of the front and rear wheels, front wheel steering angle, moment of inertia of the vehicle about a preset axis, and distances from the center of mass to the front and rear axles as the targets to describe the physical laws of the lateral movement of the vehicle;

[0007] The lateral dynamics model is established by means of small angle assumption and linear tire model to ignore the influence of the front wheel longitudinal force on the vehicle yaw, and in combination with the body coordinate system transformation formula and Newton's second law;

[0008] The small angle assumption is: assume that some angles (front wheel steering angle δ f , yaw angle of the vehicle )Small enough to reduce the non - linear terms and make the model more easily linearized and solved;

[0009] The linear tire model is: Assume that the lateral force of the tire has a linear relationship with the tire slip angle, which simplifies the non - linear characteristics of the tire force into a linear relationship for easy model derivation and real - time calculation;

[0010] The expression of the lateral dynamics model is as follows:

[0011]

[0012] In the formula, m is the vehicle mass; are the derivatives of the vehicle in the X and Y directions in the earth coordinate system respectively; are the accelerations in the x and y directions in the body coordinate system respectively; are the velocities in the x and y directions in the body coordinate system respectively; φ is the yaw angle of the vehicle; C Cf and C Cr are the lateral stiffnesses of the front and rear wheels respectively; C lf and C lr are the longitudinal stiffnesses of the front and rear wheels respectively; is the yaw angular velocity of the vehicle; is the yaw angular acceleration of the vehicle; δ f is the front wheel steering angle; s f and s r are the slip ratios of the front and rear wheels respectively; I z is the moment of inertia of the vehicle about the Z - axis; a and b are the distances from the center of mass to the front and rear axles respectively; where the x - axis points to the front of the vehicle, the y - axis points to the side of the vehicle, and the Z - axis points to the direction perpendicular to the ground.

[0013] S2. Extract state variables and control variables based on the lateral dynamics model, and construct a non - linear state equation to transform the lateral dynamics model into state - space form;

[0014] The expression of the non - linear state equation is as follows:

[0015]

[0016] In the formula, ξ is the state variable, where v y is the lateral velocity in the body coordinate system, v x is the longitudinal velocity in the body coordinate system; u is the control variable, u = [δ f ,v x T ;

[0017] S3. Use the first - order Taylor expansion for the starting point of the non - linear state equation, and subtract the expansion result from the non - linear state equation to construct a linear state - space expression; ​

[0018] Based on the lateral dynamics model, the influence of the longitudinal force of the front wheels on vehicle yaw is ignored, and through linearization correction, the errors of small-angle and linear tire assumptions are compensated;

[0019] The steps to construct the linear state space include:

[0020] S3.1. Use the first-order Taylor expansion for the starting point [ξ0, u0] of the nonlinear state equation, and ignore other high-order terms to obtain:

[0021]

[0022] In the formula, A(t) is the Jacobian matrix of the system state transition function f with respect to the state quantity ξ; B(t) is the Jacobian matrix of the system state transition function f with respect to the control quantity u;

[0023] S3.2. Subtract the expanded structure from the nonlinear state equation to obtain the linear state space expression:

[0024]

[0025] In the formula, is (ξ - ξ0); is (u - u0).

[0026] S4. Discretize the linear state space expression using the forward Euler method to obtain the discretized model;

[0027] The expression of the discretized model is as follows:

[0028] ξ(k + 1) = A k ξ(k) + B k u(k) + d(k)

[0029] A k = E + T′A

[0030] B k = T′B

[0031] d(k) = ξ0(k + 1) - A k ξ0(k) - Bu0(k)

[0032] η(k) = Cξ(k)

[0033] In the formula, k is the system operation time; T′ is the sampling time; A k 、B k are the first system matrix and the second system matrix after discretization respectively; E is the identity matrix; ξ(k + 1) is the predicted next moment value of ξ(k); C is the system output matrix; η(k) is the system output quantity.

[0034] S5. Based on the prediction horizon, control horizon, control variation, prediction time, predicted output sequence, expected value of the controller output, and weight matrix, construct an objective function, and optimize the objective function by introducing a relaxation factor; obtain a quadratic programming problem through the discretized model and the optimized objective function;

[0035] The steps are as follows:

[0036] S5.1. Based on the prediction horizon, control horizon, control variation, prediction time, predicted output sequence, expected value of the controller output, and weight matrix, construct an objective function, and optimize the objective function by introducing a relaxation factor;

[0037] The optimization method is: introduce a relaxation factor to prevent the situation where it is difficult to obtain the optimal solution in actual execution;

[0038] The expression of the optimized objective function is as follows:

[0039]

[0040] In the formula, N p is the prediction horizon; i' is the prediction time, i' ∈ N p ; N c is the control horizon, i is the control time, i ∈ N c ; τ is the weight coefficient; ε is the relaxation factor; ΔU is the control increment; k + i represents the prediction time; is the predicted output sequence; η ref is the expected value of the controller output; Q is the output weight matrix; R is the control increment weight matrix;

[0041] S5.2. Obtain a quadratic programming problem through the discretized model and the optimized objective function, and the expression is as follows:

[0042]

[0043] In the formula, represents the input variable, H represents the Hessian matrix; f represents the linear term vector;

[0044] The representation of

[0045]

[0046] Among them, represents the control input from time k to k + N c -1;

[0047] The expression of f is: Among them, θ represents the control transfer matrix, which is used to describe the influence of the control input on the future output; denotes the extended output weight; ψ denotes the state transition matrix, which is used to describe the influence of the initial state on future outputs; denotes the current state vector; T denotes the transpose;

[0048] The expression of H is:

[0049] where, I Np denotes N p order identity matrix; denotes the Kronecker product; denotes the extended control weight; R denotes the control weight matrix;

[0050] where, t denotes the sampling time, t ∈ T′;

[0051] S6. Set the constraints of the quadratic programming problem, including: control variable constraints, control increment constraints, output quantity constraints, centroid side slip angle constraints, vehicle adhesion condition constraints, and tire side slip angle constraints;

[0052] The control variable constraints, control increment constraints, and output quantity constraints are used to limit the front wheel steering angle range, and the expressions are as follows:

[0053] U min (k + i) ≤ U(k + i) ≤ U max (k + i)

[0054] ΔU min (k + i) ≤ ΔU(k + i) ≤ ΔU max (k + i)

[0055] Y min (k + i) ≤ Y(k + i) ≤ Y max (k + i)

[0056] ε ≥ 0, i = 0, 1, …, N c -1

[0057] In the formula, U max (k + i) and U min (k + i) are the upper and lower limit constraints of the control variable respectively; ΔU max (k + i) and ΔU min (k + i) are the upper and lower limit constraints of the control increment respectively; Y max (k + i) and Y min (k + i) are the upper and lower limit constraints of the output quantity respectively;

[0058] The centroid side slip angle constraints, vehicle adhesion condition constraints, and tire side slip angle constraints are used to ensure that the vehicle does not lose stability;

[0059] The expression is as follows:

[0060] -12° ≤ β ≤ 12°

[0061] a min -ε ≤ a y ≤ a max +ε

[0062] -5° ≤ α ≤ 5°

[0063] In the formula, β is the sideslip angle of the centroid; a max and a min are respectively the upper and lower limit constraints of the lateral acceleration; α is the tire sideslip angle.

[0064] S7. Construct a time-domain parameter adaptive controller by using the obtained quadratic programming problem and the quadratic programming problem constraint input; use the multivariable control strategy (MPC) for the time-domain parameter adaptive controller to obtain the optimal control time domain and prediction time domain.

[0065] Advantages of the present invention:

[0066] Based on conditional assumptions, the present invention establishes a non-linear dynamic model, processes the model to construct an objective function, solves it by quadratic programming, and at the same time constructs a time-domain parameter adaptive controller considering the constraints. By using the control time domain and prediction time domain parameters corresponding to different vehicle speed node ranges and integrating them into the MPC algorithm, a complete lateral control method is formed; the control method designed by the present invention can adaptively adjust the time-domain parameters according to different vehicle speeds, and the overall effect is better than the traditional MPC control with fixed time-domain parameters; the present invention ensures the accuracy of the autonomous vehicle at low speeds, improves the stability at high speeds, and guarantees the safety of the vehicle when driving at different vehicle speeds. Description of the drawings

[0067] Figure 1 is the step flow chart of the present invention;

[0068] Figure 2 is the effect obtained by the autonomous vehicle controlled by the adaptive control time domain parameters of the present invention tracking a double lane change road;

[0069] Figure 3 is the comparison curve of the low-speed condition tracking accuracy of the vehicle in the implementation process of the present invention and the MPC algorithm;

[0070] Figure 4 is the comparison curve of the high-speed tracking stability of the vehicle in the implementation process of the present invention and the MPC algorithm;

[0071] Figure 5 is the yaw angle change curve of the vehicle in the implementation process of the present invention;

[0072] Figure 6 This is the curve of the yaw rate change of the vehicle during the implementation of the present invention;

[0073] Figure 7 This is the curve of the sideslip angle of the vehicle's center of mass change during the implementation of the present invention. Specific implementation manner

[0074] The present invention will be further described in detail below in conjunction with specific embodiments.

[0075] As Figure 1 shown, a lateral control method for an autonomous driving vehicle with adaptive time-domain parameters at different vehicle speeds includes the following steps:

[0076] S1. Establish a lateral dynamics model with the vehicle mass, derivatives, speed, acceleration along a preset direction, yaw angle, yaw rate of the vehicle, lateral stiffness, longitudinal stiffness, slip ratio of the front and rear wheels, front wheel steering angle, moment of inertia of the vehicle about a preset axis, and distances from the center of mass to the front and rear axles as the targets to describe the physical laws of the vehicle's lateral motion;

[0077] The lateral dynamics model is established by means of the small-angle assumption and the linear tire model to ignore the influence of the front wheel longitudinal force on the vehicle yaw, and in combination with the body coordinate system transformation formula and Newton's second law;

[0078] The small-angle assumption is: assume that some angles (front wheel steering angle δ f , yaw angle of the vehicle ) during the vehicle motion are small enough to reduce the non-linear terms and make the model easier to linearize and solve;

[0079] The linear tire model is: assume that the lateral force of the tire is linearly related to the tire sideslip angle, and the non-linear characteristics of the tire force are simplified to a linear relationship for easy model derivation and real-time calculation;

[0080] The expression of the lateral dynamics model is as follows:

[0081]

[0082] In the formula, m is the vehicle mass; are the derivatives of the vehicle along the X and Y directions in the earth coordinate system respectively; are the accelerations along the x and y directions in the body coordinate system respectively; are the speeds along the x and y directions in the body coordinate system respectively; φ is the yaw angle of the vehicle; C Cf and C Cr are the lateral stiffnesses of the front and rear wheels respectively; C lf and C lr are the longitudinal stiffnesses of the front and rear wheels respectively; is the yaw rate of the vehicle; is the yaw acceleration of the vehicle; δ f is the front wheel steering angle; s f and s t are the slip ratios of the front and rear wheels respectively; I z is the moment of inertia of the vehicle about the Z-axis; a and b are the distances from the center of mass to the front and rear axles respectively; where the x-axis points forward of the vehicle, the y-axis points laterally of the vehicle, and the Z-axis points vertically to the ground.

[0083] S2. Extract state variables and control variables based on the lateral dynamics model, and construct a non-linear state equation to transform the lateral dynamics model into a state space form;

[0084] The expression of the non-linear state equation is as follows:

[0085]

[0086] In the formula, ξ is the state variable, where, v y is the lateral velocity in the body coordinate system, v x is the longitudinal velocity in the body coordinate system; u is the control variable, u = [δ f , v x T ;

[0087] S3. Expand the starting point of the non-linear state equation using the first-order Taylor expansion, and subtract the expansion structure from the non-linear state equation to construct a linear state space expression;

[0088] Based on the lateral dynamics model, ignore the influence of the front wheel longitudinal force on the vehicle yaw, and compensate for the errors of small angles and linear tire assumptions through linearization correction;

[0089] The steps to construct the linear state space include:

[0090] S3.1. Expand the starting point [ξ0, u0] of the non-linear state equation using the first-order Taylor expansion, ignore other high-order terms, and obtain:

[0091]

[0092] In the formula, A(t) is the Jacobian matrix of the system state transition function f with respect to the state variable ξ; B(t) is the Jacobian matrix of the system state transition function f with respect to the control variable u;

[0093] S3.2. Subtract the expansion structure from the non-linear state equation to obtain the linear state space expression:

[0094]

[0095] In the formula,​ is (ξ - ξ0); is (u - u0).

[0096] S4. Discretize the linear state - space expression using the forward Euler method to obtain a discretized model;

[0097] The expression of the discretized model is as follows:

[0098] ξ(k + 1)=A k ξ(k)+B k u(k)+d(k)

[0099] a k =E + T′A

[0100] B k =T′B

[0101] d(k)=ξ0(k + 1)-A k ξ0(k)-Bu0(k)

[0102] η(k)=Cξ(k)

[0103] In the formula, k is the system operation time; T′ is the sampling time; A k , B k are the first system matrix and the second system matrix after discretization respectively; E is the identity matrix; ξ(k + 1) is the value of the next moment predicted from ξ(k); C is the system output matrix; η(k) is the system output quantity.

[0104] S5. Construct an objective function based on the prediction horizon, control horizon, control variation, prediction time, prediction output sequence, expected value of the controller output, and weight matrix, and optimize the objective function by introducing a relaxation factor; Obtain a quadratic programming problem through the discretized model and the optimized objective function;

[0105] The steps are as follows:

[0106] S5.1. Construct an objective function based on the prediction horizon, control horizon, control variation, prediction time, prediction output sequence, expected value of the controller output, and weight matrix, and optimize the objective function by introducing a relaxation factor;

[0107] The optimization method is: introducing a relaxation factor to prevent the situation that it is difficult to obtain the optimal solution in actual execution;

[0108] The expression of the optimized objective function is as follows:

[0109]

[0110] In the formula, N p is the prediction horizon; i' is the prediction time, i' ∈ Np ; N c is the control time domain, i is the control moment, i ∈ N c ; τ is the weight coefficient; ε is the relaxation factor; ΔU is the control increment; k + i represents the prediction time; is the predicted output sequence; η ref is the expected value of the controller output; Q is the output weight matrix; R is the control increment weight matrix;

[0111] S5.2. Obtain a quadratic programming problem through the discretized model and the optimized objective function, and the expression is as follows:

[0112]

[0113] In the formula, represents the input variable, H represents the Hessian matrix; f represents the linear term vector;

[0114] The representation of is as follows:

[0115]

[0116] Among them, represents the control input at the k~k + N c -1 moment;

[0117] The expression of f is: Among them, θ represents the control transfer matrix, which is used to describe the influence of the control input on the future output; represents the extended output weight; ψ represents the state transfer matrix, which is used to describe the influence of the initial state on the future output; represents the current state vector; T represents the transpose;

[0118] The expression of H is:

[0119] Among them, represents N p order identity matrix; represents the Kronecker product; represents the extended control weight; R represents the control weight matrix;

[0120] Among them, t represents the sampling moment, t ∈ T′;

[0121] S6. Set the constraints of the quadratic programming problem, including: control variable constraint, control increment constraint, output quantity constraint, centroid side slip angle constraint, vehicle adhesion condition constraint, and tire side slip angle constraint;

[0122] The control variable constraint, control increment constraint, and output quantity constraint are used to limit the range of the front wheel steering angle, and the expressions are as follows:

[0123] U min (k + i) ≤ U(k + i) ≤ U max (k + i)

[0124] ΔU min (k + i) ≤ ΔU(k + i) ≤ ΔU max (k + i)

[0125] Y min (k + i) ≤ Y(k + i) ≤ Y max (k + i)

[0126] ε ≥ 0, i = 0, 1, …, N c -1

[0127] In the formula, U max (k + i) and U min (k + i) are the upper and lower limit constraints of the control variable respectively; ΔU max (k + i) and ΔU min (k + i) are the upper and lower limit constraints of the control increment respectively; Y max (k + i) and Y min (k + i) are the upper and lower limit constraints of the output quantity respectively;

[0128] The center of gravity sideslip angle constraint, vehicle adhesion condition constraint, and tire sideslip angle constraint are used to ensure the vehicle's stability;

[0129] The expressions are as follows:

[0130] -12° ≤ β ≤ 12°

[0131] a min -ε ≤ a y ≤ a max +ε

[0132] -5° ≤ α ≤ 5°

[0133] In the formula, β is the center of gravity sideslip angle; a max and a min are the upper and lower limit constraints of the lateral acceleration respectively; α is the tire sideslip angle.

[0134] S7. Construct a time-domain parameter adaptive controller by using the obtained quadratic programming problem and the quadratic programming problem constraints input; use the time-domain parameter adaptive controller with a multivariable control strategy (MPC) to obtain the optimal control time domain and prediction time domain;

[0135] The parameters in the multivariable control strategy (MPC) include: simulation step size, control variable range, control increment range, output weight matrix, control increment weight matrix, relaxation factor weight coefficient, and road surface adhesion coefficient; among them, the simulation step size in this embodiment is set to 200, that is, 200 meters.

[0136] The time-domain parameter adaptive controller includes a preset number of vehicle speed nodes.

[0137] In this embodiment, the vehicle speeds of vehicle v are divided into four nodes: 36, 54, 72, and 90.

[0138] The optimal control time domain and prediction time domain results are as follows:

[0139] v ≤ 36, N P = 15, N C = 2;

[0140] 36 < v ≤ 54, N P = 15, N c = 3;

[0141] 54 < v ≤ 72, N P = 20, N c = 5;

[0142] 72 < v ≤ 90, N P = 25, N c = 6.

[0143] To verify the present invention, a co-simulation test is carried out through Carsim and Simulink. Carsim establishes a physical model, and Simulink edits the control algorithm; the input in Carsim is set as the front wheel steering angle; the outputs are the longitudinal and lateral vehicle speeds, yaw rate, acceleration, longitudinal and lateral positions, and sideslip angle of the center of mass. Taking the classic double lane change path as the test road, the road surface adhesion coefficient is selected as 0.85. After connecting Carsim and Simulink, the MPC control code is written in the S-function module in Simulink, and the adaptive time-domain parameter controller obtained by the present invention is added to implement the control scheme for the vehicle to automatically adjust the control time domain and prediction time domain at different vehicle speeds.

[0144] As Figure 2 shown, the tracking trajectory of the vehicle adopting the adaptive time-domain parameter MPC is presented. It can be seen from the results that the vehicle trajectory closely fits the reference path without obvious overshoot or lag; it is proved that the adaptive algorithm can dynamically adjust the control time domain and prediction time domain according to the vehicle speed, improving the tracking accuracy in the full vehicle speed range.

[0145] As Figure 3As shown, the lateral position errors of the present invention and the fixed-time-domain parameter MPC at low speeds are compared. It can be seen from the results that the tracking error of the present invention is significantly smaller than that of the fixed-parameter MPC; it is verified that smaller control time domains and prediction time domains at low speeds (36 km / h, 54 km / h) are sufficient to ensure accuracy.

[0146] As Figure 4 shown, the lateral control stabilities of the present invention and the fixed-time-domain parameter MPC at high speeds (72 km / h, 90 km / h) are compared; it can be seen from the results that the vehicle trajectory of the fixed-parameter MPC oscillates significantly (such as large fluctuations in yaw rate), while the trajectory of the present invention is smooth; the instability trend at high speeds is effectively suppressed.

[0147] As Figure 5 shown, the variation of the vehicle yaw angle over time is presented. It can be seen from the results that the change in the yaw angle of the present invention is smoother and there are no sudden jumps.

[0148] As Figure 6 shown, the dynamic response of the yaw rate is presented. It can be seen that the amplitude of the yaw rate of the present invention is smaller and it converges quickly, indicating that the vehicle steering is smoother.

[0149] As Figure 7 shown, the variation of the sideslip angle of the center of mass is presented. It can be seen that the present invention controls the sideslip angle of the center of mass within 4°, avoiding tire sideslip.

[0150] The above specific embodiments of the present invention are only used for exemplary illustration or explanation of the principle of the present invention, and do not constitute a limitation to the present invention. Therefore, any modifications, equivalent replacements, improvements, etc. made without departing from the spirit and scope of the present invention shall be included within the protection scope of the present invention. In addition, the appended claims of the present invention are intended to cover all variations and modified examples falling within the scope and boundaries of the appended claims, or equivalent forms of such scope and boundaries.

Claims

1. A lateral control method for an autonomous vehicle with adaptive time-domain parameters at different vehicle speeds, characterized in that, Including the following steps: S1. Establish a lateral dynamics model with the vehicle mass, derivatives, speed, acceleration in a preset direction, yaw angle, yaw angular velocity of the vehicle, lateral stiffness, longitudinal stiffness, slip ratio of the front and rear wheels, front wheel steering angle, moment of inertia of the vehicle about a preset axis, and distances from the center of mass to the front and rear axles as the objectives to describe the physical laws of vehicle lateral motion; S2. Extract state variables and control variables based on the lateral dynamics model and construct a nonlinear state equation to transform the lateral dynamics model into a state-space form; S3. Use first-order Taylor expansion for the starting point of the nonlinear state equation and subtract the expansion structure from the nonlinear state equation to construct a linear state-space expression; S4. Discretize the linear state-space expression using the forward Euler method to obtain a discretized model; S5. Construct an objective function based on the prediction horizon, control horizon, control variation, prediction time, prediction output sequence, expected value of the controller output, and weight matrix, and optimize the objective function by introducing a relaxation factor; Obtain a quadratic programming problem through the discretized model and the optimized objective function; S6. Set the constraints of the quadratic programming problem, including: control variable constraints, control increment constraints, output variable constraints, sideslip angle constraints of the center of mass, vehicle adhesion condition constraints, and tire sideslip angle constraints; S7. Construct a time-domain parameter adaptive controller through the obtained quadratic programming problem and the input of the quadratic programming problem constraints; use the multivariable control strategy for the time-domain parameter adaptive controller to obtain the optimal control horizon and prediction horizon, and complete the construction of the lateral control method for an autonomous vehicle with adaptive time-domain parameters at different vehicle speeds.

2. A lateral control method for an autonomous vehicle with different vehicle speeds adapting to time-domain parameters according to claim 1, characterized in that: The expression for establishing the lateral dynamics model with the vehicle mass, derivatives, speed, acceleration in a preset direction, yaw angle, yaw angular velocity of the vehicle, lateral stiffness, longitudinal stiffness, slip ratio of the front and rear wheels, front wheel steering angle, moment of inertia of the vehicle about a preset axis, and distances from the center of mass to the front and rear axles as the objectives is as follows: where m is the vehicle mass; are the derivatives of the vehicle along the X and Y directions in the earth coordinate system, respectively; are the accelerations along the x and y directions in the body coordinate system, respectively; are the velocities along the x and y directions in the body coordinate system, respectively; is the yaw angle of the vehicle; C Cf and C Cr are the lateral stiffnesses of the front and rear wheels, respectively; C lf and C lr are the longitudinal stiffnesses of the front and rear wheels, respectively; is the yaw rate of the vehicle; is the yaw acceleration of the vehicle; δ f is the front wheel steering angle; s f and s r are the slip ratios of the front and rear wheels, respectively; I z is the moment of inertia of the vehicle about the Z axis; a and b are the distances from the center of mass to the front and rear axles, respectively; where the x-axis points forward of the vehicle, the y-axis points laterally of the vehicle, and the Z-axis points vertically to the ground.

3. A lateral control method for an autonomous driving vehicle with adaptive time-domain parameters at different vehicle speeds according to claim 1, characterized in that: In the step of extracting state variables and control variables based on the lateral dynamics model and constructing a nonlinear state equation to transform the lateral dynamics model into a state-space form, the expression of the nonlinear state equation is as follows: The expression of the nonlinear state equation is as follows: In the formula, ξ is the state quantity, wherein, v y is the lateral velocity in the vehicle body coordinate system, and v x is the longitudinal velocity in the vehicle body coordinate system; u is the control quantity, and u = [δ f , v x T .​ 4. A lateral control method for an autonomous driving vehicle with different vehicle speed adaptive time domain parameters according to claim 1, characterized in that: The steps of using first-order Taylor expansion for the starting point of the nonlinear state equation and subtracting the expansion structure from the nonlinear state equation to construct a linear state-space expression are as follows: S3.

1. Use first-order Taylor expansion for the starting point [ξ0,u0] of the nonlinear state equation, ignore other high-order terms, and obtain: where A(t) is the Jacobian matrix of the system state transition function f with respect to the state variable ξ; B(t) is the Jacobian matrix of the system state transition function f with respect to the control variable u; S3.

2. Subtract the expansion structure from the nonlinear state equation to obtain a linear state-space expression: Wherein, is (ξ - ξ0); is (u - u0).

5. A lateral control method for an autonomous vehicle with different vehicle speeds adapting to time-domain parameters according to claim 1, characterized in that: The expression for discretizing the linear state-space expression using the forward Euler method to obtain a discretized model is as follows: ξ(k + 1) = A k ξ(k) + B k u(k) + d(k) A k = E + T'A B k = T'B d(k) = ξ0(k + 1) - A k ξ0(k) - Bu0(k) η(k) = Cξ(k) where k is the system operation time; T′ is the sampling time; A k , B k are respectively the first system matrix and the second system matrix after discretization; E is the identity matrix; ξ(k + 1) is the predicted value at the next moment of ξ(k); C is the system output matrix; η(k) is the system output quantity.

6. A lateral control method for an autonomous vehicle with adaptive time-domain parameters at different vehicle speeds according to claim 1, characterized in that: Construct an objective function based on the prediction horizon, control horizon, control variable change, prediction time, predicted output sequence, expected value of the controller output, and weight matrix, and optimize the objective function by introducing a relaxation factor; the steps to obtain a quadratic programming problem through the discretized model and the optimized objective function are as follows: S5.

1. Construct an objective function based on the prediction horizon, control horizon, control variable change, prediction time, predicted output sequence, expected value of the controller output, and weight matrix, and optimize the objective function by introducing a relaxation factor; The optimization method is as follows: introducing a relaxation factor to prevent the situation where it is difficult to obtain the optimal solution in actual execution; The expression of the optimized objective function is as follows: where N p is the prediction horizon; i' is the prediction time, i' ∈ N p ; N c is the control horizon, i is the control time, i ∈ N c ; τ is the weight coefficient; ε is the relaxation factor; ΔU is the control increment; k + i represents the prediction time; is the predicted output sequence; η ref is the expected value of the controller output; Q is the output weight matrix; R is the control increment weight matrix; S5.

2. Obtain a quadratic programming problem through the discretized model and the optimized objective function, and the expression is as follows: In the formula, represents the input variable, H represents the Hessian matrix; f represents the linear term vector.

7. A lateral control method for an autonomous vehicle with different vehicle speeds adapting to time-domain parameters according to claim 1, characterized in that: The constraints for setting the quadratic programming problem include: control variable constraints, control increment constraints, output quantity constraints, center of mass sideslip angle constraints, vehicle adhesion condition constraints, and tire sideslip angle constraints. The control variable constraints, control increment constraints, and output quantity constraints are used to limit the front wheel steering angle range, and the expressions are as follows: U min (k + i) ≤ U(k + i) ≤ U max (k + i) ΔU min (k + i) ≤ ΔU(k + i) ≤ ΔU max (k + i) Y min (k + i) ≤ Y(k + i) ≤ Y max (k + i) ε≥0, i = 0, 1, …, N c -1 where, U max (k + i) and U min (k + i) are the upper and lower bound constraints of the control variable respectively; ΔU max (k + i) and ΔU min (k + i) are the upper and lower bound constraints of the control increment respectively; Y max (k + i) and Y min (k + i) are the upper and lower bound constraints of the output respectively; The center of mass sideslip angle constraints, vehicle adhesion condition constraints, and tire sideslip angle constraints are used to ensure that the vehicle does not lose stability; The expressions are as follows: -12°≤β≤12° a min -ε ≤ a y ≤ a max +ε -5°≤α≤5° In the formula, β is the centroid side slip angle; a max and a min are respectively the upper and lower limit constraints of the lateral acceleration a y ; α is the tire side slip angle.

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