Vehicle lane changing trajectory tracking error elimination method and device and storage medium

Abstract

The invention relates to a vehicle lane changing trajectory tracking error elimination method and device and a storage medium, and belongs to the technical field of vehicle automatic driving, and the method specifically comprises the following steps: building a two-degree-of-freedom path tracking error state space of a vehicle; a quintic polynomial curve is adopted to generate lane changing tracks meeting different lane changing scenes; a trajectory preview controller is introduced to solve the time lag problem, a transverse LQR controller is constructed to perform path tracking, system steady-state errors are eliminated through a front wheel steering angle compensation controller, and the problem of insufficient steering of the vehicle is solved. And constructing a longitudinal double-PID controller to respectively adjust the longitudinal speed error and the position error so as to realize accurate trajectory tracking. According to the invention, the tracking effect is more suitable for a real driving scene to enhance the trajectory tracking precision, the path tracking capability, stability and comfort of the vehicle are improved, and more accurate trajectory tracking is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle automatic driving, and in particular to a method, device and storage medium for eliminating vehicle lane change trajectory tracking errors. Background Art

[0002] At present, autonomous driving has become a frontier field in vehicle engineering research and represents a new development direction for the future automotive industry. The autonomous driving system consists of four core parts: environmental perception, decision-making, path planning and execution control. Execution control is a crucial part of the autonomous driving system. It must not only ensure the accuracy of tracking, but also ensure the safety of intelligent vehicles and the stability of control. The linear quadratic regulator (LQR) is an optimal control strategy for the execution control part. Since it does not require real-time online optimization and solution, it saves a lot of computing resources and has been widely used in the field of vehicle path tracking control. However, due to the inertia and time lag of the vehicle system, there will often be a certain delay when the vehicle is tracking the planned path. Therefore, there are the following problems: 1. Although existing methods such as model predictive control (MPC) optimize the solution of nonlinear problems, the computational burden is still large and it is difficult to meet the needs of real-time control.

[0003] 2. The traditional LQR controller has time lag in path tracking, which leads to poor tracking effect of the vehicle under continuously changing road conditions.

[0004] 3. The existing PID controller is difficult to effectively adjust the front wheel angle when the vehicle is understeering, resulting in low trajectory tracking accuracy.

[0005] For example, the patent publication number CN119239631A discloses a "coupling control system for unmanned vehicles based on dynamic models", which includes: a sensor module for obtaining the driving state of the unmanned vehicle; a bicycle dynamic model modeling module for establishing the dynamic model of the unmanned vehicle; a steering control module for calculating the tire slip angle during steering based on the tire stiffness model and adjusting the steering torque; a coupling control module for generating control instructions for the steering, acceleration and speed of the unmanned vehicle based on the bicycle model created by the bicycle dynamic model modeling module and the tire stiffness model used by the steering control module; the invention combines the linear quadratic regulator and the model predictive controller to complete the control strategy, realizes the precise control of the unmanned vehicle, and can predict and avoid static and dynamic obstacles in real time. However, the invention still uses the traditional LQR controller to control the vehicle track in real time through the cost function. In the scenario of continuous lane change, if there is no advance prediction and adjustment of the track before the lane change, the amplitude of the subsequent lane change will be large, affecting the stability and comfort of the vehicle. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a method, device and storage medium for eliminating vehicle lane change trajectory tracking errors.

[0007] In a first aspect, a method for eliminating a vehicle lane change trajectory tracking error comprises the following steps: S1, establish the two-degree-of-freedom path tracking error state space of the vehicle; S2, using quintic polynomial trajectory planning to generate lane-changing trajectories that meet different lane-changing scenarios; S3, introduces a trajectory preview controller to solve the time lag problem, builds a lateral LQR controller for path tracking, and eliminates the system steady-state error through the front wheel angle compensation controller to solve the problem of vehicle understeering; S4, construct a longitudinal dual PID controller to adjust the longitudinal velocity error and position error respectively to achieve accurate trajectory tracking.

[0008] Optionally, in step S1, the two-degree-of-freedom path tracking error state space equation is: , matrix , and , determined by the vehicle dynamics parameters, specifically: , ,A= , , , in, is the state space of tracking error, is the lateral error of the vehicle, is the vehicle’s heading error, is the tangent angle of the error trajectory of the vehicle’s center of mass, is the front wheel slip angle, and is the cornering stiffness coefficient of the front and rear wheels, m is the vehicle body mass, is the moment of inertia about the Z axis, is the vehicle longitudinal velocity, is the distance from the center of mass to the front axle, is the distance from the center of mass to the rear axle, is the wheel angle.

[0009] The present invention uses a quintic polynomial trajectory planning module to generate lane changing trajectories that meet different lane changing scenarios, and considers the understeering problem of the vehicle, and builds a PID controller to eliminate the impact of understeering.

[0010] Optionally, in step S2, the quintic polynomial trajectory planning satisfies the following conditions: Longitudinal displacement and lateral displacement About time The function expression is: ; The boundary conditions include that the position, velocity, and acceleration of the starting and ending points are all zero. The coefficients are determined by solving the linear equations by substituting the boundary conditions into the system. , , ensuring that the trajectory curvature is continuous and the jerk is minimized, and obtaining a reference trajectory containing lateral and longitudinal position, heading angle, curvature, velocity and acceleration constraints: .

[0011] Optionally, in step S3, the trajectory preview controller solves the time lag problem in the following manner: The trajectory preview controller controls the vehicle's front wheel angle in real time to track the trajectory points within a certain period of time in the future; Among them, the prediction time is , the coordinate position information of the predicted tracking trajectory point is , To predict the horizontal and vertical coordinates of the tracking trajectory points, To predict the horizontal and vertical speeds of the tracking trajectory points, To predict the heading angle of the tracking trajectory point; The vehicle tracks the path at the current speed according to the tangent of the predicted tracking trajectory point, and the relationship between the current coordinate information of the vehicle and the coordinate position information of the predicted tracking trajectory point can be obtained as follows: .

[0012] Optionally, in step S3, the system steady-state error is eliminated by a front wheel steering angle compensation controller, specifically: Calculate the appropriate front wheel steering compensation , so that the system steady-state error is 0 to achieve the optimal control state, for: , in, is the road curvature, Corresponding to the heading error The feedback gain.

[0013] Optionally, in step S4, a longitudinal dual PID controller is constructed to adjust the longitudinal velocity error and the position error respectively, specifically: Design a position PID controller based on the position error, and the output is the speed error compensation , the output form of the PID controller can be expressed as: , in is the proportional gain, is the integral gain, is the differential gain, For time, is the integral variable, and e is the input error signal of the PID controller. When it increases, the response speed of the system is accelerated, the steady-state error of the system is reduced, and the control accuracy and sensitivity of the system are improved; Used to reduce steady-state error; It can reduce overshoot and adjustment time and improve system stability.

[0014] In a second aspect, a vehicle lane change trajectory tracking error elimination device is provided, the device comprising: Lateral control module: including LQR controller, trajectory preview controller and front wheel steering angle compensation controller; the LQR controller is used for path tracking, the trajectory preview controller is used to solve the time lag problem of the LQR controller, and the front wheel steering angle compensation controller is used to eliminate the system steady-state error; Longitudinal control module: including dual PID controller and throttle / brake calibration table. The dual PID controller is used to control the speed and position error of the vehicle, and the throttle / brake calibration table is used to control the acceleration and braking of the vehicle in real time; Trajectory planning module: uses quintic polynomial trajectory planning to generate lane-changing trajectories that meet different lane-changing scenarios; Understeering compensation module: adjusts the front wheel angle of the vehicle through the PID controller to solve the problem of vehicle understeering.

[0015] In a third aspect, a computer-readable storage medium includes a computer program or instructions, which, when executed on a computer, causes the computer to execute any method as described in the first aspect.

[0016] It also has the following beneficial effects: 1. Based on the vehicle path tracking error dynamics model, the present invention designs a trajectory tracking system on the basis of the LQR controller to reduce the computational burden and meet the needs of real-time control.

[0017] 2. In order to solve the time lag problem of the LQR controller, the present invention introduces a trajectory preview controller, which predicts the trajectory points in the future and adjusts the front wheel angle of the vehicle in real time, thereby improving the tracking effect of the vehicle under continuously changing road conditions.

[0018] 3. The present invention designs a front wheel angle compensation controller, which eliminates the steady-state error of the system through feedforward control, further improving the accuracy of trajectory tracking. A longitudinal dual PID controller is designed to adjust the speed error and position error respectively, and the acceleration and braking of the vehicle are controlled in real time through the throttle / brake calibration table, improving the accuracy and stability of longitudinal control. The front wheel angle of the vehicle is adjusted by the PID controller, which solves the problem of vehicle understeering and further improves the accuracy of trajectory tracking.

[0019] 4. The present invention adopts a quintic polynomial curve to generate a lane-changing trajectory that satisfies different lane-changing scenarios, thereby ensuring the smoothness and continuity of the trajectory and improving the comfort and stability of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the scheme in this application, the following will describe the embodiments of this application. A brief introduction to the drawings is given. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0021] Figure 1 A vehicle dynamics model diagram in an embodiment of the present invention; Figure 2 A schematic diagram of vehicle error analysis in an embodiment of the present invention; Figure 3 A diagram of a vehicle lane change trajectory tracking control scheme in an embodiment of the present invention; Figure 4 This is a schematic diagram of the design of a vehicle lateral control system in an embodiment of the present invention; Figure 5 Schematic diagram of the entry-exit tracking trajectory of the prior art LQR in an embodiment of the present invention, Figure 5 (a) is the entry tracking trajectory. Figure 5 (b) in the figure is the exit tracking trajectory; Figure 6 Schematic diagram of trajectory preview control in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those in the technical field of this application. The meanings are generally understood by technicians; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0023] Provided are a method, device and storage medium for eliminating vehicle lane change trajectory tracking errors.

[0024] In a first aspect, a method for eliminating a vehicle lane change trajectory tracking error comprises the following steps: S1, establish the two-degree-of-freedom path tracking error state space of the vehicle; S2, using a quintic polynomial curve to generate lane-changing trajectories that meet different lane-changing scenarios; S3, introduces a trajectory preview controller to solve the time lag problem, builds a lateral LQR controller for path tracking, and eliminates the system steady-state error through the front wheel angle compensation controller to solve the problem of vehicle understeering; S4, construct a longitudinal dual PID controller to adjust the longitudinal velocity error and position error respectively to achieve accurate trajectory tracking.

[0025] In order to describe the vehicle more accurately, Newton's second law and Euler-Lagrange method are applied to dynamically model the vehicle system, and a simplified bicycle dynamics model is used to consider two-dimensional plane motion. The present invention uses a dynamic model as a control model for the lateral control of an autonomous driving vehicle. For the vehicle control model, the higher the accuracy, the greater the amount of calculation, and the solution speed will decrease accordingly. The vehicle model needs to focus on the lateral movement and yaw movement of the vehicle. For complex nonlinear vehicle systems, on the basis of ensuring the vehicle's dynamic characteristics, in order to improve the real-time performance of the control algorithm and reduce the difficulty of modeling, the following simplifications are made: (1) Assume that the road conditions are good and the vehicle has no vertical motion; (2) Assuming that the front wheel turning angle of the vehicle is small, the vehicle's speed changes relatively slowly; (3) The influence of vehicle suspension is not considered; (4) Vehicle motion is described by a single-track model; (5) Ignore the effect of air resistance on the vehicle; like Figure 1 As shown, for a simple bicycle model, applying Newton's law, we get the following model equation: (1) in and are the longitudinal force and lateral force of the vehicle, respectively. and are the longitudinal and lateral inertial accelerations, For the body mass, is the rotational torque around the Z axis, is the moment of inertia about the Z axis, is the rate of change of yaw angular velocity.

[0026] Consider a nonholonomic constraint, where the rear steering angle of the wheel is set to zero ( ),therefore ,from Figure 1The relationship between the lateral force and the longitudinal force acting on the tire can be obtained by using equation (1): (2) When the vehicle is driving, the tires interact with the road surface, causing the tires to deform in different maneuvers, thereby generating tire forces in the longitudinal and lateral directions. These nonlinear forces are usually linearized for use in simple dynamic models. Under the assumption of a small slip angle, the slip angle is considered to be proportional to the tire force, which realizes the linearization of the tire force. In this case, the tire lateral force can be expressed as: (3) in, and Denote the front and rear wheel cornering stiffness coefficients, and The tire slip angle is defined as the angle between the direction of the wheel and the velocity vector. Assuming that only the front wheel can be steered, the front and rear wheel slip angles are set as: and , the rear steering angle is set to zero ( ), is the angle between the longitudinal axis of the wheel and the longitudinal speed, calculated from the ratio of the lateral speed to the longitudinal speed: (4) Combining equations (3) and (4) and using the small angle approximation method for linearization, and then processing the previous dynamic model equations and the obtained tire forces, the state space equations of lateral dynamics can be obtained: (5) in, is the state space vector, is the front wheel slip angle, For time, is the vector of the vehicle's lateral position, the matrix , and , determined by the vehicle dynamics parameters, specifically: A= , , ; in, is the vehicle longitudinal velocity, is the distance from the center of mass to the front axle, is the distance from the center of mass to the rear axle.

[0027] Optional, such as Figure 2As shown in the figure, various errors will occur in the process of tracking the expected trajectory, including lateral error, longitudinal error, heading error, speed error, and acceleration error, so it is necessary to solve the optimal control amount of the autonomous driving vehicle while ensuring that the error is as small as possible. is the vehicle position vector, is the vehicle heading angle, is the vehicle velocity vector, is the vehicle projection point position vector, is the angle of the tangent direction of the projection point, d is the distance between the vehicle position and the projection point position, which is expressed as the lateral error of the vehicle. To show the speed of the vehicle at the projection point, It is represented as the heading angle error, (6) The lateral error of formula (6) To find the derivative, It will change with the reference path, and combined with the coordinate transformation, we can get: (7) in, is the path curvature, since the heading angle is According to the small angle approximation method, Considered as a small amount, equation (7) can be expressed as:

[0028] (8) make , , combined with formula (8), we can get: (9) Since the road is generally flat, there is no second-order derivative and it is usually ignored. Substituting equation (9) into the state space (5) of lateral dynamics, the equation of the two-degree-of-freedom path tracking error state space in step S1 can be obtained: (10) matrix , and , determined by the vehicle dynamics parameters, specifically: , ,A= , , , in, is the state space of tracking error, is the lateral error of the vehicle, is the vehicle’s heading error, is the tangent angle of the error trajectory of the vehicle’s center of mass, is the front wheel slip angle, and is the cornering stiffness coefficient of the front and rear wheels, m is the vehicle body mass, is the moment of inertia about the Z axis, is the vehicle longitudinal velocity, is the distance from the center of mass to the front axle, is the distance from the center of mass to the rear axle, is the wheel angle.

[0029] As an upper-level control module, trajectory planning is an indispensable and important part of the entire autonomous driving vehicle system. Its purpose is to generate a trajectory for the autonomous driving vehicle to travel and transmit the trajectory to the control module.

[0030] like Figure 3 As shown in the figure, polynomial curves are mostly used in scenarios where the vehicle is required to reach a certain expected state at the starting point and the target point. In order to better achieve the tracking effect, the position, velocity, acceleration, heading angle, and curvature of the designed reference trajectory should be continuous at every moment. Since the curvature of the quintic polynomial is continuous and smooth, the comfort is good, that is, the rate of change of the jerk is small. The jerk is an important physical quantity to measure the comfort of the vehicle. For a function curve, the problem of finding the minimum jerk while satisfying the boundary conditions belongs to the functional problem. Under the solution of the functional, the quintic polynomial trajectory planning is the simplest polynomial curve that meets the conditions.

[0031] Optionally, in step S2, the quintic polynomial trajectory planning satisfies the following conditions: Longitudinal displacement and lateral displacement About time The function expression is: (11) The boundary conditions include that the position, velocity, and acceleration of the starting and ending points are all zero. The coefficients are determined by solving the linear equations by substituting the boundary conditions into the system. , , ensuring that the trajectory curvature is continuous and the jerk is minimized, and obtaining a reference trajectory containing lateral and longitudinal position, heading angle, curvature, velocity and acceleration constraints: (12) Based on the tracking error model of the autonomous driving vehicle, a Figure 4The lateral controller shown limits the lateral error and heading error during tracking and eliminates the state error between the vehicle's current position and a reference position.

[0032] Traditional LQR uses the current position and state as input to solve the problem, which lacks predictability and will cause a certain control lag under continuously changing road conditions. Figure 5 As shown in (a) of the entering tracking trajectory, when the vehicle enters the tracking trajectory, due to the lateral error It is not 0. In order to eliminate the steady-state error between the current state and the tracking trajectory, the vehicle state will change. However, in the future, the vehicle will enter the tracking trajectory and automatically eliminate the error. This shows that the traditional LQR affects the stability and comfort of the vehicle.

[0033] If the vehicle's current position is already on the tracking path, such as Figure 5 As shown in (b) of the figure, since the steady-state error of the vehicle at the current moment is 0, the vehicle will continue to drive in the current state until the vehicle deviates from the tracking path. When the system determines that the vehicle has a steady-state error, the controller controls the vehicle's movement back to the tracking trajectory. At this point, we need the vehicle to be able to predict the planned route points in the future so that the vehicle can stay on the tracking trajectory points in real time.

[0034] In order to eliminate the lag of the control system, reduce the functional consumption of the control system, increase the stability and comfort of the system, the trajectory points in the future are tracked, and the system determines that it controls the front wheel angle of the vehicle in real time to effectively track the trajectory, such as Figure 6 As shown, in step S3, the trajectory preview controller solves the time lag problem in the following ways: The trajectory preview controller controls the vehicle's front wheel angle in real time to track the trajectory points within a certain period of time in the future; Among them, the prediction time is , the coordinate position information of the predicted tracking trajectory point is , To predict the horizontal and vertical coordinates of the tracking trajectory points, To predict the horizontal and vertical speeds of the tracking trajectory points, To predict the heading angle of the tracking trajectory point; The vehicle follows the path at the current speed based on the predicted tangent of the tracking trajectory point. ,and , from which the relationship between the current coordinate information of the vehicle and the coordinate position information of the predicted tracking trajectory point can be obtained as follows: (13) The advantage of LQR in lateral control of autonomous driving is that when the system state deviates from the equilibrium state due to obstacles or emergencies during vehicle driving, it ensures that the vehicle tracking control system can approach the equilibrium state without consuming too much energy. In real scenarios, since the vehicle computing and processing data is discrete data, it is necessary to discretize the LQR control and discretize the continuous state space equation of formula (10). Integrating both sides of equation (10), we get (14) According to formula (14), using the integral mean value theorem, we can deduce: (15) By simplifying equation (15) using the midpoint Euler method, the forward Euler method and the backward Euler method, we obtain: (16) in, is the identity matrix, is the sampling period, .neglect The state space equation of the discretized tracking error can be expressed as: (17) in, , .

[0035] According to the discretized state space equation established in formula (17), the LQR controller system performance function is constructed: (18) in, is the state variable of the system, is the control variable of the system, is the state error weighting matrix, is the weighted matrix of the control variable. By simplifying equation (18) with the Lagrange multiplier method with constraints and constructing the Milhaton function, we can obtain: (19) The Hamiltonian function , solve equation (19), and use the method of taking the derivative and taking the extreme value to get the final control law: (20) in, is a positive definite matrix, and The Riccati equation The solution.

[0036] The optimal sequence of feedback control is obtained through iteration, and the optimal feedback coefficient matrix is , , the control quantity of the LQR controller for the autonomous driving vehicle is obtained as follows.

[0037] (twenty one) Will Substituting into equation (16), the optimal front wheel steering angle control law of the vehicle can be expressed as:

[0038] (twenty two) Among them, the weight matrix parameters of the LQR controller are and parameters Will have a significant impact on the performance of the entire controller. Generally determined based on test results and According to the lateral controller designed in this invention, the weight matrix and It can be expressed as: , where the matrix Each element in represents the importance of the corresponding control objective. Respectively represent the control system's emphasis on lateral distance error, lateral distance error change rate, heading error, and heading error change rate. The larger the value, the stronger the control degree of the system. The elements in the formula represent the degree of restriction of the control system on the control quantity. Since there is only one control quantity in this paper, that is, the front wheel turning angle of the vehicle, the elements in the formula is a fixed constant.

[0039] Optionally, in step S3, the system steady-state error is eliminated by a front wheel steering angle compensation controller, specifically: Substituting equation (22) into equation (10) and discretizing it, we can obtain: (twenty three) From formula (23), we can see that when LQR control is adopted, no matter No matter what value is taken, the system has a steady-state error, so the feedforward control is introduced to eliminate the steady-state error. Combining formula (21) with the front wheel angle compensation control, we can get: (twenty four) Among them, when hour, .

[0040] From formula (24), we can see that we need to calculate the appropriate , so that the system steady-state error is 0 to achieve the optimal control state. Simplifying equation (24), the system steady-state error equation is obtained as: (25) From formula (25), we can see that if the lateral error is required to be 0, we need for: (26) From formula (25), we can see that Calculating the curvature and simplifying it can get the angular error relative to the road surface. for: (27) because , and the heading error is , from which we can get And it is not affected by the feedforward amount.

[0041] because ,in is the road curvature, so in order to , the required front wheel steering angle compensation for: (28) in, Corresponding heading error The feedback gain.

[0042] Optionally, in step S4, a longitudinal dual PID controller is constructed to adjust the longitudinal velocity error and the position error respectively, specifically: Design a position PID controller based on the position error, and the output is the speed error compensation , the output form of the PID controller can be expressed as: , in is the proportional gain; is the integral gain; is the differential gain; For time; is the integral variable, and e is the input error signal of the PID controller. When it increases, the response speed of the system is accelerated, the steady-state error of the system is reduced, and the control accuracy and sensitivity of the system are improved; Used to reduce steady-state error; It can reduce overshoot and adjustment time and improve system stability.

[0043] In a second aspect, a vehicle lane change trajectory tracking error elimination device is provided, the device comprising: Lateral control module: includes LQR controller, trajectory preview controller and front wheel steering angle compensation controller; the LQR controller is used for path tracking, the trajectory preview controller is used to solve the time lag problem of the LQR controller, and the front wheel steering angle compensation controller is used to eliminate the system steady-state error.

[0044] Longitudinal control module: including a dual PID controller and a throttle / brake calibration table, wherein the dual PID controller is used to control the speed and position error of the vehicle, and the throttle / brake calibration table is used to control the acceleration and braking of the vehicle in real time; Trajectory planning module: uses quintic polynomial trajectory planning to generate lane-changing trajectories that meet different lane-changing scenarios; Understeering compensation module: adjusts the front wheel angle of the vehicle through the PID controller to solve the problem of vehicle understeering.

[0045] In a third aspect, a computer-readable storage medium includes a computer program or instructions, which, when executed on a computer, causes the computer to execute any method as described in the first aspect.

Claims

1. A method for eliminating vehicle lane change trajectory tracking error, characterized in that: The following steps are involved: S1, establish the two-degree-of-freedom path tracking error state space of the vehicle; S2, using quintic polynomial trajectory planning to meet lane-changing trajectories in different lane-changing scenarios; S3, introduces a trajectory preview controller to solve the time lag problem, builds a lateral LQR controller for path tracking, and eliminates the system steady-state error through the front wheel angle compensation controller to solve the problem of vehicle understeering; S4, construct a longitudinal dual PID controller to adjust the longitudinal velocity error and position error respectively to achieve accurate trajectory tracking.

2. The method for eliminating vehicle lane change trajectory tracking error according to claim 1, characterized in that: In step S1, the equation of the two-degree-of-freedom path tracking error state space is: ， matrix , and , determined by the vehicle dynamics parameters, specifically: ， ，A= ， ， ， in, is the state space of tracking error, is the lateral error of the vehicle, is the vehicle’s heading error, is the tangent angle of the error trajectory of the vehicle’s center of mass, is the front wheel slip angle, and is the cornering stiffness coefficient of the front and rear wheels, m is the vehicle body mass, is the moment of inertia about the Z axis, is the vehicle longitudinal velocity, is the distance from the center of mass to the front axle, is the distance from the center of mass to the rear axle, is the wheel angle.

3. The method for eliminating vehicle lane change trajectory tracking error according to claim 1, characterized in that: In step S2, the quintic polynomial trajectory planning satisfies the following conditions: Longitudinal displacement and lateral displacement About time The function expression is: ； The boundary conditions include that the position, velocity, and acceleration of the starting and ending points are all zero. The coefficients are determined by solving the linear equations by substituting the boundary conditions into the system. , , ensuring that the trajectory curvature is continuous and the jump is minimized, and obtaining a reference trajectory with lateral and longitudinal displacement constraints: 。 4. The method for eliminating vehicle lane change trajectory tracking error according to claim 1, characterized in that: The step S3 introduces a trajectory preview controller to solve the time lag problem, specifically: The trajectory preview controller controls the vehicle's front wheel angle in real time to track the trajectory points in the future. , the coordinate position information of the predicted tracking trajectory point is , To predict the horizontal and vertical coordinates of the tracking trajectory points, To predict the horizontal and vertical speeds of the tracking trajectory points, To predict the heading angle of the tracking trajectory point; The vehicle tracks the path at the current speed according to the tangent of the predicted tracking trajectory point, and the relationship between the current coordinate information of the vehicle and the coordinate position information of the predicted tracking trajectory point can be obtained as follows: 。 5. A method for eliminating vehicle lane change trajectory tracking errors according to claim 1 or 2, characterized in that: In step S3, the system steady-state error is eliminated by the front wheel steering angle compensation controller, specifically: Calculate the appropriate front wheel steering compensation , so that the system steady-state error is 0 to achieve the optimal control state, for: ， in, is the road curvature, Corresponding heading error The feedback gain.

6. The method for eliminating vehicle lane change trajectory tracking error according to claim 1, characterized in that: In step S4, a longitudinal dual PID controller is constructed to adjust the longitudinal velocity error and position error respectively, specifically: Design a position PID controller based on the position error, where the output is the speed error compensation , the output form of the PID controller can be expressed as: ， in is the proportional gain, is the integral gain, is the differential gain, For time, is the integral variable, and e is the input error signal of the PID controller.

7. A vehicle lane change trajectory tracking error elimination device, characterized in that: The device comprises: Lateral control module: including LQR controller, trajectory preview controller and front wheel angle compensation controller, wherein the LQR controller is used for path tracking, the trajectory preview controller is used to solve the time lag problem of the LQR controller, and the front wheel angle compensation controller is used to eliminate the steady-state error of the system; Longitudinal control module: including a dual PID controller and a throttle / brake calibration table, wherein the dual PID controller is used to control the speed and position error of the vehicle, and the throttle / brake calibration table is used to control the acceleration and braking of the vehicle in real time; Trajectory planning module: uses quintic polynomial trajectory planning to generate lane-changing trajectories that meet different lane-changing scenarios; Understeering compensation module: adjusts the front wheel angle of the vehicle through the PID controller to solve the problem of vehicle understeering.

8. A computer-readable storage medium, characterized in that: The method comprises a computer program or an instruction, which, when executed on a computer, causes the computer to execute the method according to any one of claims 1 to 6.

Images