MPC transverse, longitudinal and vertical path tracking method and system for autonomous vehicle and medium
Through the model predictive control (MPC) method, the coupling characteristics of roll motion and horizontal and vertical motion during vehicle path tracking are considered, and the problem of low conversion efficiency of decoupling control is solved, and effective control of vehicle motion and driving safety is improved. .
Patent Information
- Application Number
- CN202411454423.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-17
- Publication Date
- 2025-02-18
AI Technical Summary
The prior art fails to consider the impact of the coupling characteristics between roll motion and transverse longitudinal motion on vehicle motion control when tracking vehicle paths. The efficiency of decoupling control is too low, and the coordination, operability and driving safety of vehicle motion control are coordinated, operated and driving. There is still a need to improve.
Using the model predictive control (MPC) method, by initializing the simulation parameters of the vehicle, constructing the state space equation and output equation, constructing the output prediction model, and constructing the objective function and constraints based on the output predicted value, disturbance quantity and control quantity to solve the best control and update simulation parameters through simulation tools.
Effectively control vehicle roll movement, avoid instability and rollover, improve vehicle path tracking control performance under complex working conditions, and improve vehicle motion control coordination, operability and driving safety.
Smart Images
Figure CN119472646A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of autonomous driving, and in particular relates to a method, system and medium for MPC lateral and longitudinal vertical path tracking of an autonomous driving vehicle. Background Art
[0002] At present, autonomous vehicles and related technologies are mostly used for commercial purposes, especially in factory freight vehicles. However, these vehicles generally have a high center of gravity. When tracking the path at high speed, even a slight turn can easily cause the vehicle to tilt too much, leading to instability and rollover, which poses a serious life safety hazard to pedestrians and surrounding vehicles and easily causes great economic losses.
[0003] Therefore, it is necessary to study the rollover phenomenon during path tracking and control the conditions under which rollover may occur, so as to reduce the possibility of rollover accidents. The vehicle body control systems such as active suspension and active lateral stabilizer bar can actively provide active anti-roll moment for the vehicle body, adjust the size and direction of the lateral displacement moment under the same lateral acceleration, increase the vehicle rollover threshold, and thus avoid the vehicle from rolling over when driving at high speed. Therefore, they have been greatly developed in recent years.
[0004] However, the existing technology fails to consider the influence of the coupling characteristics between roll motion and lateral and longitudinal motion on vehicle motion control during vehicle path tracking. The decoupling control conversion efficiency is too low, and the coordination, operability and driving safety of vehicle motion control need to be improved. Summary of the invention
[0005] The purpose of the present invention is to provide a method, system and medium for MPC lateral and longitudinal path tracking of an autonomous driving vehicle, so as to solve the problems that the prior art fails to consider the influence of the coupling characteristics between the roll motion and the lateral and longitudinal motion during vehicle path tracking on the vehicle motion control, the decoupling control conversion efficiency is too low, and the coordination, operability and driving safety of the vehicle motion control need to be improved.
[0006] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for MPC lateral and longitudinal vertical path tracking of an autonomous driving vehicle, the method comprising: Initializing simulation parameters of the vehicle, the simulation parameters of the vehicle including: state quantity, disturbance quantity, control quantity and output quantity; constructing a state space equation and an output equation of the vehicle based on simulation parameters of the vehicle; Building an output prediction model based on the state space equation and output equation of the vehicle, wherein the output prediction model is used to predict the output value of the vehicle; Constructing an objective function and constraint conditions of the objective function based on the output prediction value of the vehicle and the disturbance amount and control amount in the simulation parameters of the vehicle; Solve the objective function based on the constraint conditions to obtain the optimal control quantity; The optimal control quantity is used as the control input of the vehicle, the vehicle is simulated based on a preset simulation tool, the simulation output of the vehicle is obtained, and the state of the simulation parameters is updated with the simulation output.
[0007] Preferably, the state quantities in the simulation parameters of the vehicle include: vehicle longitudinal acceleration, vehicle longitudinal velocity, vehicle lateral velocity, yaw angular velocity, lateral distance error, heading error, roll angular velocity and vehicle body roll angle; The disturbance amount in the simulation parameters of the vehicle includes: road curvature; The control quantities in the simulation parameters of the vehicle include: longitudinal desired acceleration, steering wheel angle and active anti-roll moment; The output quantities of the simulation parameters of the vehicle include: vehicle longitudinal speed, lateral distance error, heading error and vehicle body roll angle.
[0008] Preferably, constructing the state space equation and output equation of the vehicle based on the simulation parameters of the vehicle includes: Construct a dynamic model based on state quantity, disturbance quantity, control quantity and output quantity; The dynamic model is processed linearly and discretized to obtain the state space equation and output equation.
[0009] Preferably, the expressions of the state space equation and the output equation are: ; In the formula, A is the first coefficient matrix, B is the second coefficient matrix, C is the third coefficient matrix, D is the fourth coefficient matrix, x ( k ) is the k The state quantity at the sampling time, x ( k +1) is the k +1 state quantity at sampling time, y ( k ) is the k The output at the sampling time, u ( k ) is the k The control quantity at the sampling time, d ( k ) is the k The amount of disturbance at the sampling time.
[0010] Preferably, the expression of the output prediction model is: ; In the formula, Y is the output prediction value, For the k The sampling time is based on the k The state quantity at the sampling time and the k -1 The new state quantity constructed by the control quantity at the sampling time, is the fifth coefficient matrix, is the sixth coefficient matrix, is the seventh coefficient matrix, For the k The increment of the control quantity at the sampling time.
[0011] Preferably, the objective function is expressed as: ; in: ; In the formula, Q is the system output weight system matrix, R is the system control increment weight system matrix, T For transposition, The output quantity for reference, J is the objective function, H is the first intermediate quantity, q is the second intermediate quantity, d is the interference amount, To control the increment; The constraint conditions of the objective function include: longitudinal acceleration and the rate of change constraint of longitudinal acceleration, front wheel steering angle and the rate of change constraint of front wheel steering angle, and active anti-roll moment and the rate of change constraint of active anti-roll moment.
[0012] Preferably, the method further comprises: allocating the active anti-roll moment in the optimal control amount, comprising: Based on the active anti-roll moment on the front and rear axles of the vehicle, the roll moment distribution equation is constructed; Solve the distribution coefficient based on PID algorithm; Based on the distribution coefficient and the roll moment distribution equation, the active anti-roll moment distribution value is determined.
[0013] Preferably, the roll moment distribution equation is expressed as: ; The functional expression of the distribution of the active anti-roll moment is: .
[0014] In the formula, i m is the distribution coefficient,u f is the active anti-roll moment of the front axle, u r is the active anti-roll moment of the rear axle, M t is the active anti-roll moment, u is the active anti-roll moment of the vehicle, k p is the proportionality coefficient, k i is the integration coefficient, k d is the differential coefficient, is the reference roll angle, is the roll angle, is the centroid height, m is the vehicle mass, For vehicles around x The yaw moment of inertia of the axis rotation, is the vehicle's roll stiffness coefficient, is the roll damping coefficient of the vehicle, is the vehicle longitudinal acceleration, is the roll angular acceleration, is the roll angular velocity, is the vehicle longitudinal velocity, is the yaw angular velocity.
[0015] In a second aspect, the present invention provides an MPC lateral, longitudinal and vertical path tracking system for an autonomous driving vehicle, the system being used to implement the above-mentioned MPC lateral, longitudinal and vertical path tracking method for an autonomous driving vehicle, the system comprising: An initialization module, used to initialize the simulation parameters of the vehicle, wherein the simulation parameters of the vehicle include: state quantity, disturbance quantity, control quantity and output quantity; an equation building module for building a state space equation and an output equation of the vehicle based on simulation parameters of the vehicle; A model building module, used to build an output prediction model based on the state space equation and output equation of the vehicle, wherein the output prediction model is used to predict the output value of the vehicle; A function building module, used to build an objective function and constraint conditions of the objective function based on the output prediction value of the vehicle and the disturbance amount and control amount in the simulation parameters of the vehicle; Function solving module, used to solve the objective function based on the constraint conditions to obtain the optimal control quantity; The vehicle simulation module is used to use the optimal control quantity as the control input of the vehicle, simulate the vehicle based on a preset simulation tool, obtain the simulation output of the vehicle, and update the state of the simulation parameters with the simulation output.
[0016] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned MPC lateral, longitudinal and vertical path tracking method for an autonomous driving vehicle.
[0017] Beneficial effects: The present invention effectively controls the vehicle's roll motion to prevent the vehicle from becoming unstable and rolling over. At the same time, it can improve the vehicle's path tracking control performance under complex working conditions, effectively solve the problem of low conversion efficiency of decoupling control, and improve the coordination, operability and driving safety of vehicle motion control. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings: Figure 1 It is a flow chart of a method for MPC lateral, longitudinal and vertical path tracking of an autonomous driving vehicle provided by an embodiment of the present invention; Figure 2 It is a block diagram of an MPC lateral and vertical path tracking system for an autonomous driving vehicle provided by one embodiment of the present invention. DETAILED DESCRIPTION
[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in combination with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.
[0020] Embodiment 1 Figure 1 FIG. 1 is a flow chart of a method for MPC lateral and longitudinal vertical path tracking of an autonomous driving vehicle provided by an embodiment of the present invention. Figure 1 As shown, this embodiment provides an MPC lateral and longitudinal vertical path tracking method for an autonomous driving vehicle, wherein MPC (Model Predictive Control, referred to as MPC) is model predictive control, and the method includes: Step S10: Initialize the simulation parameters of the vehicle, which include: state quantity, disturbance quantity, control quantity and output quantity.
[0021] In this embodiment, the state quantities in the simulation parameters of the vehicle include: vehicle longitudinal acceleration, vehicle longitudinal velocity, vehicle lateral velocity, yaw angular velocity, lateral distance error, heading error, roll angular velocity and body roll angle; The disturbance amount in the simulation parameters of the vehicle includes: road curvature; The control quantities in the simulation parameters of the vehicle include: longitudinal desired acceleration, steering wheel angle and active anti-roll moment; The output quantities of the simulation parameters of the vehicle include: vehicle longitudinal speed, lateral distance error, heading error and vehicle body roll angle.
[0022] Step S20: constructing the state space equation and output equation of the vehicle based on the simulation parameters of the vehicle.
[0023] In this embodiment, the state space equation and output equation of the vehicle are constructed based on the simulation parameters of the vehicle, including: Step S201: constructing a dynamic model based on state quantity, disturbance quantity, control quantity and output quantity; The dynamic model includes: vehicle lateral motion, vehicle longitudinal motion, vehicle yaw motion, vehicle roll motion and tracking error equation; The expression for the lateral motion of the vehicle is: (1); The expression for the longitudinal motion of the vehicle is: (2); The expression of the vehicle yaw motion is: (3); The expression for the vehicle roll motion is: (4); The expression of the tracking error equation is: (5); (6); In formula (1) to formula (6), f , r Represent the front and rear wheels respectively. is the lateral velocity of the vehicle body, is the longitudinal velocity of the vehicle body, is the yaw angle, is the front wheel slip angle, is the rear wheel slip angle, is the distance from the front axle to the vehicle's center of mass, b is the distance from the rear axle to the vehicle's center of mass, m Indicates the vehicle mass, g represents the acceleration due to gravity, take , represents the yaw moment of inertia of the vehicle around the z-axis, is the slip angle, is the front wheel turning angle, is the roll angle, h is the height of the vehicle's center of mass, , are the front wheel cornering stiffness and the rear wheel cornering stiffness, and are the vehicle's roll stiffness coefficient and roll damping coefficient, is the active anti-roll moment, is the road curvature, is the lateral distance error, is the heading deviation, is the lateral error change rate, is the heading error change rate, is the vehicle longitudinal acceleration, is the yaw angular velocity, is the roll angular acceleration, is the roll angular velocity, is the yaw angular acceleration, is the vehicle longitudinal velocity, is the vehicle longitudinal acceleration, is the second-order rate of change of velocity, is the time constant, is the expected longitudinal acceleration.
[0024] Step S202: Perform linear processing and discrete processing on the dynamic model to obtain a state space equation and an output equation.
[0025] In this embodiment, the state quantities of the dynamic model are the vehicle longitudinal acceleration, the vehicle longitudinal velocity, the vehicle lateral velocity, the yaw angular velocity, the lateral distance error, the heading error, the roll angular velocity, and the body roll angle, which are recorded as: The control variables are the longitudinal desired acceleration, steering wheel angle, and active anti-roll moment, which are recorded as: The interference quantity is the road curvature, which is recorded as: The output is the vehicle longitudinal speed, lateral distance error, heading error, and body roll angle, which are recorded as: , where T is the transpose.
[0026] By using Taylor expansion linearization and forward Euler method discretization, we can get the state space equation and output equation.
[0027] Wherein, the expressions of the state space equation and the output equation are: (7); In formula (7), A is the first coefficient matrix, B is the second coefficient matrix, C is the third coefficient matrix, D is the fourth coefficient matrix, x ( k ) is the k The state quantity at the sampling time, x ( k +1) is the k +1 state quantity at sampling time, y ( k ) is the k The output at the sampling time, u ( k ) is the k The control quantity at the sampling time, d ( k ) is the k The amount of disturbance at the sampling time.
[0028] In formula (7): ; in, T 0 is the sampling time.
[0029] ; ; .
[0030] Step S30: constructing an output prediction model based on the state space equation and the output equation of the vehicle, wherein the output prediction model is used to predict the output value of the vehicle.
[0031] In this embodiment, for formula (7), the state quantity and control quantity of the original discrete state space expression are used as new state quantities, and the new state quantity is constructed as follows: ; Then, the state equation with the control increment as the control quantity and the state quantity and control quantity of the original discrete state space expression as the new state quantity is as follows: ; in, ; ; in, is the first intermediate coefficient matrix, is the second intermediate coefficient matrix, is the third intermediate coefficient matrix, is the fourth intermediate coefficient matrix.
[0032] set up For the prediction time domain, the prediction time length for the system; To control the time domain, the system predicts the control length; according to the state space equation and output equation finally obtained by the model, the model is predicted, and the predicted output expression of the system at time k can be derived as follows: (8); In formula (8), Y is the output prediction value, For the k The sampling time is based on the k The state quantity at the sampling time and the k -1 The new state quantity constructed by the control quantity at the sampling time, is the fifth coefficient matrix, is the sixth coefficient matrix, is the seventh coefficient matrix, For the k The increment of the control quantity at the sampling time.
[0033] In formula (8): ; ; ; .
[0034] Step S40: constructing an objective function and constraint conditions of the objective function based on the output prediction value of the vehicle and the disturbance amount and control amount in the simulation parameters of the vehicle.
[0035] In this embodiment, the objective function is set up to ensure that the vehicle can quickly and smoothly track the desired path, speed and roll angle. Therefore, it is necessary to consider the impact of the deviation of the system output and the change of the control amount on the system.
[0036] The objective function is set as follows: (9); In formula (9), The output of the reference is the reference longitudinal velocity, lateral error, attitude angle error and reference roll angle. Time to Output vector at time for: (10); In formula (10), is the reference output at time k+1.
[0037] Then the objective function can be written as: (11); in, ; In formula (11), Q is the system output weight system matrix, R is the system control increment weight system matrix, T For transposition, The output quantity for reference, J is the objective function, H is the first intermediate quantity, q is the second intermediate quantity, d is the interference amount, To control the increment.
[0038] In this embodiment, the constraints of the objective function include: longitudinal acceleration and its rate of change constraint, front wheel steering angle and its rate of change constraint, and active anti-roll moment and its rate of change constraint.
[0039] The constraint expression is: (12); In formula (12), is the lower limit of the control amount, is the upper limit of the control amount, is the lower limit of the control variable change rate, It is the upper limit of the control variable change rate.
[0040] Therefore, the MPC problem can be transformed into a standard quadratic programming problem: (13); in, .
[0041] in, is a lower triangular matrix with all 1s, is the identity matrix, for .
[0042] Step S50: Solve the objective function based on the constraint conditions to obtain the optimal control amount.
[0043] In this embodiment, by solving formula (12), a series of control input increments in the control time domain can be obtained: (14); The first control in the control sequence It is converted into actual control quantity (optimal control quantity), that is, converted into actual front wheel steering angle, longitudinal acceleration and active anti-roll torque to control the vehicle forward for status update.
[0044] Step S60: Using the optimal control amount as the control input of the vehicle, simulating the vehicle based on a preset simulation tool to obtain a simulated output of the vehicle, and updating the state of the simulation parameters with the simulated output.
[0045] In this embodiment, the preset simulation tool uses MWROKS / Sysplorer, and MWROKS / Sysplorer uses Modelica language for modeling. Modelica is a modeling language for physical systems, which is designed to support the development of efficient model libraries and model reuse. This is a modern language based on non-causal models and mathematical formulas and object-oriented structures to promote the reuse of modeling knowledge.
[0046] During the simulation process, you can check the vehicle model predictive control effect by viewing the MWROKS / Sysplorer animation model and adjust the parameter settings based on the control performance.
[0047] As a further optimization of this embodiment, the method further includes: allocating the active anti-roll moment in the optimal control amount, including: Step a10: constructing a roll moment distribution equation based on the active anti-roll moment on the front and rear axles of the vehicle; Step a20: solving the distribution coefficient based on the PID algorithm; Step a30: Determine the active anti-roll moment distribution value based on the distribution coefficient and the roll moment distribution equation.
[0048] In this embodiment, the control variable active anti-roll moment solved by the MPC lateral and vertical path tracker considering the roll motion is a cooperative effect moment generated by the suspension force and cannot directly act on the vehicle. To this end, the present invention designs an active anti-roll moment distribution controller so that the active anti-roll moment satisfies the following relationship with the active lateral stabilizer bar of the front and rear axles while not affecting the vehicle's maneuverability.
[0049] (15); The roll moment on the front and rear axles has an infinite number of distribution schemes under the premise of satisfying the above formula. In view of this, the present invention adopts a PID controller to dynamically distribute the front and rear roll moments with the goal of further improving the vehicle roll characteristics. This method uses the PID control output distribution coefficient Roll moment distribution is performed to further reduce the roll situation during the high-speed vehicle tracking path.
[0050] (16); In formula (15) and formula (16), i m is the distribution coefficient, u f is the active anti-roll moment of the front axle, u r is the active anti-roll moment of the rear axle, M t is the active anti-roll moment, u is the active anti-roll moment of the vehicle, k p is the proportionality coefficient, k i is the integration coefficient, k d is the differential coefficient, is the reference roll angle, is the roll angle, is the centroid height, m is the vehicle mass, For vehicles around x The yaw moment of inertia of the axis rotation, is the vehicle's roll stiffness coefficient, is the vehicle's roll damping coefficient, is the vehicle longitudinal acceleration, is the roll angular acceleration, is the roll angular velocity, is the vehicle longitudinal velocity, is the yaw angular velocity.
[0051] The present invention can realize path tracking under complex working conditions by taking the roll motion into consideration for the path tracking control of the autonomous driving vehicle, effectively control the roll motion of the vehicle, and avoid the vehicle from becoming unstable and rolling over. At the same time, it can improve the vehicle path tracking control performance under complex working conditions, effectively solve the problem of low conversion efficiency of decoupling control, and improve the coordination, operability and driving safety of vehicle motion control.
[0052] Embodiment 2 Figure 2 is a block diagram of an MPC lateral and vertical path tracking system for an autonomous driving vehicle provided by an embodiment of the present invention, such as Figure 2 As shown, this embodiment provides an MPC lateral, longitudinal and vertical path tracking system for an autonomous driving vehicle, the system is used to implement the MPC lateral, longitudinal and vertical path tracking method for an autonomous driving vehicle in Embodiment 1, and the system includes: An initialization module, used to initialize the simulation parameters of the vehicle, wherein the simulation parameters of the vehicle include: state quantity, disturbance quantity, control quantity and output quantity; an equation building module for building a state space equation and an output equation of the vehicle based on simulation parameters of the vehicle; A model building module, used to build an output prediction model based on the state space equation and output equation of the vehicle, wherein the output prediction model is used to predict the output value of the vehicle; A function building module, used to build an objective function and constraint conditions of the objective function based on the output prediction value of the vehicle and the disturbance amount and control amount in the simulation parameters of the vehicle; Function solving module, used to solve the objective function based on the constraint conditions to obtain the optimal control quantity; The vehicle simulation module is used to use the optimal control quantity as the control input of the vehicle, simulate the vehicle based on a preset simulation tool, obtain the simulation output of the vehicle, and update the state of the simulation parameters with the simulation output.
[0053] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the MPC lateral and vertical path tracking method of the autonomous driving vehicle in embodiment one is implemented.
[0054] The present invention effectively controls the vehicle's roll motion to prevent the vehicle from becoming unstable and rolling over. At the same time, it can improve the vehicle's path tracking control performance under complex working conditions, effectively solve the problem of low conversion efficiency of decoupling control, and improve the coordination, operability and driving safety of vehicle motion control.
[0055] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.
[0056] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0057] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.
Claims
1. A method for MPC lateral and vertical path tracking of an autonomous driving vehicle, characterized in that: The method comprises: Initializing simulation parameters of the vehicle, the simulation parameters of the vehicle including: state quantity, disturbance quantity, control quantity and output quantity; constructing a state space equation and an output equation of the vehicle based on simulation parameters of the vehicle; Building an output prediction model based on the state space equation and output equation of the vehicle, wherein the output prediction model is used to predict the output value of the vehicle; Constructing an objective function and constraint conditions of the objective function based on the output prediction value of the vehicle and the disturbance amount and control amount in the simulation parameters of the vehicle; Solve the objective function based on the constraint conditions to obtain the optimal control quantity; The optimal control quantity is used as the control input of the vehicle, the vehicle is simulated based on a preset simulation tool, the simulation output of the vehicle is obtained, and the state of the simulation parameters is updated with the simulation output.
2. The MPC lateral and vertical path tracking method for an autonomous driving vehicle according to claim 1, characterized in that: The state quantities in the simulation parameters of the vehicle include: vehicle longitudinal acceleration, vehicle longitudinal velocity, vehicle lateral velocity, yaw angular velocity, lateral distance error, heading error, roll angular velocity and body roll angle; The disturbance amount in the simulation parameters of the vehicle includes: road curvature; The control quantities in the simulation parameters of the vehicle include: longitudinal desired acceleration, steering wheel angle and active anti-roll moment; The output quantities of the simulation parameters of the vehicle include: vehicle longitudinal speed, lateral distance error, heading error and vehicle body roll angle.
3. The MPC lateral and vertical path tracking method for an autonomous driving vehicle according to claim 2, characterized in that: The state space equations and output equations of the vehicle are constructed based on the simulation parameters of the vehicle, including: Construct a dynamic model based on state quantity, disturbance quantity, control quantity and output quantity; The dynamic model is processed linearly and discretized to obtain the state space equation and output equation.
4. The MPC lateral and vertical path tracking method for an autonomous driving vehicle according to claim 3, characterized in that: The expressions of the state space equation and the output equation are: ； In the formula, A is the first coefficient matrix, B is the second coefficient matrix, C is the third coefficient matrix, D is the fourth coefficient matrix, x ( k ) is the k The state quantity at the sampling time, x ( k +1) is the k +1 state quantity at sampling time, y ( k ) is the k The output at the sampling time, u ( k ) is the k The control quantity at the sampling time, d ( k ) is the k The amount of disturbance at the sampling time.
5. The MPC lateral and vertical path tracking method for an autonomous driving vehicle according to claim 4, characterized in that: The expression of the output prediction model is: ； In the formula, Y is the output prediction value, For the k The sampling time is based on the k The state quantity at the sampling time and the k -1 The new state quantity constructed by the control quantity at the sampling time, is the fifth coefficient matrix, is the sixth coefficient matrix, is the seventh coefficient matrix, For the k The increment of the control quantity at the sampling time.
6. The MPC lateral and vertical path tracking method for an autonomous driving vehicle according to claim 5, characterized in that: The expression of the objective function is: ； in; ； In the formula, Q is the system output weight system matrix, R is the system control increment weight system matrix, T For transposition, The output quantity for reference, J is the objective function, H is the first intermediate quantity, q is the second intermediate quantity, d is the interference amount, To control the increment; The constraint conditions of the objective function include: longitudinal acceleration and the rate of change constraint of longitudinal acceleration, front wheel steering angle and the rate of change constraint of front wheel steering angle, and active anti-roll moment and the rate of change constraint of active anti-roll moment.
7. The MPC lateral and vertical path tracking method for an autonomous driving vehicle according to claim 2, characterized in that: The method further includes: allocating the active anti-roll moment in the optimal control amount, including: Based on the active anti-roll moment on the front and rear axles of the vehicle, the roll moment distribution equation is constructed; Solve the distribution coefficient based on PID algorithm; Based on the distribution coefficient and the roll moment distribution equation, the distribution of the active anti-roll moment is determined.
8. The MPC lateral and vertical path tracking method for an autonomous driving vehicle according to claim 7, characterized in that: The roll moment distribution equation is expressed as: ； The functional expression of the distribution of the active anti-roll moment is: ； In the formula, i m is the distribution coefficient, u f is the active anti-roll moment of the front axle, u r is the active anti-roll moment of the rear axle, M t is the active anti-roll moment, u is the active anti-roll moment of the vehicle, k p is the proportionality coefficient, k i is the integration coefficient, k d is the differential coefficient, is the reference roll angle, is the roll angle, is the centroid height, m is the vehicle mass, For vehicles around x The yaw moment of inertia of the axis rotation, is the vehicle's roll stiffness coefficient, is the roll damping coefficient of the vehicle, is the vehicle longitudinal acceleration, is the roll angular acceleration, is the roll angular velocity, is the vehicle longitudinal velocity, is the yaw angular velocity.
9. An MPC lateral, longitudinal and vertical path tracking system for an autonomous driving vehicle, the system being used to implement the MPC lateral, longitudinal and vertical path tracking method for an autonomous driving vehicle according to any one of claims 1 to 8, characterized in that: The system comprises: An initialization module, used to initialize the simulation parameters of the vehicle, wherein the simulation parameters of the vehicle include: state quantity, disturbance quantity, control quantity and output quantity; an equation building module for building a state space equation and an output equation of the vehicle based on simulation parameters of the vehicle; A model building module, used to build an output prediction model based on the state space equation and output equation of the vehicle, wherein the output prediction model is used to predict the output value of the vehicle; A function building module, used to build an objective function and constraint conditions of the objective function based on the output prediction value of the vehicle and the disturbance amount and control amount in the simulation parameters of the vehicle; Function solving module, used to solve the objective function based on the constraint conditions to obtain the optimal control quantity; The vehicle simulation module is used to use the optimal control quantity as the control input of the vehicle, simulate the vehicle based on a preset simulation tool, obtain the simulation output of the vehicle, and update the state of the simulation parameters with the simulation output.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the MPC lateral, longitudinal and vertical path tracking method for an autonomous driving vehicle described in any one of claims 1-8 is implemented.