A vehicle path tracking control method, device, medium, and product

Abstract

This application discloses a vehicle path tracking control method, device, medium, and product, relating to the field of path tracking. The method includes: constructing an integrated predictive model based on a vehicle dynamics model using nonlinear tire forces and actuation system characteristics; determining an optimization objective function based on vehicle path tracking performance and actuation optimization using control input and output variables in the integrated predictive model; determining the real-time dynamic stability domain boundary of the vehicle based on the control input variables, vehicle state, and elliptic parameter functions in the integrated predictive model; and solving the integrated predictive model using a rolling time-domain optimization method based on the optimization objective function and constraints to determine the front wheel steering angle and torque of each wheel; the constraints at least include the real-time dynamic stability domain boundary of the vehicle. This application enables dynamic real-time vehicle control, improving vehicle path tracking accuracy.

Description

Technical Field

[0001] This application relates to the field of path tracking, and in particular to a vehicle path tracking control method, device, medium, and product. Background Technology

[0002] Intelligent vehicle path tracking control is crucial for ensuring vehicle safety and stability. Time-varying vehicle speed and road conditions affect vehicle status. Existing path tracking and stability control can be categorized into decoupled and integrated methods. Decoupled methods involve a path tracking controller and a stability controller working together. The path tracking controller calculates the front wheel steering angle, while the stability controller estimates the vehicle's additional yaw moment through an observer and distributes wheel braking torque based on the vehicle's steering characteristics. However, when tire forces approach saturation, decoupled control methods can cause path tracking and stability control objectives to influence and constrain each other, deteriorating path tracking performance. Integrated methods, on the other hand, are based on rolling time-domain optimization methods. They combine nonlinear tire forces, vehicle stability constraints, and actuator characteristics to achieve rolling optimization of front wheel steering angle and wheel torque within constraints, ensuring both path tracking performance and driving stability. For integrated path tracking and stability control methods, linear stability constraints are typically used to achieve stability control. However, these conservative constraints increase the control trigger frequency and limit the amplitude and rate of change of actuation inputs, thus restricting the vehicle's path tracking performance on low-adhesion surfaces. Existing path tracking control methods rely on conservative stability conditions to trigger braking, maintaining vehicle lateral stability during path tracking. However, under extreme conditions, these methods frequently apply braking to maintain vehicle stability rather than tracking the desired path, leading to deterioration in path tracking performance. Therefore, a method capable of dynamic, real-time vehicle control is urgently needed to improve path tracking accuracy. Summary of the Invention

[0003] The purpose of this application is to provide a vehicle path tracking control method, device, medium, and product that can dynamically and in real time control vehicles and improve vehicle path tracking accuracy.

[0004] To achieve the above objectives, this application provides the following solution.

[0005] In a first aspect, this application provides a vehicle path tracking control method, including: constructing a vehicle dynamics model.

[0006] An integrated prediction model is constructed based on the vehicle dynamics model using nonlinear tire forces and actuation system characteristics; the actuation system characteristics include the actuation system time constant and the actuation system gain.

[0007] The optimization objective function is determined based on the control input and output variables in the integrated prediction model, taking into account vehicle path tracking performance and motion optimization.

[0008] The real-time dynamic stability domain boundary of the vehicle is determined based on the control input variables, vehicle state, and elliptic parameter functions in the integrated prediction model.

[0009] Based on the aforementioned objective function and constraints, the integrated prediction model is solved using a rolling time-domain optimization method to determine the front wheel steering angle and the torque of each wheel of the vehicle; the constraints include at least the real-time dynamic stability domain boundary of the vehicle.

[0010] Optionally, the formula for the vehicle dynamics model is as follows.

[0011]

[0012] Among them, v x v is the longitudinal vehicle speed. y Let r be the lateral velocity of the vehicle's center of gravity, and ψ be the yaw rate of the car. e For the horizontal swing angle, This is the derivative of the vertical position in the global coordinate system. This is the derivative of the horizontal coordinate system in the global coordinate system. Let be the derivative of the yaw angle.

[0013] Optionally, the spatial state equation of the integrated prediction model is:

[0014] The state variable x is:

[0015] The control input variable u is:

[0016] The disturbance variable v is:

[0017] The output variable y is:

[0018] in, for The corresponding matrix, For the differential form of the state variables, For the system matrix, For the control matrix, Here is the perturbation matrix. The output matrix is ​​ij = fl, fr, rl, rr, which represent the front left, front right, rear left, and rear right positions of the vehicle, respectively. y Let r be the lateral velocity of the vehicle's center of mass, r be the yaw rate of the car, and δ be the lateral velocity of the vehicle's center of mass. f For the front wheel steering angle, s i This refers to the tire's slip angle. Let X be the translational velocity of the wheel center. e ,Ye ,ψ e Let a be the vehicle's longitudinal position, lateral position, and yaw angle in the global coordinate system. e The acceleration of the vehicle's center of gravity. For the desired front wheel steering angle, T ij For the torque of the wheel, and All are relaxation factors. For the lateral velocity v y The perturbation variable, ζ r Let r be the disturbance variable. This indicates the longitudinal force of each tire. and The vertical position X in the global coordinate system e Horizontal position Y e The disturbance term, ζ res The term represents the disturbance of driving resistance, and T represents the matrix transpose.

[0019] Optionally, before determining the optimization objective function based on vehicle path tracking performance and motion optimization according to the control input variables and output variables in the integrated prediction model, the method further includes: discretizing the integrated prediction model using a zero-order hold.

[0020] Optionally, the formula for the optimization objective function is as follows.

[0021]

[0022] in, These are the output variable, the control input variable, and the control variable u from the previous time step. p In the prediction time domain N p The weight coefficients are a diagonal matrix within the matrix, and J is the objective function. To control the input variables in the control time domain N c The control input matrix formed internally, For matrix transpose, For the output variable in N p Internally formed matrix, The control variable at the previous time step N c The matrix formed within.

[0023] Optionally, the boundary of the vehicle's real-time dynamic stability domain is [defined].

[0024]

[0025] in, The lateral velocity v of the car's center of mass y Reference value at the reference point. This represents the reference value of the car's yaw rate r at the reference point, g(v y ,r) represents the real-time dynamic stability domain of the vehicle. This represents the real-time dynamic stability domain of the vehicle after approximate reflection transformation.

[0026] Optionally, the integrated prediction model is solved using a rolling time-domain optimization method based on the optimization objective function and constraints to determine the front wheel steering angle and the torque of each wheel of the vehicle, as specified in the formula.

[0027] u * =argminJ

[0028]

[0029] u l ≤u(k)≤u u ;x l ≤x(k)≤x u

[0030]

[0031] Among them, u * Let J be the optimal control sequence for the vehicle's front wheel steering angle and wheel torque, where J is the objective function and k is the prediction step size. For the real number field, f a f is a function of the major axis. b It is a minor axis function. Let u(k) be the rotation angle function, x(k) be the control input variable for each prediction step, x(k) be the state variable for each prediction step, x(1) be the state variable for the first prediction step, and x(t) be the initial value of the state variable. The lower and upper boundaries of the state variable and the control input variable are respectively represented by u(k) and x(t). l ,u u ,x l ,x u The upper boundary of wheel torque Represented as: in This is the maximum wheel driving torque that the actuator can provide. R is the vertical force of the vehicle tires, μ is the road adhesion coefficient, and R is the vertical force of the tires. e The effective radius of the tire. and Let be the allowable set of state variables and control input variables under constrained optimization conditions. The state terminal constraint is the equilibrium point.

[0032] Secondly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the vehicle path tracking control method described above.

[0033] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the vehicle path tracking control method described above.

[0034] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the vehicle path tracking control method described above.

[0035] According to the specific embodiments provided in this application, this application has the following technical effects.

[0036] This application provides a vehicle path tracking control method, device, medium, and product. By integrating the control input variables and geometric boundaries in the prediction model, the real-time dynamic stability domain boundary of the vehicle is determined, which expands the existing relatively conservative stability domain. The stability domain characterized by geometric parameters is integrated into the constraints of vehicle path tracking control using the geometric boundary. Under the premise of ensuring driving stability, the vehicle state fluctuation caused by braking intervention under high-speed and low-adhesion conditions is reduced, thereby realizing dynamic real-time vehicle control and improving vehicle path tracking accuracy. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is an application environment diagram of a vehicle path tracking control method according to an embodiment of this application.

[0039] Figure 2 This is a flowchart illustrating a vehicle path tracking control method provided in one embodiment of this application.

[0040] Figure 3 This is an overall flowchart of a vehicle path tracking control method provided in an embodiment of this application.

[0041] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0042] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0043] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0044] Intelligent vehicles: They can control the vehicle autonomously by generating control signals such as steering and braking based on environmental perception results without driver intervention.

[0045] Stability: The ability of a car to follow a given path and to resist external disturbances and maintain stable driving.

[0046] The vehicle path tracking control method provided in this application embodiment can be applied to, for example, Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on another server. Terminal 102 can send vehicle information to be processed to server 104. After receiving the vehicle information, server 104 constructs a vehicle dynamics model for the vehicle information; based on the vehicle dynamics model, it constructs an integrated prediction model using nonlinear tire forces and actuation system characteristics; the actuation system characteristics include the actuation system time constant and the actuation system gain; based on the control input variables and output variables in the integrated prediction model, it determines the optimization objective function based on vehicle path tracking performance and actuation optimization; based on the control input variables and geometric boundaries in the integrated prediction model, it determines the real-time dynamic stability domain boundary of the vehicle; based on the optimization objective function and constraints, it solves the integrated prediction model using a rolling time-domain optimization method to determine the front wheel steering angle and the torque of each wheel; the constraints are the real-time dynamic stability domain boundary of the vehicle. Server 104 can feed back the obtained front wheel steering angle and torque of each wheel to terminal 102. In addition, in some embodiments, the vehicle path tracking control method can also be implemented by server 104 or terminal 102 alone. For example, terminal 102 can directly track and control the vehicle to be processed, or server 104 can obtain the vehicle to be processed from the data storage system, construct a vehicle dynamics model, and then perform tracking and control.

[0047] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0048] In one exemplary embodiment, such as Figure 2 and Figure 3 As shown, a vehicle path tracking control method is provided. This method is executed by a computer device, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 201 to 205. Wherein:

[0049] Step 201: Construct the vehicle dynamics model.

[0050] Step 202: Construct an integrated prediction model based on the vehicle dynamics model using nonlinear tire forces and actuation system characteristics; the actuation system characteristics include the actuation system time constant and the actuation system gain.

[0051] Step 203: Determine the optimization objective function based on vehicle path tracking performance and motion optimization according to the control input variables and output variables in the integrated prediction model.

[0052] Step 204: Determine the real-time dynamic stability domain boundary of the vehicle based on the control input variables, vehicle state, and elliptic parameter function in the integrated prediction model.

[0053] Step 205: Based on the objective function and constraints, solve the integrated prediction model using the rolling time-domain optimization method to determine the front wheel steering angle and the torque of each wheel; the constraints include at least the real-time dynamic stability domain boundary of the vehicle. In practical applications, constraints also include upper and lower limits of the state, upper and lower limits of the actuators, etc.

[0054] By implementing steps 201 to 205 above, the vehicle can be dynamically and in real time controlled, improving the accuracy of vehicle path tracking.

[0055] In an exemplary embodiment of this application, before determining the optimization objective function based on vehicle path tracking performance and motion optimization according to the control input variables and output variables in the integrated prediction model, the method further includes: discretizing the integrated prediction model using a zero-order hold.

[0056] In one exemplary embodiment of this application, a proof process in practical application is provided to further illustrate the solution of this application. For example... Figure 2 As shown, the details are as follows.

[0057] Part 1: Integrated Predictive Models

[0058] The lateral tire force is represented using LuGre's combined slip tire model. LuGre tire force utilizes relative tire longitudinal velocity. and lateral speed Indicates the combined slip characteristics of the tire Represented as: in, ω ij R e and s i Let i represent the translational velocity, rotational velocity, effective radius, and slip angle of the front and rear wheels at positions ij = fl, fr, rl, rr (left front fl, right front fr, left rear rl, right rear rr), respectively. Since relative velocity is included, the wheel torque is expressed as...

[0059]

[0060] in, J w T ij , These represent the difference in translational acceleration at the wheel center, the wheel's moment of inertia, the wheel torque, and the longitudinal tire force, respectively. R is the difference in translational acceleration at the wheel center. e Given the effective radius of the tires, the acceleration *a* of the vehicle's center of mass. e It is represented as.

[0061]

[0062] Where m, f, C d , ρ e A r , and v x These represent the vehicle's mass, rolling drag coefficient, air drag coefficient, air density, frontal area, and longitudinal speed, respectively. g is the acceleration due to gravity. The first-order inertial element is used to represent the characteristics of the vehicle's acceleration system.

[0063]

[0064] Among them, T a K is the time constant of the driving system. a It is the system gain that drives the system. This is the derivative of the vehicle's actual acceleration. This represents the vehicle's actual acceleration. Among them, Figure 2 v in e Let be the vehicle speed. The lateral and longitudinal dynamics of the vehicle are expressed as follows.

[0065]

[0066] Among them, v y , r, and δ f These represent lateral speed, yaw rate, and front wheel steering angle, respectively. z ,l f ,l r W and W represent the vehicle's moment of inertia, the longitudinal distance from the front axle and rear axle to the vehicle's center of gravity, and the wheel width, respectively. For lateral acceleration, This represents the sum of the lateral forces exerted by the tires on the front axle. This represents the total lateral tire force on the rear axle. This represents the sum of the longitudinal tire forces on the front axle. The yaw acceleration is... This represents the difference between the left front lateral tire force and the right front lateral tire force. This represents the difference between the left rear longitudinal tire force and the right rear longitudinal tire force. This refers to the longitudinal force on the right front tire. Additionally... For the left front longitudinal tire force, This represents the sum of the longitudinal tire forces on the rear axle. For the longitudinal tire force of the left rear wheel, The longitudinal tire force of the right rear wheel. The lateral force on the left front wheel. The lateral force on the right front wheel. The lateral force on the left rear wheel. The lateral tire force of the right rear wheel is given, and the steering system characteristics are also represented by a first-order inertial element.

[0067]

[0068] K is the differential of the front wheel steering angle. δ T δ ,and These represent the steering system time constant, steering system gain, and desired front wheel steering angle, respectively. The tire force is calculated using a first-order Taylor expansion at the equilibrium point. The expanded form is written as:

[0069] For the equilibrium point of the stability region, This is the equilibrium point of the tire forces. The lateral tire force of the wheel. The tire force at the equilibrium point, For tire force in wheel speed difference Partial differential at point, Let be the partial derivative of the tire force at point s, where s is the tire slip angle. The lateral tire force is denoted as , and the vehicle tire slip angle is denoted as .

[0070]

[0071] It can be represented as: This represents the rate of change of the tire slip angle. For γ i The partial derivative at r, For γ i In v y The partial derivative at point s. f Let be the front axle tire slip angle. The vehicle's kinematic equations, or dynamic model, in the global coordinate system are expressed as: X e ,Y e and ψ e These represent the vehicle's longitudinal position, lateral position, and yaw angle in the global coordinate system, respectively. This is the derivative of the vehicle's longitudinal position in the global coordinate system, i.e., its longitudinal velocity. This is the derivative of the horizontal coordinate system in the global coordinate system, i.e., the horizontal velocity. Let be the derivative of the yaw angle. Combining (1)-(6), an integrated prediction model is constructed, which is expressed using the spatial state equation. The state variable x is represented as follows.

[0072]

[0073] Among them, v y Let r be the lateral velocity of the car's center of mass, r be the car's yaw rate, and δ be the lateral velocity. f For the front wheel steering angle, s i This refers to the tire's slip angle. Let X be the translational velocity of the wheel center. e ,Y e ,ψ e Let a be the vehicle's longitudinal position, lateral position, and yaw angle in the global coordinate system. e This refers to the acceleration of the vehicle's center of gravity.

[0074] The control input variable u is represented as follows.

[0075]

[0076] in, For the desired front wheel steering angle, T ij For the torque of the wheel, and Let v be the relaxation factor. The perturbation variable v is defined as follows.

[0077]

[0078] in, ζ r Represents lateral velocity v y And the disturbance variable of yaw rate r, This indicates the longitudinal force of each tire. and Represents the vertical position X in the local coordinate system e Horizontal position Y e The disturbance term, ζ res The disturbance term represents the driving resistance.

[0079] The output variable y is defined as follows.

[0080]

[0081] Among them, v y Let r be the lateral velocity of the car's center of mass, r be the car's yaw rate, and δ be the lateral velocity. f For the front wheel steering angle, X e ,Y e ,ψ e This represents the vehicle's longitudinal position, lateral position, and yaw angle in the global coordinate system. for The corresponding matrix, For the differential form of the state variables, For the system matrix, For the control matrix, Here is the perturbation matrix. The output matrix is ​​ij = fl, fr, rl, rr, which represent the front left, front right, rear left, and rear right positions of the vehicle, respectively.

[0082] The discrete prediction model using the zero-order hold is expressed as follows.

[0083]

[0084] In the formula: Where t p x is the discrete time step. k+1 Let be the state variable at step k+1.

[0085] Part Two: Establishing Constraints and Optimization Objectives

[0086] Combining system output variables and control variables, and considering vehicle path tracking performance and motion optimization, the optimization objective function formula is as follows.

[0087]

[0088] In the formula: These are the output variable, the control input variable, and the control variable u from the previous time step. p In the prediction time domain N p The weight coefficients are a diagonal matrix within the matrix, and J is the objective function. Control input variables in the control time domain N c The control input matrix formed within N, y is the output matrix in N p The prediction matrix formed within, For matrix transpose, For the output variable in N p Internally formed matrix, The control variable at the previous time step N c The matrix formed within.

[0089] Based on the ellipse parameters: major axis a, minor axis b, and rotation angle ψ es Constructed geometric boundaries Mapping relationship between vehicle speed and road adhesion coefficient after ellipse parameter identification and ellipse parameters. x e Let y be the x-coordinate of the ellipse boundary. e f is the ordinate of the ellipse boundary. q (v e (μ) is a function of the elliptic parameters, v e For vehicle speed, w ij coefficients for identification μ is the i1th power of the vehicle speed. j1 Let be the j1-th power of the road surface adhesion coefficient, q be the subscript of the function, and be the set of elliptic parameters, including a, b, and ψ. es The boundary of the vehicle's real-time dynamic stability domain is represented as .

[0090]

[0091] Among them, f a f is a function of the major axis. b It is a minor axis function. This is a function of the rotation angle.

[0092] To ensure convex optimization solutions, the boundary of the vehicle's real-time dynamic stability domain is represented by the affine transformation of the nonlinear function.

[0093]

[0094] in, The lateral velocity v of the car's center of mass y Reference value at the reference point. This represents the reference value of the car's yaw rate r at the reference point, g(v y ,r) represents the real-time dynamic stability domain of the vehicle. This represents the real-time dynamic stability domain of the vehicle after approximate reflection transformation.

[0095] The optimal control sequence u for the vehicle's front wheel steering angle and wheel torque * It can be calculated using FORCESPRO.

[0096] u * =argminJ

[0097]

[0098] u l ≤u(k)≤u u ;x l ≤x(k)≤x u

[0099]

[0100] In the formula: u * Let J be the optimal control sequence for the vehicle's front wheel steering angle and wheel torque, where J is the objective function and k is the prediction step size. For the real number field, f a f is a function of the major axis. b It is a minor axis function. Let u(k) be the rotation angle function, x(k) be the control input variable for each prediction step, x(k) be the state variable for each prediction step, x(1) be the state variable for the first prediction step, and x(t) be the initial value of the state variable. The lower and upper boundaries of the state variable and the control input variable are respectively represented by u(k) and x(t). l ,u u ,x l ,x u The upper boundary of wheel torque Represented as: in This is the maximum wheel driving torque that the actuator can provide. R is the vertical force of the vehicle tires, μ is the road adhesion coefficient, and R is the vertical force of the tires. e The effective radius of the tire. and Let represent the allowable set of state and control inputs under constrained optimization conditions. The state terminal constraint is the equilibrium point. Ensure the asymptotic stability of the controller.

[0101] This application constructs an integrated predictive model. A LuGre tire model is built, employing a dual-track vehicle dynamics model. A predictive model is constructed by combining nonlinear tire forces and actuation system characteristics. The nonlinear tire forces refer to lateral tire forces, and the actuation system includes the steering and drive systems. Constraints and optimization objectives are established. Combining system output and control variables, and considering the vehicle's path tracking performance and actuation optimization, an optimization objective function is established. The constraints of the controller are designed based on the vehicle's real-time dynamic stability domain boundary, constructing an intelligent vehicle path tracking controller that considers the vehicle's dynamic stability boundary. Finally, based on the constructed integrated predictive model, the real-time dynamic stability boundary is integrated into the constraints, and the rolling time-domain optimization method is used to calculate the front wheel steering angle and the torque of each wheel in real time.

[0102] This application extends the existing relatively conservative stability domain and uses a geometric model to explicitly characterize the extended vehicle system stability domain under the influence of multiple driving factors, thereby realizing the real-time change of the stability domain boundary with driving conditions. The stability domain, characterized by geometric parameters, is integrated into the constraints of the path tracking controller. While ensuring driving stability, this reduces vehicle state fluctuations caused by braking intervention under high-speed, low-adhesion conditions, improving the accuracy of vehicle path tracking. It avoids the poor path tracking performance caused by low stability margins and frequent actuator interventions resulting from traditional linear stability constraints. Thus, while ensuring vehicle active safety, it achieves better path tracking performance, improving vehicle stability and ride comfort. This provides a systematic design method and theory for improving the path tracking accuracy of intelligent vehicles under high-speed, low-adhesion conditions. Integrating the dynamic real-time stability domain into the constraints allows the vehicle to optimize its actuation inputs within the extended real-time dynamic stability boundary, reducing vehicle state fluctuations caused by braking intervention under high-speed, low-adhesion conditions, and improving the path tracking accuracy in extreme scenarios. This provides ideas and methods for integrated control of intelligent vehicles in extreme scenarios. This path tracking control method, which integrates real-time dynamic stability domains, can be used for emergency collision avoidance, overtaking, lane changing, merging, etc. of intelligent vehicles. It can also be used to integrate vehicle system stability domains characterized by other geometric models or vehicle system stability domain boundaries obtained by other processing methods such as machine learning. It can also be used to integrate boundary constraints of other vehicle motion states.

[0103] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 4As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores vehicle tracking data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When the computer program is executed by the processor, it implements a vehicle path tracking control method.

[0104] Those skilled in the art will understand that Figure 4 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method embodiments.

[0105] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the above-described method embodiments.

[0106] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the above-described method embodiments.

[0107] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0108] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0109] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0110] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0111] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A vehicle path tracking control method characterized by comprising: The vehicle path tracking control method comprises: constructing a vehicle dynamics model; constructing an integrated prediction model using nonlinear tire forces and actuation system characteristics according to the vehicle dynamics model; the actuation system characteristics include actuation system time constants and actuation system system gains; determining an optimization objective function based on vehicle path tracking performance and actuation optimization according to control input variables and output variables in the integrated prediction model; determining a real-time dynamic stability domain boundary of the vehicle according to control input variables, vehicle states and elliptical parameter functions in the integrated prediction model; solving the integrated prediction model based on the optimization objective function and constraint conditions using a receding horizon optimization method to determine front wheel steering angles and torques of each wheel of the vehicle; the constraint conditions at least include the real-time dynamic stability domain boundary of the vehicle.

2. The vehicle path tracking control method according to claim 1, characterized by, The formula of the vehicle dynamics model is: ； ； ； wherein, is the longitudinal vehicle speed, is the lateral vehicle mass center speed, is the lateral vehicle speed, is the yaw angle, is the derivative of the longitudinal position in the global coordinate system, is the derivative of the lateral coordinate in the global coordinate system, is the derivative of the yaw angle.

3. The vehicle path tracking control method according to claim 1, characterized by, The spatial state equation of the integrated prediction model is: ； State variable is: ； Control input variable Is: ； Disturbance variable is: ； Output variable is: ； in, for The corresponding matrix, For the differential form of the state variables, For the system matrix, For the control matrix, Here is the perturbation matrix. For the output matrix, ij=fl,fr,rl,rr These indicate the front left, front right, rear left, and rear right positions of the vehicle, respectively. The lateral velocity of the vehicle's center of gravity. Let yaw rate be the angular velocity of the car. For the front wheel steering angle, This refers to the tire's slip angle. Let be the translational speed of the wheel center. The vehicle's longitudinal position, lateral position, and yaw angle in the global coordinate system. The acceleration of the vehicle's center of gravity. For the desired front wheel steering angle, For the torque of the wheel, and All are relaxation factors. lateral velocity The perturbation variable, yaw rate The perturbation variable, This indicates the longitudinal force of each tire. and Vertical position in the global coordinate system Horizontal position The disturbance term, The term represents the disturbance of driving resistance, and T represents the matrix transpose.

4. The vehicle path tracking control method according to claim 1, characterized by, Before the step of determining an optimization objective function based on vehicle path tracking performance and actuation optimization according to control input variables and output variables in the integrated prediction model, it further comprises: discretizing the integrated prediction model using a zero-order holder.

5. The vehicle path tracking control method according to claim 1, characterized by, The formula of the optimization objective function is: ； in, These are the output variable, the control input variable, and the control variable from the previous time step. In the prediction time domain The weight coefficient diagonal matrix within, To optimize the objective function, To control input variables in the control time domain The control input matrix formed internally, For matrix transpose, For the output variable in N p Internally formed matrix, The control variable at the previous time step N c The matrix formed within.

6. The vehicle path tracking control method according to claim 1, characterized by, The real-time dynamic stability domain boundary of the vehicle is: ； wherein is the lateral velocity of the vehicle's center of mass is a reference value at the reference point, is the yaw rate of the vehicle is a reference value at the reference point, is the real-time vehicle dynamics stability domain, is the approximated reflected vehicle dynamics stability domain.

7. The vehicle path tracking control method according to claim 1, characterized by, Solving the integrated prediction model based on the optimization objective function and constraint conditions using a receding horizon optimization method to determine front wheel steering angles and torques of each wheel of the vehicle, and the specific formula is: ； wherein is the optimal control sequence of vehicle front wheel angle and wheel torque, is the optimization objective function, is the prediction step, is the real number field, is the long axis function, is the short axis function, is the rotation angle function, is the control input variable for each prediction step, is the state variable for each prediction step, is the state variable for the first prediction step, is the initial value of the state variable, the lower and upper bounds of the state variable and the control input variable are denoted as , the upper bound of the wheel torque is expressed as: wherein is the maximum wheel driving torque that the actuator can provide, is the vertical force of the vehicle tire, is the road adhesion coefficient, is the effective radius of the tire, and are the allowable sets of the state variable and the control input variable under the constraint optimization condition, indicates that the state terminal constraint is the equilibrium point ; is the state variable of the k+1 step; is the lateral velocity of the vehicle mass center, is the yaw angular velocity of the vehicle, is the lateral velocity of the vehicle mass center is the reference value at the reference point, indicates that the yaw angular velocity of the vehicle is the reference value at the reference point, is the vehicle real-time dynamic stability domain, is the vehicle real-time dynamic stability domain after the approximate reflection transformation.

8. A computer device comprising: A memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the vehicle path tracking control method of any one of claims 1-7.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the vehicle path tracking control method of any one of claims 1-7.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the vehicle path tracking control method of any one of claims 1-7.

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