Vehicle control method and device, vehicle, computer equipment and storage medium

Abstract

The invention relates to a vehicle control method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: establishing a kinematic model of a target vehicle, and constructing a state equation for describing an error between an actual motion track and an expected motion track of the target vehicle according to the kinematic model; performing rolling optimization on the state equation according to a preset constraint and an optimization target to obtain a steering wheel angle instruction based on a kinematic model; the method comprises the following steps: scaling a steering wheel angle instruction according to a plurality of preset scaling coefficients to obtain a plurality of scaled steering wheel angle instructions, calculating a cost value of each scaled steering wheel angle instruction according to an error value between an actual vehicle response and an expected trajectory, determining a target cost value according to each cost value, and determining a target steering wheel angle instruction according to the target cost value. And determining a steering wheel angle instruction for controlling the target vehicle according to the target cost value. The steering wheel angle instruction obtained through the method is high in precision, calculation power consumption can be reduced, and calculation resources are saved.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to a vehicle control method, apparatus, computer equipment, storage medium, and computer program product. Background Art

[0002] With the development of vehicle electrification, current vehicle driving control mainly includes steps such as information collection, data analysis, decision-making and generation of control strategies, execution of control strategies, and feedback and adjustment. Control strategies include power control strategies, steering control strategies, and braking control strategies. These strategies achieve vehicle acceleration, deceleration, cornering, or lane changes by controlling vehicle acceleration or deceleration, steering angle, or braking force, ensuring the vehicle safely achieves its driving objectives. The vehicle's electronic control unit (ECU) is responsible for converting control strategies into specific instructions and transmitting them to the corresponding actuators for execution. After execution, feedback information is transmitted back to the control system. If the vehicle deviates from the intended path, the control system adjusts the instructions to compensate for the deviation.

[0003] Commercial vehicles are vehicles used in freight, logistics, or public transportation, typically possessing high carrying capacities and operating loads. These vehicles feature high system complexity, a wide range of load variations, and complex and changing operating environments, requiring frequent adjustments during control. Due to their high load and power requirements, actuators in commercial vehicles often exhibit significant inertia and hysteresis, impacting control accuracy. Summary of the Invention

[0004] Based on this, it is necessary to provide a vehicle control method, device, vehicle, computer equipment, computer-readable storage medium and computer program product that can improve vehicle control accuracy in order to address the above technical problems.

[0005] In a first aspect, the present application provides a vehicle control method, comprising:

[0006] Establishing a kinematic model of a target vehicle, and constructing a state equation describing an error between an actual motion trajectory of the target vehicle and the desired motion trajectory based on the kinematic model;

[0007] Perform rolling optimization on the state equation according to preset constraints and optimization objectives to obtain a steering wheel angle command based on a kinematic model;

[0008] The steering wheel angle command is scaled according to a plurality of preset scaling factors to obtain a plurality of scaled steering wheel angle commands, a cost value of each of the scaled steering wheel angle commands is calculated according to an error value between an actual vehicle response and a desired trajectory, a target cost value is determined according to each of the cost values, and a steering wheel angle command for controlling the target vehicle is determined according to the target cost value.

[0009] In one embodiment, the vehicle dynamics model includes a vehicle kinematics model and a steering wheel response model, and constructing a state equation describing the error between the actual motion trajectory of the target vehicle and the desired motion trajectory based on the kinematics model includes:

[0010] Establishing a two-degree-of-freedom kinematic model of the vehicle according to tire force characteristics and suspension force characteristics of the target vehicle;

[0011] Establishing the steering wheel response model according to a preset function of the steering system parameters and the steering force of the target vehicle;

[0012] The vehicle dynamics model is discretized according to the desired motion trajectory of the vehicle to obtain a discrete kinematic model of the target vehicle, and the discrete kinematic model is converted into an expression of a control quantity increment of the target vehicle to obtain a vehicle dynamics model.

[0013] In one embodiment, performing rolling optimization on the state equation according to preset constraints and optimization objectives to obtain a steering wheel angle command based on a kinematic model includes:

[0014] determining a prediction period and a control period of the kinematic model, wherein the prediction period is greater than the control period;

[0015] The optimal control input sequence of the kinematic model within the prediction period is calculated according to the preset optimization target, the control amount increment and the preset constraints to obtain the steering wheel angle command.

[0016] In one embodiment, calculating the cost of each scaled steering wheel angle command based on the error between the actual vehicle response and the desired trajectory includes:

[0017] calculating an error value between an actual vehicle response and a desired trajectory corresponding to each of the scaled steering wheel angle commands based on the two-degree-of-freedom kinematic model and the steering wheel response model;

[0018] The cost value corresponding to each of the rotation angle commands is calculated according to each of the error values, each of the rotation angle commands and a preset optimization target.

[0019] In one embodiment, determining a target cost value based on each of the cost values, and determining a steering wheel angle command for controlling the vehicle based on the target cost value, includes:

[0020] Comparing the cost values ​​to determine a minimum cost value of a predetermined number among the cost values;

[0021] An interpolation calculation is performed based on each of the minimum cost values ​​to obtain an intermediate value of each of the minimum cost values, a steering wheel angle instruction corresponding to the intermediate value is obtained, and the steering wheel angle instruction corresponding to the intermediate value is used as the target steering wheel angle instruction.

[0022] In a second aspect, the present application further provides a vehicle control device, comprising:

[0023] A model prediction module is used to establish a kinematic model of the target vehicle, and to construct a state equation describing the error between the actual motion trajectory of the target vehicle and the expected motion trajectory based on the kinematic model;

[0024] An optimization solution module, configured to perform rolling optimization on the state equation according to preset constraints and optimization objectives, and obtain a steering wheel angle command based on a kinematic model;

[0025] The command determination module is configured to scale the steering wheel angle command according to a plurality of preset scaling factors to obtain a plurality of scaled steering wheel angle commands, calculate a cost value for each of the scaled steering wheel angle commands based on an error value between an actual vehicle response and a desired trajectory, determine a target cost value based on each of the cost values, and determine a steering wheel angle command for controlling the target vehicle based on the target cost value.

[0026] In a third aspect, the present application further provides a vehicle, comprising a vehicle control device as described in the first aspect or any embodiment of the first aspect, wherein the vehicle control device is used to control the movement of the vehicle.

[0027] In a fourth aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0028] Establishing a kinematic model of a target vehicle, and constructing a state equation describing an error between an actual motion trajectory of the target vehicle and the desired motion trajectory based on the kinematic model;

[0029] Perform rolling optimization on the state equation according to preset constraints and optimization objectives to obtain a steering wheel angle command based on a kinematic model;

[0030] The steering wheel angle command is scaled according to a plurality of preset scaling factors to obtain a plurality of scaled steering wheel angle commands, a cost value of each of the scaled steering wheel angle commands is calculated according to an error value between an actual vehicle response and a desired trajectory, a target cost value is determined according to each of the cost values, and a steering wheel angle command for controlling the target vehicle is determined according to the target cost value.

[0031] In a fifth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0032] Establishing a kinematic model of a target vehicle, and constructing a state equation describing an error between an actual motion trajectory of the target vehicle and the desired motion trajectory based on the kinematic model;

[0033] Perform rolling optimization on the state equation according to preset constraints and optimization objectives to obtain a steering wheel angle command based on a kinematic model;

[0034] The steering wheel angle command is scaled according to a plurality of preset scaling factors to obtain a plurality of scaled steering wheel angle commands, a cost value of each of the scaled steering wheel angle commands is calculated according to an error value between an actual vehicle response and a desired trajectory, a target cost value is determined according to each of the cost values, and a steering wheel angle command for controlling the target vehicle is determined according to the target cost value.

[0035] In a sixth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0036] Establishing a kinematic model of a target vehicle, and constructing a state equation describing an error between an actual motion trajectory of the target vehicle and the desired motion trajectory based on the kinematic model;

[0037] Perform rolling optimization on the state equation according to preset constraints and optimization objectives to obtain a steering wheel angle command based on a kinematic model;

[0038] The steering wheel angle command is scaled according to a plurality of preset scaling factors to obtain a plurality of scaled steering wheel angle commands, a cost value of each of the scaled steering wheel angle commands is calculated according to an error value between an actual vehicle response and a desired trajectory, a target cost value is determined according to each of the cost values, and a steering wheel angle command for controlling the target vehicle is determined according to the target cost value.

[0039] The above-mentioned vehicle control method, device, computer equipment, storage medium and computer program product can accurately describe the motion characteristics and state changes of the target vehicle by establishing a kinematic model of the target vehicle, can accurately reflect the dynamic characteristics of the vehicle, and can achieve accurate trajectory prediction by describing the deviation between the actual motion of the vehicle and the expected trajectory through the state equation. The state equation is used to perform real-time calculations on the state equation, and the steering wheel angle command can be dynamically adjusted according to the current state. It can quickly adapt to external interference and trajectory changes and has strong real-time performance. The steering wheel angle command is scaled by a preset scaling factor and the corresponding cost value is calculated. There is no need to independently optimize each steering wheel command. The resources required for calculation are relatively few, the computational complexity is reduced, and the demand for hardware can be reduced while meeting the control accuracy. The steering wheel angle command obtained by the above-mentioned vehicle control method, device, computer equipment, storage medium and computer program product is highly accurate and can reduce computing power consumption and save computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] FIG1 is a diagram illustrating an application environment of a vehicle control method according to an embodiment;

[0042] FIG2 is a schematic flow chart of a vehicle control method according to an embodiment;

[0043] Figure 3 is a flow chart of a vehicle control method according to another embodiment;

[0044] Figure 4 is a schematic diagram of a vehicle kinematic model involved in a vehicle control method in one embodiment;

[0045] Figure 5 is a schematic diagram of a two-degree-of-freedom vehicle dynamics model involved in a vehicle control method in one embodiment;

[0046] Figure 6 is a structural block diagram of a vehicle control device in one embodiment;

[0047] FIG7 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0049] The vehicle control method provided in the embodiment of the present application can be applied to the application environment shown in Figure 1. The terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The terminal 102 can be, but is not limited to, various personal computers, laptops, and portable IoT devices. The IoT devices can be smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented as an independent server or a server cluster consisting of multiple servers.

[0050] In the prior art, the control effect of smart commercial vehicles is often improved by increasing the complexity of the model, such as: using a dynamic model that considers the characteristics of tires and suspension instead of a kinematic model that regards the vehicle as an ideal rigid body; considering the dynamic response of the steering system and the drive and braking systems, etc. However, as the complexity of the model increases, it may lead to complex model solving or even inability to solve the model; the computing power required for solving the model increases sharply; the stability of the model solution is poor, and other problems not only affect the control accuracy, but also lead to increased power consumption and design costs of the vehicle. In response to the above problems, in an exemplary embodiment, as shown in FIG2 , a vehicle control method is provided, which is described by taking the application environment in FIG1 as an example, and includes the following steps 202 to 206. Among them:

[0051] Step 202 : Establish a kinematic model of the target vehicle, and construct a state equation describing the error between the actual motion trajectory and the expected motion trajectory of the target vehicle based on the kinematic model.

[0052] Among them, the target vehicle refers to a vehicle that needs to be path planned, tracked or motion controlled in an actual driving or simulation environment, that is, a vehicle controlled by the vehicle control method provided in this application. The kinematic model refers to a mathematical model that describes the motion law of the target vehicle, which may include variables such as vehicle position, speed and posture. The actual motion trajectory refers to the path formed by the actual motion state of the target vehicle within a certain period of time. It can be obtained by continuous recording of the position and posture passed by the vehicle in a real operation or simulation environment, or by data collected by sensors, or by simulation in a simulation environment based on input control instructions and vehicle models. The expected motion trajectory refers to the ideal path and motion plan of the vehicle, which can be generated by a path planning algorithm or a preset motion path. The expected trajectory can be used as the target of the control method. The state equation refers to a mathematical expression that describes the relationship between the motion state of the target vehicle and its control input.

[0053] For example, a kinematic model can be determined based on the characteristics of the target vehicle, with key parameters describing the vehicle's motion, such as position, azimuth, speed, etc., used as state variables, and instructions that affect the vehicle's motion, such as the front wheel steering angle or acceleration, etc., used as control inputs. The deviation between the actual trajectory and the parameters such as the position and azimuth of the expected trajectory is determined to obtain an error variable, and the kinematic model is used to represent the changes in the actual state and the changes in the expected state, respectively, to obtain a state equation.

[0054] Step 204 : Perform rolling optimization on the state equation according to the preset constraints and optimization objectives to obtain a steering wheel angle command based on the kinematic model.

[0055] Among them, the preset constraints refer to the restrictions that need to be met during the optimization process of the state equation, which may include vehicle physical constraints, road environment constraints or control constraints. The optimization objective refers to the performance indicator that needs to be minimized or maximized during the optimization process, which may be the minimization of trajectory tracking error, optimal control input or comprehensive optimization objectives. Rolling optimization refers to a real-time optimization method that solves the optimization problem based on the current state and future predictions at each moment, calculates the optimal control sequence within a period of time, but only executes the first control instruction, and then repeats the process. The steering wheel angle instruction refers to the direct control variable that controls the vehicle's driving trajectory, which represents the steering angle of the vehicle's front wheels and is usually expressed in the form of a steering wheel rotation angle.

[0056] For example, a quadratic programming solver or other nonlinear optimization algorithm can be used to solve the optimization problem at the current moment, obtain the optimal control sequence for several future moments, obtain the first control input from the optimization result as the steering wheel angle command, and update the current state of the vehicle at the next sampling moment, and then perform a rolling prediction of the expected trajectory points in the future, and repeat the optimization solution process.

[0057] Step 206 : Scale the steering wheel angle command according to a plurality of preset scaling factors to obtain a plurality of scaled steering wheel angle commands, calculate a cost value for each scaled steering wheel angle command based on an error value between an actual vehicle response and a desired trajectory, determine a target cost value based on each cost value, and determine a steering wheel angle command for controlling a target vehicle based on the target cost value.

[0058] The preset scaling factor is the proportional factor used to adjust the steering wheel angle command, which is used to generate multiple candidate control commands. The cost value is a quantitative indicator used to measure the quality of a control command during the optimization process, typically consisting of error and control cost. The target cost value is the final cost value selected during the optimization process and can be the cost value of the control solution corresponding to the lowest cost value among all candidate steering wheel angle commands.

[0059] For example, a set of scaling coefficients can be defined based on the uncertainty or nonlinear characteristics of the vehicle response. The vehicle kinematic model and the steering wheel angle dynamic response model can be used to simulate the vehicle motion response of each scaling instruction respectively. For each scaling instruction, the error between the actual trajectory and the expected trajectory of the vehicle is calculated. A cost value function is defined to comprehensively measure the trajectory error and the smoothness of the control input. For each scaling instruction, the error value and the control input change are substituted. Combined with the optimization objective, the cost value of each scaling instruction is calculated, the minimum cost value is selected as the target cost value, and the optimal steering wheel angle instruction is determined by the target cost value for actual control.

[0060] The above-mentioned vehicle control method can accurately describe the motion characteristics and state changes of the target vehicle by establishing a kinematic model of the target vehicle, can accurately reflect the dynamic characteristics of the vehicle, and can achieve accurate trajectory prediction by describing the deviation between the actual motion of the vehicle and the expected trajectory through the state equation. The state equation is used to perform real-time calculations on the state equation, and the steering wheel angle command can be dynamically adjusted according to the current state. It can quickly adapt to external interference and trajectory changes and has strong real-time performance. The steering wheel angle command is scaled by a preset scaling factor and the corresponding cost value is calculated. There is no need to independently optimize each steering wheel command. The resources required for calculation are relatively few, which reduces the computational complexity. While meeting the control accuracy, the demand for hardware can be reduced. The obtained steering wheel angle command has high accuracy, and can reduce computing power consumption and save computing resources.

[0061] In an exemplary embodiment, the vehicle dynamics model includes a vehicle kinematic model and a steering wheel response model. A state equation describing the error between the motion of the target vehicle and the desired motion trajectory is constructed based on the kinematic model, including: establishing a two-degree-of-freedom kinematic model of the vehicle based on the tire force characteristics and suspension force characteristics of the target vehicle; establishing a steering wheel response model based on the preset function of the steering system parameters and steering force of the target vehicle; discretizing the vehicle dynamics model according to the desired motion trajectory of the vehicle to obtain a discrete kinematic model of the target vehicle, and converting the discrete kinematic model into an expression of the control quantity increment of the target vehicle to obtain the vehicle dynamics model.

[0062] A vehicle's tire load characteristics refer to the mechanical properties exhibited by the tire when in contact with the ground. These characteristics are related to factors such as tire stiffness, friction coefficient, and slip rate, and may include tire cornering force, tire longitudinal force, and friction coefficient. Suspension load characteristics refer to the ability of the suspension system (including springs, shock absorbers, etc.) to withstand and transmit forces during vehicle motion, including stiffness and damping. A two-degree-of-freedom kinematic model is a kinematic model that considers two primary degrees of freedom (DOFs), which can be longitudinal and lateral displacements, or the steering angles of the front and rear wheels and the yaw angle of the vehicle body. Steering system parameters refer to various technical indicators used to describe the performance and response of a vehicle's steering system, including steering ratio, steering force, and steering system response time. Steering force refers to the force required by the driver to operate the steering wheel. A steering wheel response model describes how the steering system converts the driver's steering wheel input into a wheel angle response. Discretization refers to the process of converting a continuous-time or continuous-state model into a discrete-time or discrete-state model. In a control system, a control variable increment refers to the change in the control input at a given moment relative to the control input at the previous moment.

[0063] For example, a tire lateral force model and a longitudinal force model can be defined based on the tire force characteristics of the target vehicle. The tire lateral force model can be characterized by a lateral force model and a lateral damping force model, and the tire longitudinal force model can be characterized by a tire rolling resistance and traction model. Based on the suspension force characteristics of the target vehicle, the suspension stiffness and damping characteristics are defined. The suspension stiffness model can be characterized by the spring stiffness and the elastic deformation of the suspension spring, and the damping characteristics can be characterized by the shock absorber damping coefficient. Based on the above tire force characteristics and suspension force characteristics, a two-degree-of-freedom kinematic model is established. Based on the steering system parameters of the target vehicle, such as the steering ratio, the power assist characteristics of the steering system, and a preset function of the steering force, a steering system model is established. Based on the steering system parameters of the target vehicle and the preset function of the steering force, a steering wheel response model is established. A discretization method (such as the Euler method or the homogeneous difference method) is used to convert the continuous two-degree-of-freedom kinematic model and the steering wheel response model into discrete models. Based on the discretized models, the relationship between the control variable increment and the actual control input is derived, thereby obtaining a vehicle dynamics model.

[0064] In an exemplary embodiment, a rolling optimization is performed on the state equation according to preset constraints and optimization objectives to obtain a steering wheel angle command based on a kinematic model, including: determining a prediction period and a control period of the kinematic model, wherein the prediction period is greater than the control period; and calculating the optimal control input sequence of the kinematic model within the prediction period according to the preset optimization objectives, control quantity increments and preset constraints to obtain a steering wheel angle command.

[0065] The prediction period refers to the timeframe or number of steps used in a model predictive control algorithm to predict the future behavior of a system. The control period refers to the timeframe or number of steps that the control algorithm is actually applied after each optimization. The optimal control sequence is the set of control instructions obtained by an optimization algorithm (such as MPC) that achieves the optimal system performance objective.

[0066] For example, the prediction period can be determined based on factors such as the vehicle's response delay, vehicle inertia, and trajectory complexity. When the response is slow, the inertia is large, or the road conditions are complex, a longer prediction period can be selected. The control period is determined based on the real-time requirements of the control and the hardware parameters. The objective function is defined to measure the trajectory tracking error and the control input change, and dynamic constraints and control constraints are defined. The vehicle state, the desired trajectory point sequence, the vehicle speed, the preset optimization objective function, and the constraints can be used as inputs, and the control input sequence can be used as variables. The optimization algorithm is used to solve the optimal control input sequence, execute the first control input, update the vehicle state, and re-predict and optimize in the next control period, and output the steering wheel angle command for the current control period.

[0067] In an exemplary embodiment, a cost value of each scaled steering wheel angle command is calculated based on an error value between an actual vehicle response and a desired trajectory, including: calculating an error value between an actual vehicle response and a desired trajectory corresponding to each scaled steering wheel angle command based on a two-degree-of-freedom kinematic model and a steering wheel response model; and calculating a cost value corresponding to each steering angle command based on each error value, each steering angle command, and a preset optimization target.

[0068] Among them, cost value refers to the numerical indicator used in the optimization algorithm to measure the quality of the control scheme or decision, usually the value of the objective function.

[0069] For example, the trajectory error under each steering wheel angle command can be calculated as the error cost, and the control cost caused by the angle change can be calculated, and the error cost and the control cost can be added together to obtain the total cost value.

[0070] In an exemplary embodiment, a target cost value is determined based on each cost value, and a steering wheel angle command for controlling a vehicle is determined based on the target cost value, including: comparing each cost value to determine a preset number of minimum cost values ​​among each cost value; performing interpolation calculation based on each minimum cost value to obtain an intermediate value of each minimum cost value, obtaining a steering wheel angle command corresponding to the intermediate value, and using the steering wheel angle command corresponding to the intermediate value as the target steering wheel angle command.

[0071] The minimum cost value refers to the cost values ​​that are the smallest after sorting the cost values ​​in order of magnitude. Interpolation calculation refers to a calculation method that estimates the value of a certain location point using a preset rule or function.

[0072] For example, the intermediate value of two minimum cost values ​​can be calculated by linear interpolation, and the steering wheel instruction corresponding to the intermediate value can be used as the target steering wheel instruction. The intermediate value can also be calculated based on multiple minimum cost values ​​by interpolation calculation methods such as polynomial interpolation, piecewise interpolation or spline interpolation to obtain the corresponding target steering wheel angle instruction.

[0073] In an exemplary embodiment, a vehicle control method is provided, which uses a kinematic model to solve an MPC solution, scales the kinematic model, performs dynamic calculations to obtain a response cost, and interpolates the kinematic model to obtain a final control instruction, including the following steps 302 to 306. In particular:

[0074] Step 302 : Establish a simple vehicle kinematic model and convert it into a state equation that describes the vehicle's relative desired trajectory error.

[0075] Step 304: Use the MPC algorithm to solve and obtain the optimal steering wheel angle command based on the kinematic model.

[0076] In step 306, the steering wheel angle command is scaled to varying degrees to obtain several different response commands. These commands are then passed through the steering wheel response model and the vehicle dynamics model, and the cost for each command is calculated. The two smallest cost values ​​are selected and interpolated to obtain the minimum cost and the steering angle command.

[0077] Taking vehicle steering control as an example, the algorithm and implementation steps used are explained:

[0078] 1. Establishment of vehicle kinematic model

[0079] Without considering the elasticity of tires and suspension, the vehicle kinematic model is established. The following relationship exists:

[0080]

[0081] Where v is the vehicle's longitudinal velocity, δ is the front wheel angular rate, θ is the angle between the vehicle's longitudinal axis and the earth's coordinate system, xy is the earth's coordinate system, and L is the vehicle's wheelbase.

[0082] 2. MPC solution establishment

[0083] MPC control is a real-time closed-loop optimization control method. The main advantage of this algorithm is that it is performed repeatedly online and can continuously obtain the current optimal control quantity. The model predictive control algorithm has the basic characteristics of predictive model, rolling optimization, and feedback correction. Traditional research methods often ignore or simplify kinematic and dynamic constraints, which have a significant impact on control performance. The model predictive control method can explicitly take into account the vehicle's kinematic and dynamic constraints by optimizing the objective function. Using the rolling optimization and feedback correction characteristics of MPC can effectively reduce or even eliminate the impact of closed-loop system lag problems, and can optimize motion control in combination with the future trajectory information given by the plan, thereby improving control performance.

[0084] To establish the MPC control model, the following relationship exists:

[0085] 1. Based on the desired vehicle motion trajectory, the above vehicle kinematic model is discretized. The derivation process can be implemented by existing technologies and will not be described in detail in this application. It is expressed as follows:

[0086] (1)

[0087] In this formula, the state variable x represents the difference between the vehicle motion and the desired vehicle motion trajectory, and the input variable u represents the required steering wheel angle.

[0088] Rewrite the model as an incremental control:

[0089] (2)

[0090] in:

[0091]

[0092]

[0093]

[0094]

[0095]

[0096] Let the model prediction period be , the control period is , .have:

[0097] (3)

[0098]

[0099] (4)

[0100] From the above reasoning, we can get Always The output of the moment model is predicted and can be organized into the following form:

[0101] (5)

[0102] in:

[0103]

[0104]

[0105]

[0106]

[0107] 2) Scrolling optimization

[0108] The optimization objective can be written as follows:

[0109] (6)

[0110] In the formula Relaxation factor

[0111] set up:

[0112] (7)

[0113] Then formula (6) can be expressed as:

[0114] (8)

[0115] Where:

[0116]

[0117]

[0118]

[0119]

[0120] In the MPC controller, the constraints of the controlled quantity are considered:

[0121] (9)

[0122] (10)

[0123] Formula (9) is expressed as the row form of the control quantity increment constraint, but Formula (10) is the row form of the control quantity constraint. Since Formula (8) is the row form of the control quantity increment, Formula (10) can be written as the row form of the control quantity increment:

[0124] (11)

[0125] in:

[0126]

[0127]

[0128] 、 are the minimum and maximum values ​​of the control quantity.

[0129] Combining equations (8), (9), and (10), we can solve the inequality constraint optimization problem and obtain

[0130] (12)

[0131] Will

[0132] (13)

[0133] As the control variable input of the current system.

[0134] 3. Vehicle Dynamics Model Establishment

[0135] Using a kinematic model to describe the steering motion of a commercial vehicle results in relatively large errors. A more reasonable approach is to establish a two-degree-of-freedom vehicle dynamics model that considers the force characteristics of the tires and suspension, as well as a steering wheel response model that accounts for steering hysteresis, stiffness, and damping.

[0136] Establish a two-degree-of-freedom vehicle dynamics model:

[0137] have:

[0138] (14)

[0139] Where: m is the vehicle mass, vx is the vehicle longitudinal velocity, β is the sideslip angle of the center of mass, δf is the front wheel turning angle, F xf is the longitudinal force of the front wheel, F yf is the lateral force of the front wheel, F yr is the lateral component of the rear wheel force. a is the distance from the center of mass to the front wheel, and b is the distance from the center of mass to the rear wheel.

[0140] In actual steering control, the steering wheel angle is controlled, and the response of the steering wheel angle to the vehicle's steering wheel is a dynamic process. Therefore, a steering wheel (front wheel in this case) response model should also be established.

[0141] Jw ‘ +Cw+Ff=Fw

[0142] Fw=f(δ(t-Dt)) (15)

[0143] Where J is the steering system inertia, C is the steering system damping, Ff is the steering system friction, and Fw is the steering force. Fw is a function of the steering wheel angle δ(t), and δ(t-Dt) represents the steering wheel angle after a pure lag time Dt.

[0144] 4. Kinetic Model Calibration

[0145] The steering wheel command (Equation 13) obtained using the kinematic model and the MPC solver is insufficient to represent the optimal directional control command. This is because the actual vehicle has many response links, and after being disturbed by several dynamic links, its response has already deviated significantly from the optimal value.

[0146] To maintain the optimality of the instructions obtained by MPC, the solution proposed in this patent is to amplify the solution obtained by (Equation 12) by 0.8 times, 0.9 times, 1.1 times, and 1.2 times, respectively, to obtain two new solutions:

[0147] (16)

[0148] (17)

[0149] (18)

[0150] (19)

[0151] Equations 16 through 19, along with Equation 13, are used as five hypothetical steering wheel angle commands. These are calculated using the steering wheel response component (Equation 15) and the vehicle dynamics component (Equation 14). The error between the actual vehicle response and the desired trajectory is obtained. Combining the five hypothetical steering wheel angle commands, Equation 6 calculates five different cost values. The two smallest cost values ​​are interpolated to obtain the minimum cost value, and the corresponding steering wheel angle command is calculated as the final steering wheel angle command, which represents the optimal solution that comprehensively considers vehicle dynamics and steering response.

[0152] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0153] Based on the same inventive concept, embodiments of the present application further provide a vehicle control device for implementing the aforementioned vehicle control method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more vehicle control device embodiments provided below can be found in the above-described limitations of the vehicle control method and will not be further elaborated here.

[0154] In an exemplary embodiment, Figure 6 As shown, a vehicle control device is provided, comprising: a model prediction module, an optimization solution module and an instruction determination module, wherein:

[0155] The model prediction module is used to establish a kinematic model of the target vehicle and construct a state equation that describes the error between the actual motion trajectory of the target vehicle and the expected motion trajectory based on the kinematic model;

[0156] The optimization solution module is used to perform rolling optimization on the state equation according to preset constraints and optimization objectives to obtain the steering wheel angle command based on the kinematic model;

[0157] The command determination module is used to scale the steering wheel angle command according to multiple preset scaling factors to obtain multiple scaled steering wheel angle commands, calculate a cost value for each scaled steering wheel angle command based on an error value between an actual vehicle response and a desired trajectory, determine a target cost value based on each cost value, and determine a steering wheel angle command for controlling a target vehicle based on the target cost value.

[0158] In an exemplary embodiment, the model prediction module is used to: establish a two-degree-of-freedom kinematic model of the vehicle based on the tire force characteristics and suspension force characteristics of the target vehicle; establish the steering wheel response model based on the preset function of the steering system parameters and steering force of the target vehicle; discretize the vehicle dynamics model according to the expected motion trajectory of the vehicle to obtain a discrete kinematic model of the target vehicle, and convert the discrete kinematic model into an expression of the control quantity increment of the target vehicle to obtain a vehicle dynamics model.

[0159] In an exemplary embodiment, the optimization solution module is used to: determine the prediction period and control period of the kinematic model, wherein the prediction period is greater than the control period; calculate the optimal control input sequence of the kinematic model within the prediction period according to preset optimization objectives, control quantity increments and preset constraints to obtain a steering wheel angle command.

[0160] In an exemplary embodiment, the instruction determination module is used to: calculate the error value between the actual vehicle response and the expected trajectory corresponding to each of the scaled steering wheel angle instructions based on the two-degree-of-freedom kinematic model and the steering wheel response model; and calculate the cost value corresponding to each of the steering angle instructions based on each of the error values, each of the steering angle instructions and a preset optimization target.

[0161] In an exemplary embodiment, the instruction determination module is used to: compare each of the cost values ​​to determine a preset number of minimum cost values ​​among the cost values; perform interpolation calculations based on each of the minimum cost values ​​to obtain an intermediate value of each of the minimum cost values, obtain a steering wheel angle instruction corresponding to the intermediate value, and use the steering wheel instruction corresponding to the intermediate value as the target steering wheel angle instruction.

[0162] Each module in the aforementioned vehicle control device may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor within a computer device in the form of hardware, or may be stored in a computer device memory in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0163] In an exemplary embodiment, the present application further provides a vehicle, which includes the vehicle control device as described in the above embodiments, and the vehicle control device is used to control the movement of the vehicle.

[0164] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure may be shown in Figure 7. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and computer program stored in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless means. The wireless means may be implemented via Wi-Fi, a mobile cellular network, NFC (near field communication), or other technologies. When executed by the processor, the computer program implements a vehicle control method. The display unit of the computer device is used to produce a visual image and may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0165] Those skilled in the art will understand that the structure shown in FIG7 is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0166] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-mentioned method embodiments. The computer device may be the computer device shown in FIG7 .

[0167] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0168] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0169] 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, stored data, displayed data, 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 relevant data must comply with relevant regulations.

[0170] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, 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 various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0171] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, 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.

[0172] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A vehicle control method, characterized in that: The method comprises: Establishing a kinematic model of a target vehicle, and constructing a state equation describing an error between an actual motion trajectory of the target vehicle and the desired motion trajectory according to the kinematic model; Perform rolling optimization on the state equation according to preset constraints and optimization objectives to obtain a steering wheel angle command based on a kinematic model; The steering wheel angle command is scaled according to a plurality of preset scaling factors to obtain a plurality of scaled steering wheel angle commands, a cost value of each of the scaled steering wheel angle commands is calculated according to an error value between an actual vehicle response and a desired trajectory, a target cost value is determined according to each of the cost values, and a steering wheel angle command for controlling the target vehicle is determined according to the target cost value.

2. The method according to claim 1, characterized in that The vehicle dynamics model includes a vehicle kinematics model and a steering wheel response model. The state equation describing the error between the actual motion trajectory of the target vehicle and the expected motion trajectory is constructed according to the kinematics model, including: Establishing a two-degree-of-freedom kinematic model of the vehicle according to tire force characteristics and suspension force characteristics of the target vehicle; Establishing the steering wheel response model according to the preset function of the steering system parameters and the steering force of the target vehicle; The vehicle dynamics model is discretized according to the desired motion trajectory of the vehicle to obtain a discrete kinematic model of the target vehicle, and the discrete kinematic model is converted into an expression of a control amount increment of the target vehicle to obtain a vehicle dynamics model.

3. The method according to claim 2, characterized in that The step of performing rolling optimization on the state equation according to the preset constraints and the optimization target to obtain a steering wheel angle command based on a kinematic model includes: Determining a prediction period and a control period of the kinematic model, wherein the prediction period is greater than the control period; The optimal control input sequence of the kinematic model within the prediction period is calculated according to the preset optimization target, the control amount increment and the preset constraints to obtain the steering wheel angle command.

4. The method according to claim 1, characterized in that: The calculating the cost value of each scaled steering wheel angle command according to the error value between the actual vehicle response and the expected trajectory comprises: Calculating an error value between an actual vehicle response and a desired trajectory corresponding to each of the scaled steering wheel angle commands according to the two-degree-of-freedom kinematic model and the steering wheel response model; The cost value corresponding to each of the rotation angle commands is calculated according to each of the error values, each of the rotation angle commands and a preset optimization target.

5. The method according to any one of claims 1 to 4, characterized in that: The step of determining a target cost value according to each of the cost values, and determining a steering wheel angle command for controlling the vehicle according to the target cost value, comprises: Comparing the cost values, and determining a minimum cost value of a preset number among the cost values; An interpolation calculation is performed based on each of the minimum cost values ​​to obtain an intermediate value of each of the minimum cost values, a steering wheel angle instruction corresponding to the intermediate value is obtained, and the steering wheel angle instruction corresponding to the intermediate value is used as the target steering wheel angle instruction.

6. A vehicle control device, characterized in that: The device comprises: A model prediction module, used to establish a kinematic model of the target vehicle, and to construct a state equation describing the error between the actual motion trajectory of the target vehicle and the expected motion trajectory according to the kinematic model; An optimization solution module, used for performing rolling optimization on the state equation according to preset constraints and optimization targets, and obtaining a steering wheel angle command based on a kinematic model; The command determination module is used to scale the steering wheel angle command according to multiple preset scaling factors to obtain multiple scaled steering wheel angle commands, calculate the cost value of each scaled steering wheel angle command according to the error value between the actual vehicle response and the expected trajectory, determine a target cost value according to each cost value, and determine the steering wheel angle command for controlling the target vehicle according to the target cost value.

7. A vehicle, characterized in that: The vehicle includes the vehicle control device as claimed in claim 6, and the vehicle control device is used to control the movement of the vehicle.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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