Vehicle control method and device, electronic equipment and storage medium

Abstract

The invention discloses a vehicle control method and device, electronic equipment and a storage medium, the vehicle control method can be applied to the technical field of automatic driving, and the method comprises the steps that under the condition that a target vehicle is in an unstable sliding state, current control information of the target vehicle is acquired; based on the current control information, simulating the motion of the target vehicle in an unstable sliding state to obtain the actual state information of the target vehicle; based on the current control information, simulating motion of the target vehicle in a stable sliding state to obtain reference state information of the target vehicle; taking the actual state information and the reference state information as input of a controller of the target vehicle, and performing optimization control by taking reduction of a gap between the actual state information and the reference state information in a coverage range of a road where the target vehicle is located as an optimization target to obtain target control information; and controlling the target vehicle based on the target control information. According to the invention, the safety and controllability of the vehicle running in the unstable sliding state are improved.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and in particular to a vehicle control method, device, electronic device and storage medium. Background Art

[0002] With the development of autonomous driving technology, autonomous driving is gradually becoming a reality. It can not only improve road safety, but also show the potential to surpass human drivers in complex driving environments. Existing autonomous driving algorithms mainly focus on ensuring that the vehicle can drive safely under stable conditions. The design of these algorithms usually relies on the vehicle's Electronic Stability Program (ESP) to manage chassis stability control. The ESP system can maintain vehicle stability by monitoring the vehicle's motion state and automatically adjusting the driving force and braking force distribution, thereby helping to prevent loss of control. Although this ESP-based control strategy simplifies the control problem, it also significantly limits the dynamic performance range that the vehicle can achieve.

[0003] For example, in extreme driving situations, such as racing, experienced drivers often use a technique called "drifting" to increase cornering speed and effectively avoid obstacles. Drifting means that the driver deliberately causes the rear wheels of the vehicle to lose some or all of their grip, causing the vehicle to slide, while still being able to maintain the vehicle along the intended path through directional control of the front wheels. This technique requires the driver to have a deep understanding of the dynamic characteristics of the vehicle and to be able to accurately control the throttle, steering, and brakes to achieve the purpose of high-speed cornering.

[0004] However, most current autonomous driving systems are not capable of performing such advanced driving maneuvers because traditional autonomous driving control algorithms are usually designed to prevent the vehicle from experiencing unstable states, such as drifting, which could pose a safety risk. Summary of the invention

[0005] In order to solve the problems of the prior art, the embodiments of the present application provide a vehicle control method, device, electronic device and storage medium. The technical solution is as follows:

[0006] In one aspect, a vehicle control method is provided, the method comprising:

[0007] When the target vehicle is in an unstable sliding state, obtaining current control information of the target vehicle; the unstable sliding state indicates that the friction between the target vehicle and the road surface is less than a first preset threshold;

[0008] Based on the current control information, simulating the movement of the target vehicle in the unstable sliding state to obtain actual state information of the target vehicle;

[0009] Based on the current control information, simulating the movement of the target vehicle in a stable sliding state to obtain reference state information of the target vehicle; the stable sliding state indicates that the friction between the target vehicle and the road surface is greater than or equal to the first preset threshold;

[0010] The actual state information and the reference state information are used as inputs of a controller of the target vehicle, and optimization control is performed with the optimization goal of reducing the gap between the actual state information and the reference state information within the coverage range of the road where the target vehicle is located, thereby obtaining target control information of the target vehicle;

[0011] The target vehicle is controlled based on the target control information to update the current control information.

[0012] In another aspect, a vehicle control device is provided, the device comprising:

[0013] An information acquisition module, used for acquiring current control information of the target vehicle when the target vehicle is in an unstable sliding state; the unstable sliding state indicates that the friction between the target vehicle and the road surface is less than a first preset threshold;

[0014] A first motion simulation module, configured to simulate the motion of the target vehicle in the unstable sliding state based on the current control information, and obtain actual state information of the target vehicle;

[0015] A second motion simulation module is used to simulate the motion of the target vehicle in a stable sliding state based on the current control information to obtain reference state information of the target vehicle; the stable sliding state indicates that the friction between the target vehicle and the road surface is greater than or equal to the first preset threshold;

[0016] an information determination module, configured to use the actual state information and the reference state information as inputs of a controller of the target vehicle, and to perform optimization control with the optimization goal of reducing the gap between the actual state information and the reference state information within the coverage range of the road where the target vehicle is located, thereby obtaining target control information of the target vehicle;

[0017] A control update module is used to control the target vehicle based on the target control information to update the current control information.

[0018] In an exemplary embodiment, the information determination module includes:

[0019] A tracking cost module, used to determine the degree to which the target vehicle deviates from the reference track indicated by the reference state information based on the actual state information and the reference state information, and obtain a path tracking cost;

[0020] A relaxation cost module, used to determine the extent to which the target vehicle exceeds the coverage range based on the actual state information, and obtain a relaxation cost;

[0021] A control cost module, configured to determine a control cost of the controller based on the path tracking cost and the relaxation cost;

[0022] The control information module is used to perform optimization control with reducing the control cost as the optimization target and determine the target control information.

[0023] In an exemplary embodiment, the relaxation cost module includes:

[0024] An offset range module is used to determine a target offset range of the target vehicle within the coverage range of the road where the target vehicle is located and in a direction perpendicular to the driving direction of the target vehicle; the target offset range is a distance range of the center of mass of the target vehicle relative to the center line of the road where the target vehicle is located;

[0025] The deviation degree module is used to determine the degree to which the target vehicle exceeds the target deviation range based on the actual state information to obtain the relaxation cost.

[0026] In an exemplary embodiment, the control information module includes:

[0027] A static constraint module, used to determine a numerical range corresponding to the target control information based on the physical limitations of the actuator of the target vehicle to obtain a static constraint condition;

[0028] A dynamic constraint module, used to determine a numerical range corresponding to the rate of change of the target control information based on the dynamic performance of the actuator, and obtain a dynamic constraint condition;

[0029] The optimization control module is used to perform optimization control with reducing the control cost as the optimization goal and determine the target control information under the premise of satisfying the static constraint condition and the dynamic constraint condition.

[0030] In an exemplary embodiment, the first motion simulation module includes:

[0031] A first whole vehicle simulation module, used for simulating the whole vehicle motion of the target vehicle in the unstable sliding state based on the current control information to obtain whole vehicle state information;

[0032] A tire simulation module, configured to simulate the tire movement of the target vehicle in the unstable sliding state based on the current control information and the vehicle state information, and obtain tire state information;

[0033] The second whole vehicle simulation module is used to simulate the whole vehicle motion of the target vehicle in the unstable sliding state based on the current control information and the tire state information to obtain the actual state information.

[0034] In an exemplary embodiment, the current control information includes the current braking torque, the current engine torque and the current front wheel angle of the target vehicle; the first motion simulation module includes:

[0035] A third motion simulation module is used to simulate the movement of the target vehicle in the unstable sliding state based on the current engine torque and the current front wheel angle, so as to obtain the actual state information of the target vehicle under the assumption that the current braking torque is less than a second preset threshold value; the second preset threshold value indicates that the braking torque does not affect the movement of the target vehicle.

[0036] On the other hand, an electronic device is provided, comprising a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the vehicle control method of any of the above aspects.

[0037] On the other hand, a computer-readable storage medium is provided, in which at least one instruction or at least one program is stored, and the at least one instruction or the at least one program is loaded and executed by a processor to implement a vehicle control method as described in any of the above aspects.

[0038] On the other hand, a computer program product or a computer program is provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device performs the vehicle control method of any of the above aspects.

[0039] The embodiment of the present application simulates the movement of the target vehicle in an unstable sliding state and the movement of the target vehicle in a stable sliding state when the target vehicle is in an unstable sliding state, and determines the target control information of the target vehicle by reducing the difference between the two within the coverage range of the road where the target vehicle is located. The target vehicle is controlled based on the target control information, and the movement of the vehicle in the unstable sliding state can be effectively controlled, thereby improving the controllability and safety of the vehicle driving in the unstable sliding state, reducing the safety risks caused by loss of control, and improving the adaptability of the vehicle in complex driving environments, especially the performance on wet or icy roads. The flexibility of the automatic driving system is enhanced, enabling it to better cope with emergencies, such as emergency obstacle avoidance, and providing passengers with a more comfortable and smooth riding experience, especially when driving at high speeds and cornering. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. 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 creative work.

[0041] Figure 1 is a flow chart of a vehicle control method provided in an embodiment of the present application;

[0042] Figure 2 It is a schematic diagram of the principle of a bicycle model provided in an embodiment of the present application;

[0043] Figure 3 It is a schematic diagram of a framework of a vehicle control method provided in an embodiment of the present application;

[0044] Figure 4 is a structural block diagram of a vehicle control device provided in an embodiment of the present application;

[0045] Figure 5 It is a hardware structure block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0047] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0048] It is understandable that in the specific implementation of this application, related data such as user information is involved. When the above embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0049] See also Figure 1 , which shows a flow chart of a vehicle control method provided in an embodiment of the present application. It should be noted that this specification provides method operation steps such as the embodiments or flow charts, but may include more or fewer operation steps based on conventional or non-creative work. The order of steps listed in the embodiments is only one way of executing the steps among many orders, and does not represent the only order of execution. When the system or product is executed in practice, it can be executed in the order shown in the embodiments or drawings or in parallel (for example, in a parallel processor or multi-threaded processing environment). Specifically, Figure 1 As shown, the method may include:

[0050] S101, when the target vehicle is in an unstable sliding state, obtaining current control information of the target vehicle.

[0051] Among them, the target vehicle is the current control object.

[0052] The unstable sliding state indicates that the friction between the target vehicle and the road surface is less than a first preset threshold. Specifically, the unstable sliding state is manifested as the target vehicle losing a certain degree of lateral stability during driving, causing the dynamic behavior of the target vehicle to become difficult to predict and control. This state usually occurs when the target vehicle turns quickly, accelerates or brakes suddenly, or drives on a slippery road.

[0053] Specifically, in an unstable sliding state, the friction between the target vehicle and the road surface is small, and the tires cannot fully grip the ground, causing the target vehicle to lose some or all of its lateral stability; the target vehicle may skid, that is, the rear wheels of the target vehicle lose grip and skid, which makes it difficult for the target vehicle to drive as the driver intends; the dynamic behavior of the target vehicle becomes unpredictable, and drift or loss of control may occur; the target vehicle may experience a large lateral offset during driving, that is, the target vehicle deviates from the intended route to a large extent.

[0054] In specific implementations, the causes of the unstable sliding state may include: when turning at high speed, the lateral force borne by the tire exceeds the friction between the tire and the road surface; when accelerating or braking suddenly on a slippery road surface, the friction between the tire and the road surface is insufficient; wet, icy or other adverse road conditions can cause the tire's grip to decrease.

[0055] The first preset threshold value is a key value used to distinguish whether the target vehicle is in an unstable sliding state. This threshold value is used to determine whether the friction between the target vehicle and the road surface is large enough to ensure that the target vehicle can maintain stable driving.

[0056] Among them, the current control information refers to the control state of the target vehicle at the current moment, including the engine output torque and the front wheel angle. Among them, the engine output torque is the torque generated by the engine, which directly affects the power output of the vehicle, and the front wheel angle is the steering angle of the front wheel, which affects the steering behavior of the vehicle. In specific implementations, the current control information may also include other control variables, such as braking torque and other parameters related to vehicle control.

[0057] S103, based on the current control information, simulating the movement of the target vehicle in an unstable sliding state to obtain actual state information of the target vehicle.

[0058] The actual state information refers to the dynamic information of the target vehicle in the unstable sliding state, which mainly includes the dynamic characteristics of the target vehicle such as position, speed, and direction, and is used to understand the dynamic behavior of the target vehicle in the unstable sliding state.

[0059] In specific implementation, a vehicle dynamics model can be established to consider factors such as the vehicle's mass distribution, suspension characteristics, and tire stiffness. A tire-road interaction model can also be established to simulate the interaction between tires and different road surfaces, including friction characteristics under various complex conditions such as dry / wet roads, ice and snow roads, etc. A driver behavior model can also be established to consider the driver's operations, such as steering angle, acceleration / deceleration pedal position, etc. Consider the influence of external environmental factors such as wind resistance, slope, and temperature. If the target vehicle is equipped with an ESP system or other active safety systems, the response behavior of these systems needs to be simulated.

[0060] Furthermore, the initial position, speed, direction and other parameters of the target vehicle are set according to the actual scenario, and the above model is used for real-time dynamic simulation to calculate the response of the target vehicle under specific input. Various state information of the target vehicle is continuously monitored, including dynamic characteristics such as the position, speed, and direction of the target vehicle, and may also include lateral and longitudinal acceleration, yaw rate, lateral offset, tire slip angle, etc.

[0061] In an exemplary implementation, the above step S103 may include:

[0062] Based on the current control information, the whole vehicle motion of the target vehicle in the unstable sliding state is simulated to obtain the whole vehicle state information;

[0063] Based on the current control information and vehicle status information, the tire movement of the target vehicle in an unstable sliding state is simulated to obtain the tire status information;

[0064] Based on the current control information and tire status information, the whole vehicle motion of the target vehicle in an unstable sliding state is simulated to obtain the actual status information.

[0065] The vehicle status information includes but is not limited to speed, acceleration, yaw angle, lateral offset, etc. Specifically, a dynamic model of the target vehicle is established, such as a bicycle model of a rear-wheel drive vehicle, and factors such as the vehicle's mass distribution, suspension characteristics, and tire stiffness are considered. The current control information is used as input, and the vehicle's motion response in an unstable sliding state is simulated through the dynamic model. The vehicle status information during the simulation process, such as longitudinal speed, lateral speed, yaw angle, lateral offset, etc., is monitored.

[0066] The tire status information includes but is not limited to the contact status between the tire and the road surface, the tire slip rate, etc. Specifically, based on the tire model, considering the mechanical properties and friction characteristics of the tire, using the current control information and the vehicle status information as input, simulating the response of the tire in an unstable sliding state, and monitoring the tire status information, such as the slip rate and contact pressure between the tire and the road surface.

[0067] Specifically, the tire status information is integrated with the current control information, and the integrated information is used to re-simulate the motion response of the vehicle in the unstable sliding state through the dynamic model, and the vehicle status information during the simulation is monitored, including but not limited to longitudinal speed, lateral speed, yaw angle, lateral offset, etc.

[0068] See also Figure 2, which shows a schematic diagram of the principle of a bicycle model provided in an embodiment of the present application. The bicycle model is a simplified version of the dynamic model, which is mainly used to describe the behavior of the vehicle in lateral motion. The bicycle model assumes that the movement of the vehicle on the horizontal plane can be simplified to the movement between the front and rear axles, focusing on the movement of the vehicle's center of mass, and ignoring some details, such as the longitudinal movement of the vehicle, the dynamic characteristics of the suspension, and the complexity of the vehicle's internal structure. This simplification makes the model easier to understand and analyze, especially when dealing with the stability and steering characteristics of the vehicle. The center of mass in the model represents the position of the center of gravity of the vehicle. The model takes into account the position of the front and rear axles and their relationship to the center of mass, and also considers the influence of the steering angle of the front wheel on the vehicle's motion. The bicycle model usually contains equations that describe the lateral and rotational motion of the vehicle, as well as equations of the tire model, such as linear or nonlinear models, to describe the interaction between the tire and the road surface. In the figure, a is the distance from the front axle to the center of mass, b is the distance from the rear axle to the center of mass, δ is the front wheel turning angle, α f is the front wheel slip angle, v f is the front wheel speed, v is the speed at the center of mass of the vehicle, β is the sideslip angle at the center of mass, r is the yaw rate, F xf , F xr are the front and rear axle longitudinal forces, F xr , F yr are the lateral forces of the front and rear axles respectively, path is the reference path, and path Tangent is the tangent of the reference trajectory.

[0069] A bicycle model of a rear-wheel drive vehicle is established. To locate the vehicle relative to the reference trajectory, the Frenet coordinate system is used. The vehicle state can be expressed as formula (1):

[0070]

[0071] The rate of change in the direction of the velocity vector is It can be expressed as formula (2):

[0072]

[0073] The input u of the bicycle model can be expressed as formula (3):

[0074]

[0075] Where m is the vehicle mass, I z is the moment of inertia of the vehicle around the Z axis, w r is the angular velocity of the rear wheel, I w is the moment of inertia of the tire, k ref is the curvature of the reference trajectory, τ is the engine output torque, G is the ratio of engine to wheel torque, and R is the wheel radius.

[0076] In highly dynamic nonlinear driving, it is necessary to understand the influence of longitudinal force and lateral force on wheel speed dynamics. In specific implementation, the Fiala tire brush model can be used. Assuming the tire is isotropic, the longitudinal force and lateral force of the tire can be estimated by the ratio of slip rate and sideslip angle. The distribution between longitudinal and lateral tire forces can be obtained as the ratio of each slip to the total slip σ, that is, formula (4):

[0077]

[0078] Among them, F total is the resultant lateral and longitudinal forces on the tire. α and ε are the lateral and longitudinal slips, respectively. Formulas (5) to (8) can be obtained from the bicycle model:

[0079]

[0080] ε f =0 (6)

[0081]

[0082]

[0083] Among them, α f , α r are the front axle slip angle and the rear axle slip angle, ε f , ε r are the front axle longitudinal slip rate and the rear axle longitudinal slip rate respectively.

[0084] The total slip σ can be expressed as formula (9):

[0085]

[0086] Formula (10) can be obtained from the Fiala tire brush model:

[0087]

[0088] Where C is the tire cornering stiffness, μ is the adhesion coefficient, and F z is the vertical load, σ sl The slip value at which the tire starts to slide completely.

[0089] It can be seen from the above technical solutions of the embodiment of the present application that the embodiment of the present application simulates and predicts the whole vehicle motion of the target vehicle in an unstable sliding state through the current control information to obtain the whole vehicle state information; then, the motion characteristics of the tire in this state are simulated using the current control information and the whole vehicle state information to obtain the tire state information; finally, the whole vehicle motion of the target vehicle in an unstable sliding state is simulated again by combining the current control information and the tire state information, thereby obtaining more accurate actual state information. This significantly improves the understanding of the behavior of the target vehicle in an unstable sliding state and provides important data support for designing a more effective vehicle control system.

[0090] In an exemplary embodiment, the current control information includes the current braking torque, the current engine torque and the current front wheel steering angle of the target vehicle; the above step S103 may include:

[0091] Under the assumption that the current braking torque is less than the second preset threshold, based on the current engine torque and the current front wheel angle, the movement of the target vehicle in an unstable sliding state is simulated to obtain actual state information of the target vehicle.

[0092] Among them, braking torque refers to the torque applied by the braking system to the wheels, that is, the torque generated by the brakes through friction to prevent the wheels from rotating. Specifically, braking torque determines the vehicle's ability to decelerate, including emergency braking and regular deceleration operations. When the braking torque is large, the vehicle decelerates faster; conversely, if the braking torque is small, the deceleration is slower. The current braking torque refers to the actual torque value generated by the braking system at the current moment.

[0093] Among them, engine torque refers to the torque output by the engine to the transmission system, which is a key indicator to measure the engine's power. Specifically, engine torque directly affects the vehicle's acceleration performance, climbing ability and carrying capacity. Higher engine torque means stronger acceleration ability and better climbing performance. The current engine torque refers to the actual torque value output by the engine at the moment.

[0094] The front wheel angle refers to the steering angle of the front wheels of the target vehicle relative to the straight-line driving position. Specifically, the front wheel angle determines the steering direction and flexibility of the vehicle. A larger front wheel angle can make the vehicle turn more easily, while a smaller angle allows the vehicle to keep driving in a straight line. The current front wheel angle refers to the actual steering angle of the front wheels at the current moment.

[0095] Among them, the second preset threshold indicates that the braking torque does not affect the movement of the target vehicle. Specifically, when the braking torque is less than the second preset threshold, it is assumed that the braking torque has little effect on the movement of the vehicle and can be ignored, which means that during the simulation, the influence of the braking torque can be regarded as a secondary factor, and the main focus is on the influence of the engine torque and the front wheel angle on the vehicle movement. In the specific implementation, in order to simplify the problem, it is assumed that the braking torque is zero throughout the sliding drift stage, the target vehicle is accelerated only by throttle control, and the rear wheels are losing longitudinal friction with the ground, which may cause the vehicle to enter a drift state.

[0096] Specifically, the vehicle dynamics model is used to consider factors such as the interaction between the tire and the road surface and the vehicle mass distribution to simulate the movement of the target vehicle in an unstable sliding state, and the actual state information of the target vehicle in the unstable sliding state is obtained. The actual state information reflects the actual movement of the vehicle in the unstable sliding state.

[0097] It can be seen from the above technical solutions of the embodiment of the present application that the embodiment of the present application simulates and predicts the motion characteristics of the target vehicle in an unstable sliding state based on the current engine torque and the current front wheel angle under the assumption that the current braking torque is less than the second preset threshold, thereby obtaining the actual state information of the target vehicle. By ignoring the secondary influence of the braking torque, more attention is paid to the influence of the engine torque and the front wheel angle on the vehicle motion, thereby improving the accuracy and practicality of the simulation.

[0098] S105, based on the current control information, simulating the movement of the target vehicle in a stable sliding state to obtain reference state information of the target vehicle.

[0099] The stable sliding state indicates that the friction between the target vehicle and the road surface is greater than or equal to a first preset threshold. Specifically, the stable sliding state is characterized by the friction between the target vehicle and the road surface being sufficient to support the target vehicle to travel along the expected trajectory, and the target vehicle can maintain a certain degree of lateral stability during driving, that is, the target vehicle can maintain good directional control while slightly sliding. In the stable sliding state, the target vehicle can maintain good lateral stability.

[0100] Specifically, in a stable sliding state, the target vehicle may experience a slight side slip, but the driver or the autonomous driving system can keep the target vehicle moving along the predetermined path by adjusting control variables such as steering angle and drive torque. Even in the case of side slip, the target vehicle can maintain good directional control, that is, the driving direction of the target vehicle is consistent with the intention of the driver or the autonomous driving system.

[0101] In specific implementations, the causes of the stable sliding state may include: smooth driving behavior, such as moderate acceleration, deceleration and steering; dry and clean road surfaces providing sufficient friction; and proper configuration of the vehicle's suspension system, tire type and pressure.

[0102] The reference state information reflects the dynamic performance of the target vehicle under ideal conditions. Specifically, the reference state information may include a reference trajectory, i.e., a pre-planned path that the target vehicle should follow. In a stable sliding state, the target vehicle travels along the reference trajectory under the control of the current control information. In a specific implementation, the reference trajectory may be a series of coordinate points or a parameterized curve equation. In a specific implementation, the reference trajectory may be calculated offline using multi-stage nonlinear optimization of the same vehicle model.

[0103] S107, using the actual state information and the reference state information as inputs of the controller of the target vehicle, performing optimization control with the optimization goal of reducing the gap between the actual state information and the reference state information within the coverage range of the road where the target vehicle is located, and obtaining target control information of the target vehicle.

[0104] The controller is the system component responsible for receiving vehicle status information and generating control signals to adjust vehicle behavior.

[0105] Among them, the coverage of the road where the target vehicle is located refers to the spatial range within the driving path and a certain area around it, which is used to describe the current location of the target vehicle and its potential travel path information.

[0106] The optimization goal is to reduce the gap between the actual state information and the reference state information by adjusting the control input of the target vehicle to improve the stability and controllability of the target vehicle in an unstable sliding state. By continuously simulating, comparing and optimizing the control strategy, it can be ensured that the target vehicle can maintain good handling performance in an unstable sliding state.

[0107] Among them, the optimization control aims to find a set of target control information so that the target vehicle can be as close to the controllable level in the stable sliding state as possible even in the unstable sliding state.

[0108] The target control information is the control state of the target vehicle at the next moment, including the engine output torque and the front wheel angle. The engine output torque is the torque generated by the engine, which directly affects the power output of the vehicle, and the front wheel angle is the steering angle of the front wheel, which affects the steering behavior of the vehicle. In a specific implementation, the current control information may also include other control variables, such as braking torque and other parameters related to vehicle control.

[0109] Specifically, the gap between the actual state information and the reference state information may include the distance s traveled by the target vehicle along the reference trajectory, the lateral deviation distance e of the target vehicle relative to the reference trajectory, and the angular deviation Δφ between the velocity direction at the center of mass of the vehicle and the reference trajectory.

[0110] Specifically, define an error function to quantify the gap between the actual state information and the reference state information. This error function can be the sum of the squares of the differences between the actual state information and the reference state information, or other forms of error measurement; determine the control variables, which refer to the control inputs of the target vehicle, such as the front wheel angle, braking torque, engine torque, etc. These variables will be optimized to minimize the error function; based on the vehicle dynamics model and / or other related models, establish a model that can describe the motion characteristics of the target vehicle in an unstable sliding state, such as a tire-road interaction model, a vehicle mass distribution model, etc.; solve the optimization problem through an optimization algorithm, and commonly used optimization algorithms include gradient descent, genetic algorithm, particle swarm optimization algorithm, linear quadratic regulator (Linear Quadratic Regulator, LQR), model predictive control (Model Predictive Control, MPC), etc.; use the actual state information and the reference state information as the input of the controller, use the optimization algorithm to solve the error function, and find the optimal control variable, that is, the target control information. This process may require multiple iterations to achieve satisfactory results.

[0111] In an exemplary implementation, the above step S107 may include:

[0112] Based on the actual state information and the reference state information, determining the degree to which the target vehicle deviates from the reference trajectory indicated by the reference state information, and obtaining the path tracking cost;

[0113] Based on the actual state information, determine the extent to which the target vehicle exceeds the coverage range and obtain the relaxation cost;

[0114] Determine the control cost of the controller based on the path tracking cost and the relaxation cost;

[0115] Optimize control with the goal of reducing control cost and determine target control information.

[0116] Among them, the reference trajectory indicated by the reference state information is an ideal path, which defines the route that the target vehicle should follow and the law of speed change over time. This path is usually pre-planned to meet the needs of a specific task, such as reaching a destination or completing a series of actions. Specifically, the reference trajectory contains spatial position information (i.e., path), and may also contain information on other state variables, such as speed, acceleration, yaw angle, etc. In a stable sliding state, the friction between the target vehicle and the road surface is sufficient to support the target vehicle to move along the reference trajectory in response to the current control information.

[0117] The path tracking cost is calculated based on the difference between the actual state information and the reference state information, which reflects the degree to which the target vehicle deviates from the reference trajectory. Specifically, by comparing the actual state information with the reference state information, the path tracking error can be calculated. The path tracking cost is a function of the path tracking error. The larger the error, the higher the path tracking cost. track It is mainly manifested in the lateral deviation distance e of the target vehicle relative to the reference trajectory and the angular deviation β of the front wheel relative to the reference trajectory. rel The weighted sum of the angular deviation Δφ between the velocity direction of the vehicle's center of mass and the reference trajectory, refer to the following formula (11):

[0118]

[0119] Among them, Q e , Q β , They are the weighting coefficients of lateral offset, reference sideslip angle, and heading angle deviation respectively.

[0120] Among them, coverage refers to the coverage of the road where the target vehicle is located, which is the spatial range in which the vehicle can operate safely or effectively.

[0121] Among them, the relaxation cost is to evaluate the degree to which the target vehicle exceeds the coverage range based on the actual state information, that is, whether the vehicle deviates from the predetermined safety or operation boundary. Specifically, the relaxation cost is calculated by checking whether the actual state information exceeds the allowed boundary or range. For example, if the actual position of the vehicle exceeds a preset safety area, a relaxation cost will be generated.

[0122] The step of determining the degree to which the target vehicle exceeds the coverage range based on the actual state information and obtaining the relaxation cost may include the following steps:

[0123] Within the coverage range of the target vehicle's road, in a direction perpendicular to the target vehicle's travel direction, a target offset range of the target vehicle is determined; the target offset range is a distance range of the target vehicle's center of mass relative to the center line of the target vehicle's road;

[0124] Based on the actual state information, the degree to which the target vehicle exceeds the target offset range is determined, and the relaxation cost is obtained.

[0125] The target offset range refers to the distance range of the target vehicle's center of mass relative to the center line of the road where the target vehicle is located, perpendicular to the target vehicle's driving direction, and is used to determine a safe or effective lateral offset range to ensure that the target vehicle remains near the center line of the road during driving and does not exceed the preset limit. In specific implementation, the road center line can be obtained through map data, visual sensors, or laser radar, and the lateral distance of the vehicle's center of mass relative to the road center line is calculated. A reasonable offset range is set based on safety and operational requirements. For example, a strip area with a fixed width can be set as the target offset range.

[0126] Specifically, the relaxation cost J over It is mainly used to prevent feasibility problems when the target vehicle temporarily exceeds the road boundary. Therefore, the road boundary is defined as a relaxed constraint with severe penalties, referring to the following formulas (12) and (13):

[0127] J over = W e ·DZ(e , e max ,e min ) 2 (12)

[0128]

[0129] Among them, e min 、e max is the minimum and maximum lateral displacement allowed, that is, the target displacement range is e min With e max Between, can be set from Frenet coordinates. e is the relaxation weight. Function DZ is a dead zone-like function. If the vehicle is within the boundary, the function is 0. If it exceeds the boundary, its cost will increase, which will serve as a penalty for lateral deviation.

[0130] Among them, the control cost is a measure that combines the path tracking cost and the relaxation cost to comprehensively evaluate the controller performance. Usually, the control cost is a linear combination of the path tracking cost and the relaxation cost, and the weight between them can be adjusted according to the specific application scenario. For example, the weight factor of the path tracking cost and the relaxation cost can be set to reflect the importance of path tracking accuracy and coverage in different application scenarios.

[0131] Specifically, the goal of optimization control is to reduce the control cost, that is, to minimize the path tracking error and the degree of exceeding the coverage range. By adjusting the controller parameters (such as the proportional, integral, and derivative terms of the PID (Proportional-Integral-Derivative Controller) controller) or other control strategies (such as model predictive control), the target control information that can minimize the control cost is found. Specifically, an optimization algorithm (such as gradient descent method, genetic algorithm, etc.) is used to iteratively improve the control parameters.

[0132] Among them, performing optimization control with reducing control cost as the optimization goal and determining target control information may include the following steps:

[0133] Based on the physical limitations of the actuators of the target vehicle, the numerical range corresponding to the target control information is determined to obtain static constraint conditions;

[0134] Based on the dynamic performance of the actuator, the numerical range corresponding to the rate of change of the target control information is determined to obtain the dynamic constraint condition;

[0135] Under the premise of satisfying static and dynamic constraints, optimal control is performed with the goal of reducing control cost to determine the target control information.

[0136] Among them, the actuator is the component in the control system that is responsible for converting control signals into physical actions. Specifically, in a vehicle control system, the actuator usually includes the engine, brake, steering system, etc. The actuator receives signals from the controller and generates corresponding actions based on these signals, such as increasing or decreasing engine torque, applying or releasing brake pressure, changing steering angle, etc.

[0137] The physical limitations of the actuator refer to the maximum and minimum output range that the actuator can produce. These limitations are determined by the physical characteristics and design of the actuator itself. The physical limitations define the operating range that the actuator can achieve, which is very important for designing the controller, because the controller needs to work within this range to ensure the safety and stability of the vehicle.

[0138] Among them, static constraints refer to the determination of the numerical range corresponding to the target control information based on the physical limitations of the actuators of the target vehicle (such as the engine, brakes, steering mechanism, etc.). Specifically, the physical limitations of the actuators are determined, such as the maximum torque, minimum torque of the engine, and the maximum brake pressure of the brake; according to the physical limitations of the actuators, a reasonable value is set for each control parameter. For example, if the maximum torque of the engine is 200N·m, the control signal range of the engine torque can be set to [-200N·m, 200N·m]. Specifically, the static constraints can be expressed as:

[0139]

[0140] Among them, δ is the front wheel turning angle, δ min is the minimum value of the front wheel turning angle, δ max is the maximum value of the front wheel angle, τ is the engine torque, τ min is the minimum value of the engine torque, τ is the maximum value of the engine torque, w r is the rear wheel speed, w r, is the minimum value of the rear wheel speed, w r, is the maximum rear wheel speed.

[0141] The dynamic performance of an actuator refers to the speed and accuracy with which the actuator responds to control signals, which involves the actuator's response time, stability, and consistency of performance under different working conditions. Specifically, the dynamic performance of an actuator is critical to ensuring that the vehicle can respond to control signals quickly and accurately. For example, if the actuator's response time is too long, the vehicle may not be able to make the necessary adjustments in time to deal with emergencies.

[0142] Among them, the dynamic constraint condition refers to the determination of the numerical range corresponding to the rate of change of the target control information based on the dynamic performance of the actuator. Specifically, the dynamic performance of the actuator is determined, for example, the rate of change of the engine torque, the rate of change of the brake torque, etc.; according to the dynamic performance of the actuator, a reasonable rate of change range is set for each control parameter. For example, if the maximum rate of change of the engine torque is 50N·m per second, then the rate of change range of the engine torque control signal can be set to [-50N·m / s, 50N·m / s]. Specifically, the dynamic constraint condition can be expressed as:

[0143]

[0144] in, is the rate of change of the front wheel steering angle, is the minimum value of the front wheel steering angle change rate, is the maximum value of the front wheel steering angle change rate, is the engine torque change rate, is the minimum value of the engine torque change rate, It is the maximum value of the engine torque change rate.

[0145] See also Figure 3, which shows a framework schematic diagram of a vehicle control method provided by an embodiment of the present application. The Frenet coordinate vehicle dynamics model in the sliding state and the vehicle body posture, position and other state errors of the reference model are given to the controller under the premise of satisfying static constraints and dynamic constraints, and an optimization algorithm (such as gradient descent method, genetic algorithm, particle swarm optimization algorithm, etc.) is used to find the target control information that can minimize the control cost. Through repeated iterations, the target control information is gradually adjusted until the optimal solution is found. The target control information is a control signal that minimizes the control cost on the basis of satisfying all constraints. The final target control information will be used to control the actuator of the vehicle to ensure that the vehicle can travel along the reference trajectory while remaining within a safe operating range. In a specific implementation, more cost functions can be defined to achieve a better planning solution set for the vehicle.

[0146] It can be seen from the above technical solutions of the embodiment of the present application that the embodiment of the present application calculates the degree of deviation of the vehicle from the ideal path through the actual state information and the reference state information, thereby obtaining the path tracking cost, and determines an allowable vertical offset range within the coverage range of the road where the vehicle is located, that is, the target offset range, which is determined by the distance between the center of mass of the vehicle and the center line of the road. If the actual position of the vehicle exceeds this range, the relaxation cost is calculated according to the degree of excess to quantify the degree of deviation of the vehicle from the predetermined driving range, and the path tracking cost and the relaxation cost are combined to form a comprehensive control cost. On the basis of considering the physical limitations and dynamic performance of the vehicle actuator, static and dynamic constraints are set. Under this constraint, the optimization algorithm is used to find the best control information that can minimize the control cost to achieve more accurate path tracking and more stable driving control. The refined cost calculation and optimization control strategy ensure that the autonomous driving vehicle can not only accurately drive on the predetermined route, but also effectively avoid the occurrence of situations exceeding the safe driving range, thereby improving the overall performance and safety of the autonomous driving system.

[0147] S109, controlling the target vehicle based on the target control information to update the current control information.

[0148] Specifically, the actual control input of the target vehicle is adjusted according to the target control information. After the control is implemented, the actual state information of the target vehicle is monitored and compared with the reference state information to evaluate the control effect. The control strategy is continuously adjusted through a closed-loop feedback mechanism to ensure that the actual state information of the target vehicle can continue to approach the reference state information. If it is found that the gap between the actual state information and the reference state information is still large, the control strategy needs to be further adjusted.

[0149] It can be seen from the above technical solutions of the embodiments of the present application that the embodiments of the present application determine the target control information of the target vehicle by simulating the movement of the target vehicle in an unstable sliding state and the movement of the target vehicle in a stable sliding state when the target vehicle is in an unstable sliding state, and reducing the difference between the two within the coverage range of the road where the target vehicle is located. The target vehicle is controlled based on the target control information, which can effectively control the movement of the vehicle in an unstable sliding state, improve the controllability and safety of the vehicle driving in an unstable sliding state, and is crucial to improving the performance of autonomous driving vehicles under extreme conditions, especially on wet or icy roads, or when emergency obstacle avoidance is required, and can help the vehicle complete driving tasks more safely and stably, thereby improving the overall driving safety and driving experience.

[0150] Corresponding to the vehicle control methods provided in the above-mentioned embodiments, the embodiment of the present application also provides a vehicle control device. Since the vehicle control device provided in the embodiment of the present application corresponds to the vehicle control methods provided in the above-mentioned embodiments, the implementation methods of the aforementioned vehicle control methods are also applicable to the vehicle control device provided in this embodiment and will not be described in detail in this embodiment.

[0151] See also Figure 4 , which is a schematic diagram of the structure of a vehicle control device provided in an embodiment of the present application, the device has the function of implementing the vehicle control method in the above method embodiment, and the function can be implemented by hardware or by hardware executing corresponding software. Figure 4 As shown, the device may include:

[0152] The information acquisition module 410 is used to acquire the current control information of the target vehicle when the target vehicle is in an unstable sliding state; the unstable sliding state indicates that the friction between the target vehicle and the road surface is less than a first preset threshold;

[0153] A first motion simulation module 420 is used to simulate the motion of the target vehicle in an unstable sliding state based on the current control information to obtain actual state information of the target vehicle;

[0154] The second motion simulation module 430 is used to simulate the motion of the target vehicle in a stable sliding state based on the current control information to obtain reference state information of the target vehicle; the stable sliding state indicates that the friction between the target vehicle and the road surface is greater than or equal to a first preset threshold;

[0155] An information determination module 440 is used to use the actual state information and the reference state information as inputs of a controller of a target vehicle, and to perform optimization control with the optimization goal of reducing the gap between the actual state information and the reference state information within the coverage range of the road where the target vehicle is located, thereby obtaining target control information of the target vehicle;

[0156] The control update module 450 is used to control the target vehicle based on the target control information to update the current control information.

[0157] In an exemplary embodiment, the information determination module includes:

[0158] A tracking cost module is used to determine the degree to which the target vehicle deviates from the reference trajectory indicated by the reference state information based on the actual state information and the reference state information, and obtain a path tracking cost;

[0159] A relaxation cost module is used to determine the degree to which the target vehicle exceeds the coverage range based on the actual state information to obtain the relaxation cost;

[0160] A control cost module, used to determine the control cost of the controller based on the path tracking cost and the relaxation cost;

[0161] The control information module is used to perform optimization control with the reduction of control cost as the optimization goal and determine the target control information.

[0162] In an exemplary embodiment, the relaxation cost module includes:

[0163] An offset range module is used to determine a target offset range of the target vehicle within the coverage range of the road where the target vehicle is located and in a direction perpendicular to the driving direction of the target vehicle; the target offset range is a distance range of the center of mass of the target vehicle relative to the center line of the road where the target vehicle is located;

[0164] The deviation degree module is used to determine the degree to which the target vehicle exceeds the target deviation range based on the actual state information, and obtain the relaxation cost.

[0165] In an exemplary embodiment, the control information module includes:

[0166] A static constraint module, used to determine the numerical range corresponding to the target control information based on the physical limitations of the actuators of the target vehicle, and obtain the static constraint conditions;

[0167] A dynamic constraint module is used to determine the numerical range corresponding to the rate of change of the target control information based on the dynamic performance of the actuator to obtain the dynamic constraint condition;

[0168] The optimization control module is used to perform optimization control with the reduction of control cost as the optimization goal and determine the target control information under the premise of satisfying static constraints and dynamic constraints.

[0169] In an exemplary embodiment, the first motion simulation module includes:

[0170] A first vehicle simulation module is used to simulate the vehicle motion of the target vehicle in an unstable sliding state based on the current control information to obtain vehicle state information;

[0171] A tire simulation module is used to simulate the tire movement of the target vehicle in an unstable sliding state based on the current control information and the vehicle state information to obtain the tire state information;

[0172] The second vehicle simulation module is used to simulate the whole vehicle motion of the target vehicle in an unstable sliding state based on the current control information and tire state information to obtain actual state information.

[0173] In an exemplary embodiment, the current control information includes the current braking torque, the current engine torque and the current front wheel steering angle of the target vehicle; the first motion simulation module includes:

[0174] The third motion simulation module is used to simulate the movement of the target vehicle in an unstable sliding state based on the current engine torque and the current front wheel angle under the assumption that the current braking torque is less than a second preset threshold value, so as to obtain the actual state information of the target vehicle; the second preset threshold value indicates that the braking torque does not affect the movement of the target vehicle.

[0175] It should be noted that the device provided in the above embodiment, when implementing its functions, is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0176] An embodiment of the present application provides an electronic device, which includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement any one of the vehicle control methods provided in the above method embodiments.

[0177] The memory can be used to store software programs and modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, application programs required for functions, etc.; the data storage area can store data created according to the use of the device, etc. In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory can also include a memory controller to provide the processor with access to the memory.

[0178] The method embodiments provided in the embodiments of the present application may be executed in a computer terminal, a server or a similar computing device, that is, the above-mentioned electronic device may include a computer terminal, a server or a similar computing device. Figure 5 is a hardware structure block diagram of a computer device for running a vehicle control method provided by an embodiment of the present invention, such as Figure 5 As shown, the internal structure of the computer device may include but is not limited to: a processor, a network interface and a memory. The processor, network interface and memory in the computer device may be connected via a bus or other means. Figure 5 The example of connecting through bus is taken in the following.

[0179] Among them, the processor (or CPU (Central Processing Unit)) is the computing core and control core of the computer device. The network interface may optionally include a standard wired interface, a wireless interface (such as WI-FI, a mobile communication interface, etc.). Memory is a memory device in a computer device for storing programs and data. It is understandable that the memory here can be a high-speed RAM storage device or a non-volatile memory device (non-volatile memory), such as at least one disk storage device; optionally, it can also be at least one storage device located away from the aforementioned processor. The memory provides a storage space, which stores the operating system of the electronic device, which may include but is not limited to: Windows system (an operating system), Linux (an operating system), Android (Android, a mobile operating system) system, IOS (a mobile operating system) system, etc., and the present invention is not limited to this; and, in the storage space, one or more instructions suitable for being loaded and executed by the processor are also stored, and these instructions can be one or more computer programs (including program codes). In the embodiment of this specification, the processor loads and executes one or more instructions stored in the memory to implement the vehicle control method provided in the above method embodiment.

[0180] An embodiment of the present application also provides a computer-readable storage medium, which can be set in an electronic device to store at least one instruction or at least one program related to implementing a vehicle control method. The at least one instruction or the at least one program is loaded and executed by the processor to implement any one of the vehicle control methods provided in the above method embodiments.

[0181] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

[0182] It should be noted that the above-mentioned sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above-mentioned specific embodiments of this specification are described. Other embodiments are within the scope of the attached claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0183] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0184] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0185] The above are only preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A vehicle control method, characterized in that: The method comprises: When the target vehicle is in an unstable sliding state, obtaining current control information of the target vehicle; the unstable sliding state indicates that the friction between the target vehicle and the road surface is less than a first preset threshold; Based on the current control information, simulating the movement of the target vehicle in the unstable sliding state to obtain actual state information of the target vehicle; Based on the current control information, simulating the movement of the target vehicle in a stable sliding state to obtain reference state information of the target vehicle; the stable sliding state indicates that the friction between the target vehicle and the road surface is greater than or equal to the first preset threshold; The actual state information and the reference state information are used as inputs of a controller of the target vehicle, and optimization control is performed with the optimization goal of reducing the gap between the actual state information and the reference state information within the coverage range of the road where the target vehicle is located, thereby obtaining target control information of the target vehicle; The target vehicle is controlled based on the target control information to update the current control information.

2. The vehicle control method according to claim 1, characterized in that: The actual state information and the reference state information are used as inputs of the controller of the target vehicle, and optimization control is performed with the optimization goal of reducing the gap between the actual state information and the reference state information within the coverage range of the road where the target vehicle is located, to obtain target control information of the target vehicle, including: Based on the actual state information and the reference state information, determining the degree to which the target vehicle deviates from the reference trajectory indicated by the reference state information, and obtaining a path tracking cost; Based on the actual state information, determining the extent to which the target vehicle exceeds the coverage range, and obtaining a relaxation cost; Determining a control cost of the controller based on the path tracking cost and the relaxation cost; Optimizing control is performed with the reduction of the control cost as the optimization goal, and the target control information is determined.

3. The vehicle control method according to claim 2, characterized in that: The determining, based on the actual state information, the degree to which the target vehicle exceeds the coverage range and obtaining the relaxation cost comprises: Within the coverage range of the road where the target vehicle is located, in a direction perpendicular to the driving direction of the target vehicle, determine the target offset range of the target vehicle; the target offset range is the distance range of the center of mass of the target vehicle relative to the center line of the road where the target vehicle is located; Based on the actual state information, the degree to which the target vehicle exceeds a target offset range is determined to obtain the relaxation cost.

4. The vehicle control method according to claim 2, characterized in that: The optimizing control with reducing the control cost as the optimization target and determining the target control information includes: Based on the physical limitations of the actuator of the target vehicle, determining the numerical range corresponding to the target control information to obtain a static constraint condition; Based on the dynamic performance of the actuator, determining a numerical range corresponding to the rate of change of the target control information to obtain a dynamic constraint condition; On the premise of satisfying the static constraint condition and the dynamic constraint condition, optimization control is performed with reducing the control cost as the optimization goal, and the target control information is determined.

5. The vehicle control method according to claim 1, characterized in that: The simulating the movement of the target vehicle in the unstable sliding state based on the current control information to obtain the actual state information of the target vehicle includes: Based on the current control information, simulating the whole vehicle motion of the target vehicle in the unstable sliding state to obtain whole vehicle state information; Based on the current control information and the vehicle state information, simulating the tire movement of the target vehicle in the unstable sliding state to obtain tire state information; Based on the current control information and the tire state information, the whole vehicle motion of the target vehicle in the unstable sliding state is simulated to obtain the actual state information.

6. The vehicle control method according to claim 1, characterized in that: The current control information includes the current braking torque, the current engine torque and the current front wheel steering angle of the target vehicle; based on the current control information, simulating the movement of the target vehicle in the unstable sliding state to obtain the actual state information of the target vehicle includes: Under the assumption that the current braking torque is less than a second preset threshold, the movement of the target vehicle in the unstable sliding state is simulated based on the current engine torque and the current front wheel steering angle to obtain the actual state information of the target vehicle; the second preset threshold indicates that the braking torque does not affect the movement of the target vehicle.

7. A vehicle control device, characterized in that: The device comprises: An information acquisition module, used for acquiring current control information of the target vehicle when the target vehicle is in an unstable sliding state; the unstable sliding state indicates that the friction between the target vehicle and the road surface is less than a first preset threshold; A first motion simulation module, configured to simulate the motion of the target vehicle in the unstable sliding state based on the current control information, and obtain actual state information of the target vehicle; A second motion simulation module is used to simulate the motion of the target vehicle in a stable sliding state based on the current control information to obtain reference state information of the target vehicle; the stable sliding state indicates that the friction between the target vehicle and the road surface is greater than or equal to the first preset threshold; an information determination module, configured to use the actual state information and the reference state information as inputs of a controller of the target vehicle, and to perform optimization control with the optimization goal of reducing the gap between the actual state information and the reference state information within the coverage range of the road where the target vehicle is located, thereby obtaining target control information of the target vehicle; A control update module is used to control the target vehicle based on the target control information to update the current control information.

8. An electronic device, characterized in that: The invention comprises a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement the vehicle control method according to any one of claims 1 to 6.

9. A computer-readable storage medium, wherein at least one instruction or at least one program is stored in the storage medium, and the at least one instruction or the at least one program is loaded and executed by a processor to implement the vehicle control method according to any one of claims 1 to 6.

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

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