A human-machine co-driving vehicle drift assist control method, system and automobile

Abstract

This invention discloses a vehicle drift assist control method, system, and automobile for human-machine co-driving, relating to the fields of vehicle chassis active control and advanced driver assistance technologies. The method includes: activating the drift assist mode and acquiring steering wheel angle and vehicle state variables; calculating the desired turning radius based on the steering wheel angle and performing boundary verification; retrieving and locking the target drift equilibrium point in a database and extracting the reference target state; constructing the core equation based on the state error and the composite control law, and combining the rear axle tire adhesion circle constraint to execute two-state tire force distribution to solve for the target axle force; generating commands through inverse dynamics calculation and sending them to the chassis actuators for closed-loop servo tracking. This invention intuitively maps steering wheel input to the drift turning radius and dynamically switches between two-state force distribution, which has the beneficial effects of lowering the threshold for extreme handling, improving attitude tracking robustness, and ensuring driving safety.

Description

Technical Field

[0001] This invention relates to the field of active control of vehicle chassis and advanced driver assistance technology, and in particular to a human-machine co-driving vehicle drift assistance control method, system and automobile. Background Technology

[0002] With the continuous development of intelligent vehicle technology, more and more driver assistance systems that improve vehicle performance are being widely used. Among these systems, active safety systems have always occupied an important position in the core functions of the vehicle.

[0003] Currently, modern vehicle active safety systems, represented by AEB (Autonomous Emergency Braking) and AES (Autonomous Emergency Steering), significantly improve vehicle stability by controlling the vehicle's yaw characteristics in a closed loop, keeping the vehicle within a preset "stable" range. This prevents skidding and fishtailing, thus effectively enhancing driving safety.

[0004] However, existing active safety systems essentially sacrifice vehicle maneuverability for safety. That is, when a vehicle approaches its tire grip limits or exhibits a large tendency to yaw, traditional stability control systems forcibly suppress sideslip and fishtailing, preventing the vehicle from entering a high-maneuverability state. For highly intelligent and high-performance vehicles, this control strategy is too conservative, severely limiting the vehicle's handling performance and making it unable to cope with extreme conditions such as low-friction surfaces, high-speed lane changes, and obstacle avoidance in confined spaces.

[0005] Inspired by the extreme driving skills of professional race car drivers, drifting technology offers a new approach to improving vehicle safety under extreme conditions. Vehicle drifting refers to a dynamic process in which the driver, through precise vehicle control, intentionally causes the rear wheels to lose all or part of their traction while driving, resulting in a controllable lateral slip while still maintaining control of the vehicle's trajectory.

[0006] Its typical characteristics include: 1. The rear wheels operate under significant slip conditions, and the lateral force is adjusted by controlling the longitudinal slip ratio; 2. The vehicle's center of gravity sideslip angle is large and persistent, typically reaching 20° or even higher; 3. The vehicle remains controllable even under large sideslip angles and large yaw rates; 4. The vehicle's attitude adjustment is highly dynamic, and there is a noticeable "counter-steering" phenomenon.

[0007] By making reasonable use of drifting techniques, the maneuverability and controllability of vehicles under extreme conditions can be significantly improved. For example, in situations such as turning on roads with low coefficient of friction, cornering in narrow spaces, and extreme obstacle avoidance scenarios, the vehicle's posture can be quickly adjusted by drifting, thereby increasing the success rate of obstacle avoidance.

[0008] Existing technologies have made some progress in automatic drift control. For example, related research and patent literature have proposed control frameworks for automatic drift, analyzed the steady-state drift mechanism of vehicles, and studied vehicle dynamics models under extreme conditions, drift equilibrium point solutions, and extreme drift control.

[0009] However, existing technologies still have at least the following problems in practical applications: 1. The human-computer interaction logic is counterintuitive, with a single control dimension and an unintuitive interaction method.

[0010] Current technology only allows input of the desired drift sideslip angle via the central control screen, lacking comprehensive control over key drift state variables such as turning radius and yaw rate. Adjusting the drift target via the touchscreen during actual driving is neither intuitive nor safe, and does not conform to the driver's intuitive "steering wheel or pedals – vehicle posture" model.

[0011] Furthermore, the operating logic of existing technology contradicts the everyday intuition of ordinary drivers. In real drifting, maintaining a drift posture usually requires the driver to counter-steer the steering wheel, but ordinary drivers find it difficult to accurately grasp the timing and extent of this counter-steering. Existing technology does not handle the mapping between the steering wheel and the front wheel steering angle, resulting in an excessively high barrier to entry, making it difficult to initiate or maintain a drift.

[0012] 2. The control strategy is discrete and rigid, and does not achieve closed-loop control of the entire process.

[0013] Existing technologies only lower the threshold for entering a drift state, but do not achieve full-process drift control. For example, existing technologies mainly optimize rear-wheel drive or braking torque to make it easier for vehicles to enter a drift state, but do not achieve closed-loop control of the entire drift process. Once the vehicle enters a large sideslip angle state, the driver still needs extremely high driving skills to maintain a controllable drift posture, which is difficult for ordinary users to use safely. At the same time, drift intensity control is discrete and rigid, lacking continuous human-machine co-driving capability. For example, some solutions pre-divide multiple drift levels, each corresponding to different center of gravity sideslip angle (vehicle sideslip angle) control targets. This solution cannot continuously adjust the drift intensity according to the driver's real-time intentions during the drift, lacking the flexibility of human-machine co-driving.

[0014] 3. It relies heavily on expensive, high-spec chassis hardware, resulting in high system engineering implementation costs and poor applicability.

[0015] Existing technologies rely excessively on complex hardware such as rear-wheel steering and multi-motor independent drive in order to forcibly maintain vehicle posture or perform dynamic compensation. This results in high overall vehicle manufacturing costs and makes it impossible to deploy and popularize them on a large scale in mainstream mass-produced conventional rear-wheel drive models, leading to high costs and poor applicability.

[0016] Therefore, those skilled in the art are dedicated to developing a human-machine co-driving vehicle drift assistance control method, system, and vehicle to overcome the inherent defects of existing technologies, such as counterintuitive operating logic, discontinuous drift control process, and high dependence on complex chassis hardware. Summary of the Invention

[0017] In view of the above-mentioned deficiencies of the prior art, the technical problem to be solved by the present invention is that traditional vehicle drift control systems are difficult to balance the driver's intuitive control intentions with the dynamic stability of the vehicle under extreme driving conditions. They suffer from problems such as human-machine interaction logic conflicts, lack of refined dynamic closed-loop allocation for tire saturation under extreme conditions, and high cost of mass production due to high dependence on expensive and complex chassis actuators.

[0018] To achieve the above objectives, this invention provides a vehicle drift assist control method for human-machine co-driving, comprising the following steps: Step S101: The control system acquires the driver's drift request signal to activate the drift assist mode, and during the activation of the drift assist mode, it collects the steering wheel angle signal and the vehicle's current actual motion state in real time; Step S102: The control system calculates the desired turning radius based on the steering wheel angle signal, performs real-time boundary verification on the desired turning radius to obtain the final desired turning radius, and then retrieves and locks the target drift balance point in a preset drift balance point database based on the current actual longitudinal vehicle speed, extracting the corresponding reference target state; Step S103: The control system calculates the state error based on the actual motion state and the reference target state, constructs the core control equation based on the composite control law, and performs two-state tire force distribution in conjunction with the rear axle tire adhesion circle constraint, solving for the target front axle lateral force and the target rear axle longitudinal force; Step S104: The control system performs inverse dynamics calculation based on the target front axle lateral force and the target rear axle longitudinal force, generates actuator control commands and sends them to the vehicle chassis actuators to perform closed-loop servo tracking of the target drift balance point.

[0019] In a preferred embodiment of the present invention, step S102, which calculates the desired turning radius based on the steering wheel angle signal, includes: the control system acquiring the actual steering wheel angle signal. Then, the dead zone of the drift intention is determined; if the absolute value of the steering wheel angle is... If the dead zone is less than the preset threshold, the control system determines that the driver has no obvious intention to drift and will adjust the desired turning radius. When the maximum value is set to a preset level, the drift controller does not intervene in the near-linear state; if the absolute value of the steering wheel angle is... If the value is greater than or equal to the dead zone threshold, the control system calculates the initial expected turning radius based on the steering wheel angle range, the turning radius range, and the actual steering wheel angle using the following formula: ,in, The maximum turning radius, Minimum turning radius, The maximum steering wheel angle is preset; the turning direction of the vehicle, whether left or right, is determined based on the sign of the steering wheel angle.

[0020] In a preferred embodiment of the present invention, step S102, in which the control system performs real-time boundary verification of the desired turning radius, includes: the control system checks the desired turning radius in real time. Whether the physical constraint of the minimum safe turning radius is met, and whether the current actual vehicle speed does not exceed the safe upper limit speed of the vehicle under the current road surface adhesion conditions; if the desired turning radius is... If the turning radius exceeds the feasible range, the control system will limit it, forcibly restricting it to the nearest safe and feasible value to obtain the final desired turning radius. It will also issue over-limit feedback prompts to the driver through the human-machine interface or the vehicle's dynamic behavior.

[0021] In a preferred embodiment of the present invention, a preset drift balance point database is pre-constructed through offline calculation. The construction steps include: discretely selecting multiple vehicle speeds within the envelope range of a given vehicle speed and front wheel steering angle parameters. and front wheel angle; for each pair Using the vehicle dynamics model and the Fiala tire model, a set of nonlinear equations with zero vehicle state derivatives is solved to obtain a feasible solution set. For each equilibrium point in the solution set, the corresponding steady-state turning radius is calculated using the following formula. And record: Step S102, retrieving and locking the target drift balance point in the preset drift balance point database, includes: the control system using nearest neighbor search or multidimensional interpolation algorithm, based on the current actual longitudinal vehicle speed. Retrieve steady-state turning radius from the corresponding database slice Closest to the desired turning radius The target front wheel steering angle is determined, and during the search process, the steady-state equilibrium point corresponding to the normal driving steering is automatically avoided and filtered out, while the non-steady-state equilibrium point corresponding to the extreme lateral deviation state is accurately selected and locked as the target drift equilibrium point.

[0022] In a preferred embodiment of the present invention, the reference target state includes the target centroid sideslip angle. β refTarget yaw rate r ref and the longitudinal speed of the target vehicle v x, ref The actual motion state quantities include the actual sideslip angle, actual yaw rate, and actual vehicle longitudinal velocity; the state errors calculated in step S103 include: sideslip angle error. Yaw rate error Vehicle longitudinal speed error Step S103 also includes the dynamic design of the desired yaw rate: the control system introduces a dynamic compensation mechanism for sideslip angle error based on the target yaw rate, and calculates the desired yaw rate after dynamic error correction according to the following formula: ,in, The gain is the preset side slip angle control.

[0023] In a preferred embodiment of the present invention, the construction of the core control equation in step S103 includes: the first-order decay expected dynamic of the yaw rate error introduced by the control system: ,in, For yaw rate feedback gain; combined with the vehicle yaw dynamics equations: ,in, This is the distance from the rear axle to the center of mass. This is the distance from the front axle to the center of gravity. , The lateral forces are those of the front and rear wheels, respectively; the following feedforward-feedback core control equations are constructed: ,in, For the reason and Coefficients determined by parameters, This refers to the feedforward term related to the equilibrium point.

[0024] In a preferred embodiment of the present invention, the execution of two-state tire force distribution in step S103 specifically includes the control system dividing into mode one and mode two based on whether the required lateral force of the front wheels reaches the physical adhesion limit of the front wheels: When the control system determines that the front wheels of the vehicle are in an unsaturated adhesion state, it operates in mode one: First, the longitudinal speed controller is used to calculate the target rear axle longitudinal force with the goal of eliminating longitudinal speed error; Second, based on the rear axle tire adhesion circle constraint, combined with the road adhesion coefficient, the rear axle vertical load, and the target rear axle longitudinal force, the rear axle lateral force is calculated, and it is substituted into the simultaneous equations to solve for the target front axle lateral force; When the control system determines that the required lateral force of the front wheels reaches or exceeds the physical adhesion limit of the front wheels, it automatically switches to mode two: The target front axle lateral force is fixed to the value corresponding to the physical adhesion limit of the front wheels, and it is substituted into the simultaneous equations to solve for the target rear axle lateral force in reverse, and then based on the rear axle tire adhesion circle constraint, combined with the road adhesion coefficient, the rear axle vertical load, and the target rear axle lateral force, the target rear axle longitudinal force that meets the joint adhesion limit is calculated in reverse.

[0025] In a preferred embodiment of the present invention, step S104 specifically includes: the control system converts the target front axle lateral force into a front wheel slip angle using a reverse tire model, and calculates the target front wheel steering angle command in combination with vehicle kinematics; the control system calculates the target rear wheel torque command based on the target rear axle longitudinal force and the effective tire radius; the control system cyclically executes steps S101 to S104 for closed-loop tracking, and automatically exits the assisted drift mode when it detects that a drift request has been cancelled or that there is a safety risk.

[0026] In another preferred embodiment of the present invention, the present invention also provides a human-machine co-driving vehicle drift assistance control system, the system comprising: a signal acquisition module configured to acquire a driver's drift request signal to activate the drift assistance mode, and to collect the steering wheel angle signal and the vehicle's current actual motion state in real time during the drift assistance mode; a working point determination module configured to calculate the desired turning radius based on the steering wheel angle signal, and to perform real-time boundary verification on the desired turning radius to obtain the final desired turning radius, and then retrieve and lock the target drift balance point in a preset drift balance point database based on the current actual longitudinal vehicle speed, and extract the corresponding reference target state; an attitude control module configured to calculate the state error based on the actual motion state and the reference target state, construct the core control equation based on the composite control law, and combine the rear axle tire adhesion circle constraint to execute the two-state tire force distribution, and solve for the target front axle lateral force and the target rear axle longitudinal force; and a command calculation module configured to perform inverse dynamic calculation based on the target front axle lateral force and the target rear axle longitudinal force, generate actuator control commands and send them to the vehicle chassis actuators to perform closed-loop servo tracking of the target drift balance point.

[0027] In another preferred embodiment of the present invention, the present invention also provides an automobile, the automobile including a physical chassis actuator, an on-board sensor assembly, and a control system, wherein: the on-board sensor assembly is communicatively connected to the control system and configured to collect the current actual motion state of the vehicle in real time and feed it back to the control system; the physical chassis actuator includes at least a front wheel steer-by-wire system and a rear wheel drive / brake actuator, which are communicatively connected to the control system respectively; the control system is equipped with the aforementioned human-machine co-driving vehicle drift assist control system, or the control system is configured to execute the aforementioned human-machine co-driving vehicle drift assist control method; the control system controls the automobile to work in conventional stability control logic in normal driving mode, and switches to human-machine co-driving drift assist control logic when it receives a drift request signal actively triggered by the driver, and sends the calculated actuator control commands to the front wheel steer-by-wire system and the rear wheel drive / brake actuator for execution respectively.

[0028] This invention has the following beneficial technical effects: it realizes human-machine co-driving closed-loop interaction that conforms to human intuition, reducing the threshold of extreme control; it has created a unique two-state tire force distribution mechanism that balances extremely high control robustness with low mass production cost; it has multi-dimensional dynamic compensation for state errors to achieve closed-loop tracking of extreme postures; and it has constructed a complete safety boundary verification and smooth exit mechanism to fully ensure the driving safety of the entire vehicle.

[0029] The following will further explain the concept, specific structure, and technical effects of the present invention in conjunction with the accompanying drawings, so as to fully understand the purpose, features, and effects of the present invention. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of a three-degree-of-freedom vehicle dynamics model in a preferred embodiment of the present invention; Figure 2 This is a schematic diagram of the spatial distribution of drift equilibrium points in a preferred embodiment of the present invention; Figure 3 This is a schematic diagram of the mapping relationship between the turning radius and the front wheel steering angle corresponding to the drift balance point in a preferred embodiment of the present invention; Figure 4 This is a schematic diagram of a human-machine co-driving drift collaborative control framework in a preferred embodiment of the present invention; Figure 5 This is a flowchart of a human-machine co-driving assisted drift control method in a preferred embodiment of the present invention; Figure 6 This is a schematic diagram of the vehicle's trajectory under a variable posture U-shaped bend drift condition in a preferred embodiment of the present invention; Figure 7 This is a schematic diagram illustrating the relationship between driver input and vehicle response under a U-shaped bend condition in a preferred embodiment of the present invention. Detailed Implementation

[0031] The following description, with reference to the accompanying drawings, illustrates several preferred embodiments of the present invention to make its technical content clearer and easier to understand. The present invention can be embodied in many different forms, and the scope of protection of the present invention is not limited to the embodiments mentioned herein.

[0032] In the accompanying drawings, components with the same structure are indicated by the same numerical designation, and components with similar structures or functions are indicated by similar numerical designations. The dimensions and thicknesses of each component shown in the drawings are arbitrary, and the present invention does not limit the dimensions and thicknesses of each component. To make the illustrations clearer, the thickness of some components has been appropriately exaggerated in the drawings.

[0033] I. Vehicle Dynamics Model

[0034] like Figure 1 In a preferred embodiment of the present invention, the human-machine co-driving vehicle drift assist control system is built on the vehicle dynamics model and tire model at the underlying control logic level.

[0035] The vehicle dynamics model, preferably, employs a three-degree-of-freedom (lateral, longitudinal, and yaw) vehicle dynamics model. The vehicle dynamics model states include, but are not limited to, the vehicle's longitudinal velocity. v x , centroid side slip angle β and yaw rate r The input is the front wheel steering angle. δ and longitudinal force of the rear wheel F xr The vehicle dynamics equations are:

[0036] in, The rate of change of the centroid sideslip angle. The yaw acceleration is... For longitudinal acceleration, For vehicle quality, Let the moment of inertia be about the vertical axis. This is the distance from the rear axle to the center of mass. This is the distance from the front axle to the center of gravity. , These are the lateral forces of the front and rear wheels, respectively. This refers to the longitudinal force on the rear wheel. The small steering angle assumption is used here, i.e. .

[0037] The tire model, preferably the Fiala tire model, is used to characterize the strongly nonlinear characteristics of the tire under drifting conditions. The Fiala tire model equations are:

[0038] in, C α Let z be the tire lateral stiffness, and z be the tangent of the tire slip angle. F z For vertical loads, μ For road surface adhesion coefficient, α This refers to the tire slip angle; α sl To reach the slip angle threshold for slip saturation, ξ This is a slippage correction factor.

[0039] II. Solving for and Spatial Distribution of Drift Equilibrium Points

[0040] Based on the vehicle dynamics model and tire model constructed above, the human-machine co-driving vehicle drift assist control system pre-solves the drift equilibrium point and analyzes its spatial distribution characteristics, thereby constructing an equilibrium point database that meets the real-time call requirements of the vehicle.

[0041] The drift equilibrium point of this invention refers to the point at a given vehicle speed. With front wheel steering angle Under these conditions, the vehicle reaches a steady state in all its main states, namely:

[0042] In this paper, the subscript eq represents the drift equilibrium point, that is, the parameters when the vehicle's main state reaches a steady state.

[0043] Substituting into the vehicle dynamics equations, we get:

[0044] Substituting the aforementioned steady-state conditions into the vehicle dynamics equations and combining them with the nonlinear Fiala tire model, a set of nonlinear algebraic equations can be constructed to characterize the vehicle's steady-state properties.

[0045] Specifically, the system of algebraic equations initially contains seven physical variables (front wheel lateral force). Rear wheel lateral force yaw rate r vehicle longitudinal speed v x Rear wheel longitudinal force Front wheel cornering δ and centroid side slip angle β (and three equality constraints, including the front wheel lateral force) and rear wheel Lateral forces can be characterized by other kinematic variables through a tire model, ultimately reducing the system to five physical variables.

[0046] During the solution process, the front wheel steering angle is fixed. δ and vehicle longitudinal speed v x With the degrees of freedom of this system of equations fully constrained, the corresponding equilibrium point can be obtained.

[0047] The spatial distribution image of the drift equilibrium point obtained when the vehicle's longitudinal speed is 8 m / s is as follows. Figure 2 As shown, taking the spatial distribution image of the equilibrium point of the centroid sideslip angle as an example, when the vehicle's longitudinal speed... v x The front wheel steering angle is 8 m / s. δ When it is 20°, the corresponding centroid sideslip angle β It is 30°. Similarly, the spatial distribution images of the yaw rate equilibrium point, the spatial distribution images of the rear wheel longitudinal force equilibrium point, and the spatial distribution images of the rear wheel lateral force equilibrium point can be used to determine the yaw rate when the vehicle's longitudinal velocity... v x The front wheel steering angle is 8 m / s. δ When the angle is 20°, the corresponding yaw rate is r -0.6 rad / s, longitudinal force of the rear wheel 4000N, front wheel lateral force It is -5000N.

[0048] In the drift equilibrium point spatial distribution image, "o" refers to the equilibrium point during normal driving and steering, i.e., the steady-state equilibrium point; the "o" in the drift equilibrium point spatial distribution image... "" refers to a non-steady-state equilibrium point. In this invention, in order to make the vehicle drift, it is necessary to control the vehicle at the solution of the non-steady-state equilibrium point.

[0049] These values ​​together form the solution to the aforementioned physical variables. Each set of physical variables together constitutes a complete target operating point, so that the vehicle drift assist control system of human-machine co-driving can control the vehicle's state on these solutions.

[0050] III. Calculation of Turning Radius

[0051] Based on the drift equilibrium point mentioned above, the corresponding turning radius can be mapped to it. This provides a basis for an intuitive mapping between the steering wheel angle and the drift balance point.

[0052] Specifically, Under steady-state circular motion, the vehicle's sideslip angle is... The yaw rate is The longitudinal velocity is The angle between the vehicle's center of gravity velocity direction and the vehicle's longitudinal axis is... The scalar velocity of the center of mass is:

[0053] During stable circular motion, the geometric relations satisfy:

[0054] This gives the turning radius. :

[0055] IV. Intuitive mapping from steering wheel angle to front wheel angle

[0056] To align with the driver's intuition that "the more you turn the steering wheel, the smaller the turning radius," this invention no longer directly maps the steering wheel angle to the front wheel angle. Instead, it introduces "turning radius" as an intermediate physical quantity to achieve the following mapping link: Steering wheel angle Desired turning radius The desired balance is found by searching / interpolating in the equilibrium point database. drift equilibrium point Obtain the target front wheel steering angle and target state .

[0057] Here, since the controller's control objective is the drift equilibrium point, the subscript... ref and eq They are equivalent in a physical sense.

[0058] The specific implementation process of the above intuitive mapping is as follows: 1. Define the working range of the steering wheel angle and the safe range of the turning radius. The working range of the steering wheel angle is shown below:

[0059] in Can be taken Or other reasonable rotation angles.

[0060] The safe range for turning radius is shown below:

[0061] For a given drift equilibrium point, the range of achievable turning radii is known and can be obtained using the formula in "III. Calculation of Turning Radius". Therefore, by plotting a series of turning radii at a given vehicle speed, the range of turning radii can be obtained.

[0062] like Figure 3As shown, when the vehicle's longitudinal speed is 8 m / s, the achievable turning radii in the positive and negative directions are ±[12,20].

[0063] 2. Construct a monotonic mapping relationship to solve for the desired turning radius.

[0064] Monotonic mapping relationships, including but not limited to linear mappings, are established based on the aforementioned working range and safety range. The mapping formula is as follows:

[0065] in, This indicates the steering wheel dead zone; the drift controller does not intervene when the steering wheel is in the dead zone. The turning direction is determined based on the actual steering wheel direction (left or right), thus determining the desired turning radius. The positive and negative signs.

[0066] 3. Perform a search and matching process from the desired turning radius to the drift equilibrium point.

[0067] In the pre-set equilibrium point database, for each obtained drift equilibrium point, its corresponding steady-state turning radius can be calculated using the following formula:

[0068] Obtain the current longitudinal speed of the vehicle And the expected turning radius calculated above In such cases, further matching is performed in the equilibrium point database.

[0069] During the search process, nearest neighbor search or multidimensional interpolation methods are preferred to match the steady-state turning radius. Closest to the desired turning radius The target drift equilibrium point. By locking the front wheel steering angle corresponding to this equilibrium point, all other target drift equilibrium points under this steady state can be simultaneously indexed. .

[0070] 4. Safety radius and speed constraint mechanism

[0071] To ensure safety, the following should be checked simultaneously when searching for the target's drift equilibrium point: i. ； ii. The vehicle speed shall not exceed the safe upper limit for the current adhesion conditions. If desired turning radius If it exceeds the feasible range, then it can be The limit is set to the nearest feasible value, and the driver is alerted to exceed the limit via the human-machine interface (HMI) or vehicle dynamics.

[0072] 5. Determine the target's front wheel steering angle and target status.

[0073] After successfully matching and locking the compliant target drift equilibrium point Then, the system defines the target state as:

[0074] Configure the target front wheel steering angle as follows:

[0075] The control system performs closed-loop control around these target states, stabilizing the actual motion state of the vehicle and tracking the corresponding drift equilibrium point, thereby completing intuitive continuous drift control throughout the entire process.

[0076] V. Feedforward-Feedback Closed-Loop Control and Dynamic Force Distribution Based on State Error

[0077] 1. Error definition and design of desired yaw rate

[0078] Define the centroid sideslip angle error relative to the target equilibrium point. for:

[0079] in, This is the current actual sideslip angle of the centroid. The target centroid sideslip angle is determined by the target drift equilibrium point.

[0080] Desired yaw rate is dynamically designed based on first-order error. :

[0081] in, The gain is set to the preset sideslip angle control value. For the desired yaw rate, Let be the target yaw rate determined by the target drift equilibrium point. This indicates that when the sideslip angle is too large, relatively Decreasing helps to reduce drift; when This indicates that the sideslip angle is too small. Increasing the size helps to deepen the drift.

[0082] 2. Yaw rate error and control law

[0083] Define yaw rate error for:

[0084] in, This represents the current actual yaw rate. Let be the desired yaw rate.

[0085] The closed-loop control law is designed as follows:

[0086] in Gain is used to control yaw rate.

[0087] According to the vehicle yaw dynamics equations:

[0088] Will Substituting and rearranging the data, we can obtain the information about the lateral force of the front wheel. Lateral force of the rear wheel The governing equations:

[0089] in: ， .

[0090] 3. Front and rear axle force distribution mode

[0091] To uniquely solve and determine the front wheel lateral force Lateral force of the rear wheel This invention employs a two-state operating mode for tire force distribution: Mode 1 (Front wheel not saturated): i. The longitudinal force of the rear wheel is determined by the speed controller. To maintain ; ii. Calculate the lateral force on the rear axis based on the attachment circle constraint:

[0092] iii. Will Substituting into the governing equations, a unique solution can be found. ; Mode 2 (Front wheel saturation): i. When the lateral force required by the front wheel reaches the front wheel's adhesion limit, that is... ; ii. Will Once the limit value is fixed, it can be solved from the governing equation. Then, the corresponding constraint is derived from the attachment circle constraint. .

[0093]

[0094] 4. Actuator instruction decoding

[0095] The target front axle lateral force obtained based on the above dynamic force distribution solution and target rear longitudinal force Perform the following solution: Solving for the required slip angle of the front wheels using the inverse Fiala model Combined with the current vehicle's center of gravity sideslip angle yaw rate longitudinal speed of vehicles Solve for the target front wheel steering angle command of the steer-by-wire actuator. :

[0096] Based on the target rear axle longitudinal force and effective tire radius Solve for the target rear-wheel drive / braking torque command:

[0097] Finally, the control system outputs... As control commands for the chassis actuators.

[0098] like Figure 4 As shown, in a preferred embodiment of the present invention, the human-machine co-driving vehicle drift assistance control system includes a signal acquisition module, a balance point database and state determination module, a control calculation module, and a command output module.

[0099] 1. Signal Acquisition Module

[0100] The signal acquisition module is configured to collect signals including, but not limited to, drift mode button / pedal signals, steering wheel angle signals, and vehicle status signals (vehicle longitudinal speed). v x centroid side slip angle β yaw rate r (etc.), and other optional signals, such as lateral acceleration, road adhesion estimation results, etc.

[0101] 2. Equilibrium Point Database and State Determination Module

[0102] The vehicle drift assist control system for human-machine co-driving has a drift balance point database stored or embedded internally, which can be implemented in the form of a lookup table or a fitting function.

[0103] The balance point database and state determination module are configured to calculate the desired turning radius based on the steering wheel angle. R des Based on the current vehicle speed and the calculated expected turning radius R des The target drift equilibrium point is obtained by searching or interpolating in the equilibrium point database; the corresponding target state is output, which includes, but is not limited to, the longitudinal velocity of the target vehicle. v x, ref Target centroid sideslip angle β ref Target yaw rate r ref And output the corresponding target front wheel steering angle. δ ref and the longitudinal force of the target rear wheel F xr, ref .

[0104] In addition, this module is also configured to perform safety boundary verification logic: while searching for a feasible drift equilibrium point, the desired turning radius is checked in real time. Whether the physical constraint of the minimum safe turning radius is met, and whether the current vehicle speed exceeds the safe upper limit speed under the current adhesion conditions; if it exceeds the safe feasible range, then the expected turning radius will be determined. Limit the limit to the nearest feasible value and issue over-limit feedback to the driver through the human-machine interface (HMI) or vehicle dynamic behavior.

[0105] 3. Control Calculation Module

[0106] The control calculation module is configured to implement a feedforward-feedback composite control algorithm.

[0107] Specifically, the control calculation module is configured to calculate the state error between the target state output by the equilibrium point database and the actual vehicle state, and to execute a closed-loop control law to generate target values ​​for the front axle lateral force and the rear axle longitudinal force. It has a built-in two-state tire force distribution mechanism that adopts different force distribution modes depending on whether the front wheels are saturated or not, automatically and dynamically switching between mode one (front wheels not saturated) and mode two (front wheels saturated) to achieve a reasonable distribution of tire forces between the front and rear axles. This ensures stable control of the vehicle's center of gravity sideslip angle and yaw rate within the tire adhesion limits.

[0108] 4. Command Output Module

[0109] The command output module is configured to perform inverse dynamic calculations based on the tire force commands output by the control calculation module and output control commands that can be recognized by the actuator.

[0110] Specifically, the command output module is configured to convert the front axle lateral force into a target front wheel steering angle command. δ cmd Convert the longitudinal force on the rear axle into motor / brake torque commands. T r, cmd Commands are sent to the vehicle's steering actuators and drive / braking system via an in-vehicle network (e.g., including but not limited to CAN bus or in-vehicle Ethernet).

[0111] like Figure 5 As shown, in a preferred embodiment of the present invention, the human-machine co-driving vehicle drift assist control method is configured to run in the vehicle's chassis domain controller, vehicle controller, or intelligent driving computing platform, and achieves steady-state drift assist control under extreme conditions by controlling the steer-by-wire chassis actuator. For example, the human-machine co-driving vehicle drift assist control method in this embodiment is applied to a rear-wheel drive vehicle equipped with a steer-by-wire and steer-by-wire drive / braking system.

[0112] The specific steps of the human-machine co-driving vehicle drift assist control method include: Step S101: Drift Intent Recognition and Signal Acquisition When the vehicle is driving in normal driving conditions, the control system continuously monitors various operation signals from the driver. When the driver presses the "Drift Mode" button on the steering wheel or depresses the drift mode pedal, the system sets the drift mode indicator to "1", indicating that the driver wants the vehicle to enter assisted drift mode. At this time, the system officially activates drift assist control.

[0113] This step encompasses the logical switch from normal driving to drift mode, as well as real-time data acquisition during the drift process, specifically including the following sub-processes: Step S1011: Drift Intent Recognition and Pattern Activation When the vehicle is in normal driving mode, the control system continuously monitors various interactive operation signals from the driver in the background. When a trigger signal indicating a drift request is received, the system sets the internal drift mode status flag to active (e.g., the flag is set to 1) to indicate that the vehicle has officially entered assisted drift mode.

[0114] The sources of the trigger signal include, but are not limited to, the driver pressing the physical "drift mode" button located on the steering wheel or center console, stepping on the dedicated drift operation pedal, or triggering the virtual drift activation button on the center console display.

[0115] Step S1012: Obtain drift intensity command

[0116] During the activation and maintenance of the drift assist mode, the system acquires control commands representing the driver's desired drift intensity in real time.

[0117] Specifically, the steering wheel angle sensor in the vehicle chassis collects the current steering wheel angle signal at high frequency. .

[0118] Step S1013: Obtaining the actual vehicle state parameters

[0119] While collecting control commands, the system simultaneously acquires the vehicle's current actual motion state as the feedback basis for subsequent closed-loop servo control.

[0120] Specifically, the control system acquires and calculates the vehicle's current longitudinal speed in real time by reading data from sensors such as the onboard inertial measurement unit (IMU), wheel speed sensors, and global positioning system (GPS). Actual centroid sideslip angle and actual yaw rate .

[0121] Step S102: Obtaining the desired turning radius and determining the target drift balance point

[0122] After the drift assist mode is activated, the control system translates the driver's steering wheel input into a physically achievable limit drift attitude reference target. This step encompasses the calculation of the desired turning radius, handling of accidental touch dead zones, verification of physical safety boundaries, and online optimization matching using a multi-dimensional database, specifically including the following sub-processes: Step S1021: Dead Zone Determination and Calculation of Expected Turning Radius The control system obtains the actual steering wheel angle signal. Next, the dead zone of the drift intention is determined. If the absolute value of the steering wheel angle... If the dead zone is less than the preset dead zone threshold, the control system determines that the driver has no obvious intention to drift, and at this point, the desired turning radius is adjusted. Set to a preset maximum value to keep the vehicle approximately in a straight line, preventing minor disturbances from triggering accidental drifting or causing instability; if the absolute value of the steering wheel angle... If the speed is greater than or equal to the dead zone threshold, the control system will adjust the speed according to the current actual vehicle speed. Determine the achievable turning radius envelope.

[0123] Subsequently, the control system adjusts the steering wheel angle input by the driver. Calculate the desired turning radius At the same time, the turning direction (left or right) of the vehicle is determined by the sign of the steering wheel angle:

[0124] in, The maximum turning radius, Minimum turning radius, The maximum steering wheel angle is preset; the turning direction of the vehicle, whether left or right, is determined based on the sign of the steering wheel angle.

[0125] Step S1022: Safety Limit Verification

[0126] After determining the initial desired turning radius, the control system compares it with the vehicle's physical limit radius under current speed and road surface adhesion conditions, performing real-time boundary verification. The control system checks the desired turning radius in real time. Whether the physical constraint of the minimum safe turning radius is met, and whether the current actual vehicle speed does not exceed the safe upper limit speed under the current adhesion conditions. If the desired turning radius... If the radius exceeds the safe and feasible range, the control system will limit it, forcibly restricting it to the nearest safe and feasible value, thereby obtaining the final desired turning radius. It will also issue over-limit feedback prompts to the driver through the human-machine interface (HMI) or vehicle dynamic behavior.

[0127] Step S1023: Online matching of target drift balance point

[0128] The control system is based on the current actual longitudinal vehicle speed. and the final determined expected turning radius An online search and matching process is performed in a pre-defined database of drift equilibrium points.

[0129] The drift balance point database is pre-built through offline calculation. The specific construction steps include: discretizing multiple vehicle speeds within the envelope of a given vehicle speed and front wheel steering angle parameters. and front wheel cornering For each pair Using the vehicle dynamics model and the Fiala tire model, the vehicle state derivatives are all zero (i.e., For the nonlinear equation system at time ), a feasible solution set is obtained; and for each equilibrium point in the solution set, according to Calculate and record the corresponding steady-state turning radius. .

[0130] During online matching, the controller uses nearest neighbor search or multidimensional interpolation algorithms to match the current vehicle speed. The steady-state turning radius was retrieved from the corresponding database slice. Closest to the desired turning radius The target front wheel steering angle. During this process, the system automatically avoids and filters out steady-state equilibrium points corresponding to normal driving steering (i.e., solutions marked with "o" in the image), while precisely filtering and locking onto unsteady-state equilibrium points corresponding to extreme sideslip states (i.e., solutions marked with "o" in the image). The drift working point is marked, thus uniquely determining the target drift equilibrium point.

[0131] Step S1024: Reference Target State Extraction and Output

[0132] After successfully matching and locking onto a compliant target drift equilibrium point, the final target drift equilibrium point state selected by the control system is recorded as follows: .

[0133] The extracted physical quantities will serve as the core control targets of the control calculation module, so that the subsequent control system can perform compound closed-loop control around these target states, so that the actual motion state of the vehicle can be stably servoed and track the corresponding unsteady drift equilibrium point.

[0134] Step S103: Calculation of control force and attitude control based on feedforward-feedback composite controller

[0135] This step employs a feedforward-feedback composite controller, combining the vehicle's nonlinear dynamics model with tire adhesion circle constraints, to perform closed-loop servo control on the vehicle's actual state, enabling it to stably track the target drift equilibrium point. Specifically, it includes the following sub-processes: Step S1031: Target state extraction and state error calculation The control system uses the target drift equilibrium point locked in step S102 as a reference value to set the corresponding target state, including the target centroid sideslip angle. Target yaw rate and the longitudinal speed of the target vehicle The control system displays the vehicle's current actual state (actual center of gravity sideslip angle) in real time. Actual yaw rate Actual vehicle longitudinal speed The state is compared with the target state, and the corresponding state error is defined and calculated, specifically including: centroid sideslip angle error. ; Yaw rate error Vehicle longitudinal speed error .

[0136] Step S1032: Dynamic design of desired yaw rate

[0137] To improve vehicle stability under extreme sideslip conditions, the control system introduces a dynamic compensation mechanism for sideslip angle error based on the target yaw rate, and designs the desired yaw rate. for:

[0138] in, The real-valued control gain is pre-calibrated through simulation or experimentation.

[0139] Step S1033: Construction of feedforward-feedback control law and core equation

[0140] The control system is based on the vehicle yaw dynamics equations

[0141] First-order decay expected dynamics of introducing yaw rate error

[0142] in, This is the yaw rate feedback gain.

[0143] By substituting and rearranging the equations, the feedforward-feedback core control equations are constructed:

[0144] in, For the reason and Coefficients determined by parameters, This refers to the feedforward term related to the equilibrium point.

[0145] At the same time, the control system incorporates vehicle lateral dynamics relationships:

[0146] Combining this with the aforementioned yaw core control equations, we can form a relationship regarding the front axle lateral force. and rear axle lateral force simultaneous equations .

[0147] Step S1034: Two-state tire force distribution based on adhesion circle constraints

[0148] Considering that the rear axle tires not only need to provide lateral force to maintain yaw attitude, but also need to undertake longitudinal speed control tasks, the control system relies on the lateral force of the front wheels. If the limit has been reached, execute the two-state tire force distribution mode: Mode 1 (Front wheel not saturated) When the front wheels of the vehicle are in an unsaturated adhesion state, the longitudinal force on the rear axle The longitudinal speed controller is prioritized to eliminate speed errors, and its calculation formula is as follows:

[0149] in This is the longitudinal speed control gain. Subsequently, the control system calculates the current lateral force limit that the rear axle can provide based on the rear axle tire adhesion circle constraint:

[0150] Finally, the control system will calculate the rear axle lateral force. Substituting the equations into the system of simultaneous equations constructed in step S1033, the target front axle lateral force can be uniquely calculated. .

[0151] Mode 2 (Front Wheel Saturation)

[0152] The lateral force required by the current wheel exceeds its physical adhesion limit (e.g.) When this occurs, the control system automatically switches to mode two.

[0153] At this point, the lateral force on the front axle is fixed at the lateral adhesion limit value of the front wheels:

[0154] Subsequently, the control system will fix it. Substituting into the simultaneous equations, the target rear axis lateral force required to maintain attitude can be calculated in reverse. Finally, the control system calculates the target rear axle longitudinal force that satisfies the combined adhesion constraint by using the rear axle tire adhesion circle constraint. :

[0155] Step S104: Executor instruction calculation and issuance

[0156] This step aims to translate the calculated control force into specific action commands for the underlying actuators, completing the closed-loop control cycle, and includes a robust safety exit mechanism. Specifically, it includes the following sub-processes: Step S1041: Actuator control instruction calculation The control system obtains the front axle lateral force based on step S103. and rear axle longitudinal force Inverse dynamics solution is then performed.

[0157] First, the control system uses the inverse Fiala model based on the front axle lateral force. Find the required front wheel slip angle Then, based on the current state of the vehicle, the required target front wheel steering angle command is obtained:

[0158] At the same time, the control system adjusts according to the longitudinal force of the rear axle. With tire effective radius Calculate the target rear-wheel drive / braking torque command:

[0159] Step S1042: Command Issuance and Closed-Loop Servo Control

[0160] The control system will calculate the target front wheel steering angle command. and rear-wheel drive / braking torque command The command is sent to the vehicle's steer-by-wire controller and rear-wheel drive motor controller (and braking system) for execution.

[0161] During the drift mode hold (e.g., while the driver continuously presses the drift button), the control system periodically executes steps S101 to S104 to achieve closed-loop servo control of the target drift equilibrium point. This cyclic execution process keeps the vehicle running stably near the target drift equilibrium point and allows the driver to continuously adjust the vehicle's drift attitude and turning radius by continuously changing the steering wheel angle.

[0162] Step S1043: Drift Mode Exit and Smooth Switching. During drift mode operation, the control system monitors the exit conditions in real time. When the system detects that the driver has released the drift button, or when the system detects a current safety risk (such as excessive vehicle speed, excessively small turning radius, extremely low road surface adhesion, etc.), the control system automatically exits the assisted drift mode and smoothly switches to the vehicle's normal stability control mode to ensure vehicle driving safety.

[0163] In a preferred embodiment of the present invention, a vehicle is also provided, which is equipped with the aforementioned human-machine co-driving drift assistance control system, the chassis physical architecture and actuators of which at least include: Front wheel steer-by-wire system: Communicates with the control system and is configured to receive target front wheel steering angle commands from the control system. It drives the front wheel actuator to complete the corresponding steering action.

[0164] Rear-wheel drive / brake actuator: Includes a rear axle drive motor and a brake-by-wire actuator, which is communicatively connected to the control system and configured to receive target drive / brake torque commands output by the control system. To perform the corresponding power drive or braking deceleration action.

[0165] Onboard sensor components: These include vehicle attitude sensors and wheel speed sensors, electrically or communicatively connected to the control system, configured to collect and feed back the vehicle's current actual motion state to the control system in real time. The vehicle attitude sensor is preferably an inertial measurement unit (IMU); the actual motion state quantities include at least the actual longitudinal vehicle speed. v x Actual centroid sideslip angle β and actual yaw rate r .

[0166] Control system: This refers to the human-machine co-driving drift assist control system described in the previous embodiment. It communicates with the front wheel steering system, the rear wheel drive / brake actuator and the vehicle sensor components through the vehicle network (such as CAN bus or vehicle Ethernet) to achieve bidirectional data communication and command issuance.

[0167] The specific multi-mode control switching and working logic of the vehicle are as follows: In normal driving mode, the control system controls the car to operate under conventional stability control logic. At this time, the actuators of the vehicle chassis are mainly used to suppress excessive sideslip and ensure tracking performance and safety during daily driving.

[0168] When the driver actively triggers a drift request signal through a triggering mechanism (such as a physical button on the steering wheel or center console, a dedicated pedal, or a virtual button on the center console display), the control system controls the car to switch into the aforementioned human-machine co-driving drift assistance control logic.

[0169] During the operation of the human-machine co-driving drift assist control logic, the control system executes the human-machine co-driving vehicle drift assist control method as described in the previous embodiments: First, the vehicle's current actual state is obtained in real time and at high frequency using on-board sensor components, and the steering wheel angle sensor obtains the steering wheel angle signal representing the desired drift intensity. Subsequently, by using the drift balance point database embedded in the control system, the steering wheel angle is monotonically mapped to the desired turning radius, and the unsteady drift balance point closest to the desired turning radius is retrieved and locked online to extract the target state. Then, the control force is calculated by the feedforward-feedback composite controller built into the control system, and based on the lateral force of the front wheels. Whether the limit has been reached, the tire force distribution will automatically and dynamically switch between mode one and mode two. Finally, the calculated front axle lateral force and rear axle longitudinal force are dynamically reverse-engineered into control commands that the actuators can recognize, and then sent to the front wheel steering system and the rear wheel drive / brake actuator respectively, thereby achieving continuous drift assist control that conforms to the driver's intuition that "the more you turn the steering wheel, the smaller the turning radius".

[0170] In addition, during drift control, if the control system detects that the driver releases the drift trigger mechanism, or if the data collected by the on-board sensor components triggers the preset safety risk threshold (such as excessive vehicle speed, excessively small turning radius, or extremely low road surface adhesion), the control system will automatically exit the assisted drift mode and smoothly switch back to the conventional stability control logic to ensure the dynamic driving safety of the entire vehicle.

[0171] like Figure 6 and Figure 7As shown, in a preferred embodiment of the present invention, auxiliary control of the vehicle is illustrated in the case of drifting in a U-shaped bend with varying attitude.

[0172] Figure 6 This is a schematic diagram of the vehicle's trajectory under a variable posture U-shaped bend drift condition in a preferred embodiment of the present invention; Figure 7 This is a schematic diagram illustrating the relationship between driver input and vehicle response under a U-shaped bend condition in a preferred embodiment of the present invention.

[0173] exist Figure 7 In the diagram, two vertical scale lines indicate the intervention and deactivation points of the drift controller, with the control effect description focusing on the period during which the controller is active. After the drift assist mode is activated, the driver communicates their drift intention to the control system by inputting the steering wheel angle shown in the attached diagram (SteerWheerInput). Based on a built-in mapping, the control system converts this steering wheel angle representing the drift intention into the desired front wheel steering angle command in the diagram (reversedDelta) and sends it to the actuators for execution.

[0174] As shown in Figures yawrate_rad and beta_rad, the system internally sets reference values ​​for the vehicle's yaw rate and sideslip angle as the core control targets of the control system. Curves with the suffix "Ref" are reference values, while curves without the suffix represent the actual state quantities of the vehicle. As can be seen from the figures and curve trends, after the drift controller is activated, the vehicle's actual yaw rate and actual sideslip angle are stably servo-controlled to positions close to their corresponding reference values, achieving high-precision target servo tracking control. This verifies the technical effectiveness of this invention in achieving intuitive human-machine interactive drift-assisted control under extreme attitude change conditions.

[0175] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for assisting drift control of a vehicle in a co-driving manner, characterized by, The method includes the following steps: Step S101: The control system acquires the driver's drift request signal to activate the drift assist mode, and during the activation of the drift assist mode, it collects the steering wheel angle signal and the vehicle's current actual motion state in real time. Step S102: The control system calculates the desired turning radius based on the steering wheel angle signal, performs real-time boundary verification on the desired turning radius to obtain the final desired turning radius, and then retrieves and locks the target drift balance point in the preset drift balance point database based on the current actual longitudinal vehicle speed, and extracts the corresponding reference target state. Step S103: The control system calculates the state error based on the actual motion state quantity and the reference target state, constructs the core control equation based on the composite control law, and performs two-state tire force distribution in combination with the rear axle tire adhesion circle constraint to solve for the target front axle lateral force and the target rear axle longitudinal force. Step S104: The control system performs inverse dynamic calculation based on the target front axle lateral force and the target rear axle longitudinal force, generates actuator control commands and sends them to the vehicle chassis actuators to perform closed-loop servo tracking of the target drift equilibrium point.

2. The vehicle drift assist control method for human-machine co-driving according to claim 1, wherein step S102, calculating the desired turning radius based on the steering wheel angle signal, includes: The control system obtains the actual steering wheel angle signal. Then, the dead zone of the drift intention is determined; if the absolute value of the steering wheel angle is... If the dead zone is less than the preset threshold, the control system determines that the driver has no obvious intention to drift and will adjust the desired turning radius. When the maximum value is set to a preset level, the drift controller does not intervene in the near-linear state; if the absolute value of the steering wheel angle is... If the dead zone threshold is greater than or equal to the specified dead zone threshold, the control system calculates the initial expected turning radius based on the steering wheel angle range, the turning radius range, and the actual steering wheel angle using the following formula: in, The maximum turning radius, Minimum turning radius, The maximum steering wheel angle is preset; the turning direction of the vehicle, whether left or right, is determined based on the sign of the steering wheel angle.

3. The vehicle drift assist control method for human-machine co-driving according to claim 1, wherein step S102, in which the control system performs real-time boundary verification of the desired turning radius, includes: The control system checks the desired turning radius in real time. Whether the physical constraint of minimum safe turning radius is met, and whether the current actual vehicle speed does not exceed the safe upper limit speed of the vehicle under the current road surface adhesion conditions; If the desired turning radius is beyond the feasible range, the control system limits it to the nearest safe feasible value to obtain the final desired turning radius and sends an out-of-limit feedback prompt to the driver through the human-machine interface or the dynamic behavior of the vehicle body.

4. The vehicle drift assist control method for human-machine co-driving as described in claim 1, wherein the preset drift balance point database is pre-constructed through offline calculation, and the construction steps include: Multiple vehicle speeds are discretized within the given envelope of vehicle speed and front wheel steering angle parameters. and front wheel angle; for each pair Using the vehicle dynamics model and the Fiala tire model, a feasible solution set is obtained by solving the nonlinear equations when all vehicle state derivatives are zero. For each equilibrium point in the solution set, the corresponding steady-state turning radius is calculated using the following formula. And record: The step S102 of retrieving and locking the target drift balance point from the preset drift balance point database includes: The control system employs nearest neighbor search or multidimensional interpolation algorithms to determine the current actual longitudinal vehicle speed. Retrieve steady-state turning radius from the corresponding database slice Closest to the desired turning radius The target front wheel steering angle is determined, and during the search process, the steady-state equilibrium point corresponding to the normal driving steering is automatically avoided and filtered out, while the non-steady-state equilibrium point corresponding to the extreme lateral deviation state is accurately selected and locked as the target drift equilibrium point.

5. The vehicle drift assist control method for human-machine co-driving according to claim 1, wherein the reference target state includes the target centroid sideslip angle. β ref Target yaw rate r ref and the longitudinal speed of the target vehicle v x, ref The actual motion state quantities include the actual center of gravity sideslip angle, the actual yaw rate, and the actual vehicle longitudinal velocity. The state error calculated in the step S103 includes: centroid side slip angle error , yaw rate error , vehicle longitudinal speed error ; Step S103 further includes dynamic design of the desired yaw rate: the control system introduces a dynamic compensation mechanism for sideslip angle error based on the target yaw rate, and calculates the desired yaw rate after dynamic error correction according to the following formula: wherein, is a preset side slip angle control gain.

6. The vehicle drift assistance control method for human-machine co-driving according to claim 5, wherein the construction of the core control equation in step S103 includes: The control system introduces the first-order decay expected dynamics of the yaw rate error: wherein, is the yaw angular velocity feedback gain; Combining the vehicle yaw dynamics equations: in, This is the distance from the rear axle to the center of mass. This is the distance from the front axle to the center of gravity. , These are the lateral forces of the front and rear wheels, respectively. The following feedforward-feedback core control equations are constructed: in, For the reason and Coefficients determined by parameters, This refers to the feedforward term related to the equilibrium point.

7. The vehicle drift assist control method for human-machine co-driving as described in claim 6, wherein the execution of two-state tire force distribution in step S103 specifically includes the control system dividing the system into mode one and mode two based on whether the required lateral force of the front wheels has reached the physical adhesion limit of the front wheels: When the control system determines that the vehicle's front wheels are in a state of insufficient adhesion, it operates in mode one: First, the target rear axle longitudinal force is calculated using a longitudinal speed controller with the goal of eliminating the longitudinal speed error. Secondly, based on the rear axle tire adhesion circle constraint, combined with the road adhesion coefficient, rear axle vertical load and the target rear axle longitudinal force, the rear axle lateral force is calculated, and then substituted into the simultaneous equations to solve for the target front axle lateral force. When the control system determines that the required lateral force for the front wheels has reached or exceeded the physical adhesion limit of the front wheels, it automatically switches to mode two: The target front axle lateral force is fixed to the value corresponding to the front wheel physical adhesion limit, and substituted into the simultaneous equations to calculate the target rear axle lateral force in reverse. Then, based on the rear axle tire adhesion circle constraint, combined with the road adhesion coefficient, the rear axle vertical load, and the target rear axle lateral force, the target rear axle longitudinal force that satisfies the combined adhesion limit is calculated in reverse.

8. The vehicle drift assist control method for human-machine co-driving according to claim 7, wherein step S104 specifically includes: The control system uses a reverse tire model to convert the target front axle lateral force into a front wheel slip angle, and calculates the target front wheel steering angle command in conjunction with vehicle kinematics. The control system calculates the target rear wheel torque command based on the target rear axle longitudinal force and the effective tire radius; The control system performs closed-loop tracking by repeatedly executing steps S101 to S104, and automatically exits the assisted drift mode when it detects that a drift request has been withdrawn or that there is a safety risk.

9. A vehicle drift assist control system for co-driving with a human, characterized by, The system includes: The signal acquisition module is configured to acquire the driver's drift request signal to activate the drift assist mode, and to collect the steering wheel angle signal and the vehicle's current actual motion state in real time during the drift assist mode. The working point determination module is configured to calculate the desired turning radius based on the steering wheel angle signal, perform real-time boundary verification on the desired turning radius to obtain the final desired turning radius, and then search and lock the target drift balance point in the preset drift balance point database based on the current actual longitudinal vehicle speed, and extract the corresponding reference target state. The attitude control module is configured to calculate the state error based on the actual motion state quantity and the reference target state, construct the core control equation based on the composite control law, and perform two-state tire force distribution in combination with the rear axle tire adhesion circle constraint to solve for the target front axle lateral force and the target rear axle longitudinal force. The instruction calculation module is configured to perform inverse dynamic calculation based on the target front axle lateral force and the target rear axle longitudinal force, generate actuator control commands and send them to the vehicle chassis actuators to perform closed-loop servo tracking of the target drift equilibrium point.

10. An automobile characterized by comprising: The vehicle includes a physical chassis actuator, on-board sensor components, and a control system, wherein: The on-board sensor assembly is communicatively connected to the control system and is configured to collect the current actual motion state of the vehicle in real time and feed it back to the control system. The physical chassis actuator includes at least a front wheel steer-by-wire system and a rear wheel drive / brake actuator, which are respectively communicatively connected to the control system. The control system is equipped with the vehicle drift assist control system for human-machine co-driving as described in claim 9, or the control system is configured to execute the vehicle drift assist control method for human-machine co-driving as described in any one of claims 1 to 8; In normal driving mode, the control system controls the car to operate under normal stability control logic. When it receives a drift request signal actively triggered by the driver, it switches to the human-machine co-driving drift assistance control logic and sends the calculated actuator control commands to the front wheel steering system and the rear wheel drive / brake actuator for execution.

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