A man-machine cooperative steering control method considering rain and fog weather driving characteristics

Abstract

The present application belongs to the technical field of intelligent driving control, and relates to a man-machine cooperative steering control method considering rain and fog weather characteristics, a driver model and a man-machine co-driving vehicle model considering the changes of environmental visibility and road adhesion coefficient are established, the long-distance preview point and steering wheel torque of the driver are dynamically adjusted by the driver model through the preview weight coefficient determined by the fuzzy control method; the man-machine cooperative steering control strategy is designed by the model predictive control method through the man-machine co-driving vehicle model, which is used to dynamically adjust the steering control amount according to the vehicle state and environmental conditions; the driving permission allocation strategy adopts the fuzzy control method to determine the man-machine cooperation coefficient under the rain and fog weather conditions, which is used to dynamically adjust the man-machine cooperation degree; according to the man-machine cooperative steering control strategy and the driving permission allocation strategy, the man-machine cooperative driving is executed; the present application improves the stability and safety of the vehicle under rain and fog weather.

Description

Technical Field

[0001] This invention belongs to the field of intelligent driving control technology, specifically relating to a human-machine collaborative steering control method that takes into account driving characteristics in rainy and foggy weather. Background Technology

[0002] Existing driver assistance and autonomous driving systems are primarily designed for ordinary driving scenarios, failing to adequately consider complex driving conditions such as reduced visibility and decreased road traction in rainy or foggy weather. These special conditions can lead to a mismatch between driver actions and the vehicle's automatic control system, increasing driving risks. Improving the human-vehicle-road model and dynamically adjusting the control strategy is an effective way to address this problem. Therefore, there is an urgent need for a steering control method that can integrate the characteristics of rainy and foggy weather and optimize human-machine collaborative control. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a human-machine cooperative steering control method that takes into account the driving characteristics in rainy and foggy weather.

[0004] To achieve the objectives of this invention, the following technical solutions are adopted.

[0005] A human-machine cooperative steering control method that takes into account driving characteristics in rainy and foggy weather includes the following steps:

[0006] S1. Based on the ground coordinate system and the vehicle body coordinate system, establish a driver model and a human-machine co-driving vehicle model for rain and fog environments; where:

[0007] The driver model, taking into account changes in environmental visibility and road surface adhesion coefficient, dynamically adjusts the distance of long-distance aiming points and steering wheel torque through aiming weight coefficients determined by fuzzy control methods to improve the driver's ability to perceive the path. It is used to describe the driver's visual behavior and steering control behavior when driving a vehicle in rainy or foggy conditions and performing path tracking.

[0008] The human-machine co-driving vehicle model uses a two-degree-of-freedom vehicle dynamics model to describe the vehicle's lateral and yaw motions, and employs a magic tire model to simulate tire force changes in rain and fog environments.

[0009] S2. Using the human-machine co-driving vehicle model established in step S1, design a human-machine cooperative steering control strategy through model predictive control method, which is used to dynamically adjust the steering control quantity according to the vehicle status and environmental conditions.

[0010] S3. Design a driving permission allocation strategy based on fuzzy control method; the driving permission allocation strategy uses fuzzy control method to determine the human-machine collaboration coefficient under rain and fog weather conditions, so as to dynamically adjust the degree of human-machine collaboration and reasonably allocate the driving permissions of human and machine.

[0011] S4. Based on the human-machine cooperative steering control strategy and driving authority allocation strategy, and combined with the human-machine control quantity, execute human-machine cooperative driving.

[0012] As a preferred embodiment of the present invention, the ground coordinate system is constructed with the origin O fixed at the current position of the vehicle's center of mass O, the X-axis pointing directly in front of the vehicle body at the current moment, and the positive direction of the Y-axis being the direction in which the X-axis is rotated 90 degrees counterclockwise.

[0013] As a preferred embodiment of the present invention, the vehicle coordinate system is constructed with the origin coinciding with the vehicle's center of mass O, the X-axis pointing directly forward of the vehicle, the Y-axis being rotated 90 degrees counterclockwise as its positive direction, and the Z-axis pointing directly upward of the vehicle and perpendicular to the X-axis and Y-axis.

[0014] As a preferred embodiment of the present invention, the long-distance pre-aiming point distance L eff Expressed as:

[0015] L eff =κL f +(1-κ)L n ,

[0016] In the formula, L n L is the distance to the near target point N, i.e., the distance from the vehicle's center of gravity O to the near target point N; f The distance for the far-field aiming is F, which is the distance from the vehicle's center of gravity O to the fixed far point F; L eff The distance to the dynamically adjusted long-range preview point is the distance from the vehicle's center of gravity O to the dynamic long-range preview point F. eff The distance; κ is the aiming weight coefficient designed based on the fuzzy control method, taking into account environmental visibility and road surface adhesion coefficient.

[0017] As a preferred embodiment of the present invention, the fuzzy control rules for the pre-aiming weight coefficient κ are shown in the following table:

[0018]

[0019] As a preferred embodiment of the present invention, the fuzzy control rules are formulated based on the following: when the environmental visibility V is high and the road surface adhesion coefficient μ is high, the far-point pre-aiming weight is considered to be large, and the weight coefficient is large; when the environmental visibility V is small and the road surface adhesion coefficient μ is small, the near-point pre-aiming weight is considered to be large, and the weight coefficient is small.

[0020] As a preferred embodiment of the present invention, the process of establishing the human-machine co-driving vehicle model includes the following steps:

[0021] S71. Establish the vehicle dynamics model:

[0022] Based on the torque and moment balance equations, the lateral velocity v of the vehicle is obtained.y The expression for the vehicle's yaw rate r:

[0023]

[0024] Where m is the mass of the vehicle, v y Let v be the lateral velocity of the vehicle in the vehicle coordinate system. x Let F be the vehicle's longitudinal velocity in the vehicle coordinate system, r be the vehicle's yaw rate, and F be the vehicle's longitudinal velocity. yf F is the lateral force on the front wheels of the vehicle. yr I is the lateral force on the rear wheel of the vehicle. z Let be the moment of inertia of the vehicle about the z-axis, and a and b be the distances from the front and rear axles to the center of mass, respectively.

[0025] The normal load of a tire is expressed as:

[0026]

[0027] The approximate slip angles of the front and rear wheels of a vehicle are expressed as follows:

[0028]

[0029] Linear tire models cannot accurately reflect the actual changing trend of tire forces in rain and fog conditions. This method ignores the influence of longitudinal tire forces and uses the magic tire formula under pure lateral slip conditions to calculate the lateral tire forces:

[0030]

[0031] Where μ is the road surface adhesion coefficient, F zf and F zr These are the tire normal loads, B f C f and D f B is the Magic Formula empirical parameter for the front wheels of the vehicle. r C r and D r The Magic Formula empirical parameters for the rear wheels of the vehicle;

[0032] Although the magic formula can accurately represent the nonlinear characteristics of vehicle tires in rain and fog conditions, its complex form makes it computationally intensive and difficult to solve when substituted into the vehicle dynamics equations and integrated into the vehicle model. Therefore, this method performs continuous local linearization on the tire model at each sampling time to obtain a linearized tire lateral force equation:

[0033]

[0034] in, This indicates the front wheel slip angle of the vehicle at the current sampling time. This indicates the rear wheel slip angle of the vehicle at the current sampling time; This represents the nominal lateral stiffness of the front wheel at the current sampling moment. This represents the nominal lateral stiffness of the rear wheel at the current sampling moment; This represents the residual lateral force of the front wheel at the current sampling moment. This represents the residual lateral force of the rear wheel at the current sampling moment;

[0035] At each sampling moment, after updating the vehicle state information, the vertical load F of the vehicle's front wheels is calculated using the above formula. zf and rear wheel vertical load F zr And the front wheel slip angle and the rear wheel slip angle; substituting them into the formula, the front wheel slip force at the current sampling time can be calculated. and rear wheel lateral force

[0036] nominal lateral stiffness of the front wheel at the current sampling time Rear wheel nominal lateral stiffness Residual lateral force of the front wheel and the residual lateral force of the rear wheels Calculated using the following formula

[0037]

[0038] The resulting vehicle dynamic equations at each sampling time are:

[0039]

[0040] In the formula, m is the mass of the vehicle, and v x Let F be the vehicle's longitudinal velocity in the vehicle coordinate system, r be the vehicle's yaw rate, and F be the vehicle's longitudinal velocity. yf F is the lateral force on the front wheels of the vehicle. yr I is the lateral force on the rear wheel of the vehicle. z Let be the moment of inertia of the vehicle about the z-axis, and a and b be the distances from the front and rear axles to the center of mass, respectively. The nominal lateral stiffness of the front wheel at the current sampling moment. The nominal lateral stiffness of the rear wheel at the current sampling moment. The residual lateral force of the front wheel, The residual lateral force of the rear wheel, δ f This refers to the steering angle of the vehicle's front wheels.

[0041] S72. Establish the vehicle kinematics model:

[0042] Assuming angular deviation ψ eIn the case of smaller displacements, the deviations in lateral displacement and angle can be expressed by the following formulas:

[0043]

[0044] In the formula, the vertical distance between the near aiming point N and the road in the direction of vehicle movement is defined as the lateral displacement deviation y. e =yy ref Where y is the lateral displacement, y ref This is a reference value along the road centerline; v x L is the vehicle's longitudinal velocity in the vehicle coordinate system; β is the vehicle's sideslip angle; r is the vehicle's yaw rate; L n The distance to the near target point N is the distance from the vehicle's center of gravity O to the near target point N; the reference curvature ρ ref =1 / R ref It is the curvature of the inner lane, where R ref It is the radius of curvature; angular deviation ψ e It is the angle between the vehicle's forward direction and the tangent to the road centerline, with the angle deviation ψ. e =ψ ref -ψ, where ψ is the vehicle's yaw angle. ref It is the reference for ψ along the tangent direction of the road centerline.

[0045] S73. Establish a vehicle steering system model.

[0046] Vehicle steering wheel angle δ s and the vehicle's front wheel steering angle δ f The relationship is as follows:

[0047] δ s =g s ·δ f ,

[0048] Where, δ s The steering wheel angle of a vehicle; g s This refers to the transmission ratio coefficient of the vehicle steering system;

[0049] Based on the principles of vehicle dynamics, the expression for the wheel return torque of the steering system is as follows:

[0050]

[0051] Among them, K s The aligning moment coefficient and the sideslip angle α f The expression for the linear region model is as follows:

[0052] K s =-K p C f η t ,

[0053]

[0054] Where, η t It is the sum of tire drag distance and tilt moment arm, K p It is the steering system coefficient;

[0055] Externally applied total torque T tot The expression is as follows:

[0056] T tot =λT auto +(1-λ)T dr ,

[0057] Among them, T auto T is the torque output by the automated system. dr λ represents the torque output by the driver model, and λ is the coordination coefficient.

[0058] The torque balance equation for the vehicle steering system is as follows:

[0059]

[0060] Among them, b s and J s These are the coefficient of friction and moment of inertia of the steering column, ω. s This represents the angular velocity of the steering wheel.

[0061] S74. Establish a human-machine co-driving vehicle model.

[0062] Select the vehicle's steering wheel angular velocity ω s Steering wheel angle δ s , center of mass sideslip angle β, yaw rate r, lateral displacement deviation y e and yaw angle deviation ψ e As a system state, the total steering wheel torque T of the vehicle tot As a system input, the vehicle's lateral displacement deviation y e and yaw angle deviation ψ e As system output;

[0063] Summarizing the above vehicle model, the vehicle model under rain and fog conditions can be written in state-space form, as follows:

[0064]

[0065] Where w = ρ ref As external input, the state vector and output vector are x = [ω] s ,δ s ,β,r,y e ,ψ e ] and y = [y e,ψ e and control input u=T tot The system matrix is:

[0066]

[0067] D d =[0000-v x L n v x ] Τ ,

[0068]

[0069] To facilitate controller design, the above state-space model is discretized using Euler discretization to obtain the discretized vehicle model:

[0070]

[0071] in, C d =C0,T s Sampling time.

[0072] As a preferred embodiment of the present invention, the design process of the human-machine cooperative steering control strategy includes the following steps:

[0073] S81, Design of Human-Machine Torque Cooperative Automation Controller

[0074] Define the control quantity sequence U(k) as:

[0075]

[0076] Assuming the prediction time domain has P steps and the control time domain has N steps, with N ≤ P, and also assuming the control variables outside the control time domain remain constant, i.e., u(k+N)=u(k+N+1)=…=u(k+P-1), the prediction equations for the next P steps are derived as follows:

[0077]

[0078] Where x(k+i) is the system state variable at time k+i, i = 0, 1, ..., P; u(k+i) is the optimization variable at time k+i, i = 0, 1, ..., P-1; w(k+i) is the road curvature at time k+i, i = 0, 1, ..., P-1k+i;

[0079] The output prediction equation for the time domain in P steps is as follows:

[0080]

[0081] Where y(k+i) is the system output at time k+i, i = 0, 1, ..., P;

[0082] S82. The trajectory tracking problem is described as an optimization problem with the following constraints:

[0083]

[0084] Where R(K+1)=[r(k+1),r(k+2),…,r(k+p)] Τ 2p×1 The reference vector is ΔU(k); the control increment vector is ΔU(k) ​​= [Δu(k), Δu(k+1), ..., Δu(k+m-1)]. Τ m×1 The independent variable is the time-domain output of the constrained optimization problem; the output of the time-domain P is Y(k+1|k)=[y(k+1)|k,y(k+2)|k,...,y(k+p)|k]. Τ 2p×1 It is predicted by the system model at time k; based on the actuator saturation of the steering system, a control input constraint u is introduced. max (k)=-u min (k); The state constraint Hx(k)≤G is defined by the stable control envelope to ensure vehicle stability, which is related to the limits on vehicle roll angle and yaw rate; these limits show the maximum capacity of a given tire and are based on steady-state assumptions; the vehicle yaw rate limit is:

[0085]

[0086] Where g is the acceleration due to gravity, and μ is the road adhesion coefficient. The constraint condition for the vehicle's center of gravity sideslip angle is:

[0087]

[0088] in, It is the slip angle related to the maximum lateral force, and the state constraint matrices H and G are respectively:

[0089]

[0090] Solving the above constrained optimization problem yields the optimal solution u(k) at time k.

[0091] As a preferred embodiment of the present invention, the fuzzy control rules for the human-machine collaboration coefficient are shown in the following table:

[0092]

[0093] As a preferred embodiment of the present invention, the fuzzy control rule is formulated based on: when the lateral displacement deviation |y e |Large and angular deviation|ψ eWhen the deviation is large, it is assumed that the driver is significantly affected by the rain and fog environment, resulting in a high coordination coefficient or complete control of the vehicle's lateral displacement by the auxiliary system; when the lateral displacement deviation is large... e |Smaller and angular deviation|ψ e When the coefficient is relatively small, it is assumed that the driver has a good ability to adapt to rain and fog conditions, so the coordination coefficient should be small or low.

[0094] Beneficial effects

[0095] 1. This invention establishes a two-point pre-aiming model by considering visibility and road surface adhesion coefficient in rainy and foggy environments, and dynamically adjusts the far-point pre-aiming distance by combining a dynamic pre-aiming weight coefficient adjustment method based on fuzzy control. This enables vehicles to better cope with complex weather conditions and continuously optimize their driving trajectory in changing environments, which can effectively improve the system stability and driving safety of vehicles under low visibility conditions.

[0096] 2. Through effective human-machine collaborative control, this invention reduces the driver's pressure to track the route and improves the driver's sense of security and driving comfort in weather conditions such as rain and fog with low visibility and low road surface adhesion coefficient.

[0097] 3. This invention optimizes the control strategy through model predictive control and combines it with fuzzy logic for driving permission allocation, ensuring effective collaboration between the automated system and the driver to achieve real-time rolling optimization control. This control method can effectively avoid unexpected situations and enable the system to respond more efficiently to different driving needs. Attached Figure Description

[0098] Figure 1 This is a simplified flowchart of a human-machine cooperative steering control method that takes into account the characteristics of rainy and foggy weather, as described in this invention.

[0099] Figure 2 This is a control framework diagram of a human-machine cooperative steering control method that takes into account the characteristics of rainy and foggy weather, as described in this invention.

[0100] Figure 3 This is a schematic diagram of the two-point pre-aiming driver model in this invention;

[0101] Figure 4 This is a schematic diagram of the path tracking model in this invention;

[0102] Figure 5 This is a schematic diagram of the environmental visibility membership function in this method;

[0103] Figure 6 This is a schematic diagram of the membership function of the road surface adhesion coefficient in this method;

[0104] Figure 7 This is a schematic diagram of the membership function of the weight coefficients in this method;

[0105] Figure 8 This is a schematic diagram of the fuzzy rule surface for the weight coefficients in this method;

[0106] Figure 9 This is a schematic diagram of the membership function of the lateral displacement deviation in this method;

[0107] Figure 10 This is a schematic diagram of the membership function for the yaw angle deviation in this method;

[0108] Figure 11 This is a schematic diagram of the membership function of the synergy coefficient in this method;

[0109] Figure 12 This is a schematic diagram of the fuzzy rule surface for the synergistic coefficient in this method. Detailed Implementation

[0110] The present invention will be further described in conjunction with the embodiments and accompanying drawings.

[0111] As an embodiment of the present invention, such as Figure 1 As shown, a human-machine cooperative steering control method considering driving characteristics in rainy and foggy weather includes the following steps:

[0112] Step 1: Establish driver and vehicle models for rain and fog conditions.

[0113] Establish a ground coordinate system with the origin O fixed at the current position of the vehicle's center of mass O. The X-axis points directly in front of the vehicle at the current moment, and the positive direction of the Y-axis is the direction of the X-axis rotated 90 degrees counterclockwise.

[0114] Establish a vehicle coordinate system with its origin coinciding with the vehicle's center of gravity O. The X-axis points directly forward of the vehicle and rotates counterclockwise.

[0115] Rotate 90 degrees to get the positive direction of the Y-axis, and the Z-axis points directly above the vehicle body and is perpendicular to the X-axis and Y-axis;

[0116] (1) Establish driver model

[0117] The driver preview model describes the driver's visual and steering control behaviors during path tracking. By dynamically adjusting the weights, the influence of environmental variables on driving behavior is incorporated into the model, resulting in a more robust driver model. This paper presents a driver preview model with adjustable preview weight coefficients. By optimizing the preview weight coefficients through a fuzzy controller, real-time adjustments to preview behavior are achieved, thereby improving the driver's path perception and enhancing driving safety and adaptability.

[0118] like Figure 4As shown, the two-point pre-aiming driver model combines road information from two areas of the road, one near and one far. The far point F serves as the prediction point, reflecting the approximate direction to be reached, while the near point N serves as the compensation point, gradually adjusting the driver to the desired trajectory. This method dynamically adjusts the far-point pre-aiming distance L based on environmental visibility and road surface adhesion coefficient. eff The distance L of the long-range pre-aiming point is dynamically adjusted. eff This improves vehicle driving stability, comfort, responsiveness, and safety in rain and fog conditions, enhances system adaptability, and ensures the system can make optimized decisions based on real-time environmental conditions. The following formula represents the distance L from the long-range preview point. eff The expression:

[0119] L eff =κL f +(1-κ)L n

[0120] Among them, L n L is the distance to the near target point N, i.e., the distance from the vehicle's center of gravity O to the near target point N; f The distance for the far-field aiming is F, which is the distance from the vehicle's center of gravity O to the fixed far point F; L eff The distance to the dynamically adjusted long-range preview point is the distance from the vehicle's center of gravity O to the dynamic long-range preview point F. eff The distance; κ is the aiming weight coefficient designed based on the fuzzy control method, taking into account environmental visibility and road surface adhesion coefficient.

[0121] Long-range aiming angle θ f From the vehicle's center of gravity O to the dynamic far point F eff The angle between the direction of the vehicle and the front of the vehicle; the near aiming angle θ n The angle between the direction from the vehicle's center of gravity O to its nearest point N and the front of the vehicle; the near angle θ that the driver anticipates in the driving direction. n and the far angle θ f Based on geometric relationships and kinematic principles, it can be approximated as:

[0122]

[0123] Among them, the lateral displacement deviation y e R is the perpendicular distance from the nearest point N to the direction of vehicle travel. ref Let ψ be the radius of curvature of the trajectory of the vehicle's center of mass O, and ψ be the yaw angle deviation. e It is the angle between the direction of the vehicle's movement and the tangent to the centerline of the road.

[0124] Feedforward control and compensation control can be represented as follows:

[0125]

[0126] Among them, K aFor long-range aiming angle θ f The proportional gain reflects the driver's response to the long-range aiming angle θ. f The degree of perception; among which, K c For the near aiming angle θ n The proportional gain reflects the driver's response to the near-aiming angle θ. n The degree of perception; T L T represents the lead time constant of the driver model; I is the time lag constant of the driver model.

[0127] Driver response lag and muscle response lag can be represented as follows:

[0128]

[0129] Where, τ p T is the time constant of the delay element. N The time constant is the dynamic model of the driver's arm.

[0130] The driver's response to the feedback torque on the steering wheel and the compensation for the resistance torque of the steering column can be expressed as follows:

[0131]

[0132] Among them, K d For the proportional gain of the sensing element, K G T is the proportional gain of the motion stage, T1 is the time constant of the sensing stage, and T... K1 T is the lead time constant of the action phase. K2 This is the time constant of the delay in the action.

[0133] like Figure 3 As shown, the driver model, through the coordinated action of multiple control modules, ultimately outputs the steering wheel torque T. dr This enables human-machine collaborative steering control. In the diagram, the driver model uses a long-range pre-aiming angle θ. f and near aiming angle θ n As input, after being processed by a series of transfer functions, the steering wheel torque T is generated. dr .

[0134] Since the relationship between environmental visibility V, road adhesion coefficient μ, and weighting coefficient κ cannot be precisely expressed mathematically, a fuzzy control method is used to determine the dynamic weighting coefficient. The range of environmental visibility V is [0, 500], the range of road adhesion coefficient μ is [0, 1], and the fundamental universe of discourse of the weighting coefficient κ is [0, 1]. The fuzzy subsets of environmental visibility V and road adhesion coefficient μ are {L, ML, M, MH, H}, representing the five states of low, relatively low, moderate, relatively high, and high for both. The fuzzy subset of the weighting coefficient κ is also {L, ML, M, MH, H}, representing the five states of low, relatively low, moderate, relatively high, and high for the coordination coefficient. The function graphs of environmental visibility V and road adhesion coefficient μ are shown below. Figure 5 and Figure 6 As shown, the weighting coefficient κ is as follows Figure 7 As shown. Since there are 5 fundamental universes of discourse for both environmental visibility V and road adhesion coefficient μ, a total of 5*5=25 rules need to be defined. The basis for formulating the fuzzy rules is: when both environmental visibility V and road adhesion coefficient μ are high, the far-point pre-aiming weight is considered large, and the weight coefficient is large; when both environmental visibility V and road adhesion coefficient μ are small, the near-point pre-aiming weight is considered large, and the weight coefficient is small. The specific rules are shown in Table 1, and the fuzzy rule surface for the weight coefficients is as follows. Figure 8 As shown.

[0135] Table 1. Fuzzy Control Rules for Dynamic Weight Coefficient κ

[0136]

[0137] (2) Vehicle dynamics model establishment

[0138] In this method, the vehicle dynamics model uses a two-degree-of-freedom model to represent the vehicle's lateral and yaw motions. Based on the torque and moment balance equations, the vehicle's lateral velocity v is obtained. y The expression for the vehicle's yaw rate r is as follows:

[0139]

[0140] Where m is the mass of the vehicle, v y Let v be the lateral velocity of the vehicle in the vehicle coordinate system. x Let F be the vehicle's longitudinal velocity in the vehicle coordinate system, r be the vehicle's yaw rate, and F be the vehicle's longitudinal velocity. yf F is the lateral force on the front wheels of the vehicle. yr I is the lateral force on the rear wheel of the vehicle. z Let be the moment of inertia of the vehicle about the z-axis, and a and b be the distances from the front and rear axles to the center of mass, respectively.

[0141] The normal load on the tire is as follows:

[0142]

[0143] The approximate sideslip angles of the front and rear wheels of the vehicle are as follows:

[0144]

[0145] Linear tire models cannot accurately reflect the actual changing trend of tire forces in rain and fog conditions. This method ignores the influence of longitudinal tire forces and uses the magic tire formula under pure lateral slip conditions to calculate the lateral tire forces, as shown in the following formula:

[0146]

[0147] Where μ is the road surface adhesion coefficient, F zf and F zr These are the tire normal loads, B f C f and D f B is the Magic Formula empirical parameter for the front wheels of the vehicle. r C r and D r These are the Magic Formula empirical parameters for the rear wheels of the vehicle.

[0148] Although the magic formula can accurately represent the nonlinear characteristics of vehicle tires in rain and fog conditions, its complex form makes it computationally intensive and difficult to solve when substituted into the vehicle dynamics equations and integrated into the vehicle model. Therefore, this method performs continuous local linearization on the tire model at each sampling time to obtain the linearized tire lateral force equation, as follows:

[0149]

[0150] in, This indicates the front wheel slip angle of the vehicle at the current sampling time. This indicates the rear wheel slip angle of the vehicle at the current sampling time; This represents the nominal lateral stiffness of the front wheel at the current sampling moment. This represents the nominal lateral stiffness of the rear wheel at the current sampling moment; This represents the residual lateral force of the front wheel at the current sampling moment. This represents the residual lateral force of the rear wheel at the current sampling moment;

[0151] At each sampling moment, after updating the vehicle state information, the vertical load F of the vehicle's front wheels is calculated using the above formula. zf and rear wheel vertical load F zr And the front wheel slip angle and the rear wheel slip angle; substituting them into the formula, the front wheel slip force at the current sampling time can be calculated. and rear wheel lateral force

[0152] nominal lateral stiffness of the front wheel at the current sampling time Rear wheel nominal lateral stiffness Residual lateral force of the front wheel and the residual lateral force of the rear wheels Calculated using the following formula

[0153]

[0154] The vehicle dynamic equations at each sampling time can be obtained as follows:

[0155]

[0156] (3) Vehicle kinematics model

[0157] Angular deviation ψ e It is the angle between the tangent to the centerline of the road in the direction the vehicle is moving, where ψ e =ψ ref -ψ, where ψ is the vehicle's yaw angle. ref It is the reference point for ψ along the tangent direction of the road centerline. Assuming ψ e In the case of smaller displacements, the deviations in lateral displacement and angle can be expressed by the following formulas:

[0158]

[0159] (4) Vehicle steering system model

[0160] Vehicle steering wheel angle δ s and the vehicle's front wheel steering angle δ f The relationship is as follows:

[0161] δ s =g s ·δ f

[0162] Where, δ s The steering wheel angle of a vehicle; g s This refers to the transmission ratio coefficient of the vehicle steering system.

[0163] Based on the principles of vehicle dynamics, the expression for the wheel return torque of the steering system can be obtained as follows:

[0164]

[0165] Among them, K s The aligning moment coefficient and the sideslip angle α f The expression for the linear region model is as follows:

[0166] K s =-K p C f η t

[0167]

[0168] Where, η t It is the sum of tire drag distance and tilt moment arm, K p It is the steering system coefficient.

[0169] Externally applied total torque T tot The expression is as follows:

[0170] T tot =λT auto +(1-λ)T dr

[0171] Among them, T auto T is the torque output by the automated system. dr λ represents the torque output by the driver model, and λ is the coordination coefficient.

[0172] The torque balance equation for the vehicle steering system is as follows:

[0173]

[0174] Among them, b s and J s These are the coefficient of friction and moment of inertia of the steering column, ω. s This represents the angular velocity of the steering wheel.

[0175] (5) Establish a shared driving vehicle model

[0176] Select the vehicle's steering wheel angular velocity ω s Steering wheel angle δ s , center of mass sideslip angle β, yaw rate r, lateral displacement deviation y e and yaw angle deviation ψ e As a system state, the total steering wheel torque T of the vehicle tot As a system input, the vehicle's lateral displacement deviation y e and yaw angle deviation ψ e As system output.

[0177] Summarizing the above vehicle models, the co-driving vehicle model in rain and fog conditions can be written in state-space form, as follows:

[0178]

[0179] Where w = ρ ref As external input, the state vector and output vector are x = [ω]s ,δ s ,β,r,y e ,ψ e ] and y = [y e ,ψ e and control input u=T tot The system matrix is:

[0180]

[0181] (2) To facilitate controller design, the state-space model above is discretized using Euler discretization, resulting in the following discretized vehicle model:

[0182]

[0183] in, C d =C0,T s Sampling time.

[0184] Step 2: Using the vehicle model from Step 1, design a human-machine torque cooperative automation controller using model predictive control methods.

[0185] (1) Design of Human-Machine Torque Cooperative Automation Controller

[0186] Define the control quantity sequence U(k) as:

[0187]

[0188] Assuming the prediction time domain has P steps and the control time domain has N steps, with N ≤ P, and also assuming that the control variables outside the control time domain remain unchanged, i.e., u(k+N)=u(k+N+1)=…=u(k+P-1), the prediction equation for the next P steps can be derived as follows:

[0189]

[0190] Where x(k+i) is the system state variable at time k+i, i = 0, 1, ..., P; u(k+i) is the optimization variable at time k+i, i = 0, 1, ..., P-1; w(k+i) is the road curvature at time k+i, i = 0, 1, ..., P-1k+i;

[0191] The output prediction equation for the time domain in P steps is as follows:

[0192]

[0193]

[0194] Where y(k+i) is the system output at time k+i, i = 0, 1, ..., P;

[0195] (2) The trajectory tracking problem can be described as the following optimization problem:

[0196]

[0197] stx(k+1)=A d x(k)+B d u(k)+D d w(k)

[0198] Hx(k)≤G

[0199] Δu(k)=u(k)-u(k-1)

[0200] u min (k+i)≤u(k+i)≤u max (k+i), i = 0, 1, ..., m-1

[0201] u(k+i)=0,i=m,m+1,…,p-1

[0202] Where R(K+1)=[r(k+1),r(k+2),…,r(k+p)] Τ 2p×1 The reference vector is ΔU(k). The control increment vector is ΔU(k) ​​= [Δu(k), Δu(k+1), ..., Δu(k+m-1)]. Τ m×1 These are the independent variables in the constrained optimization problem. The output of the time-domain prediction P is Y(k+1|k)=[y(k+1)|k,y(k+2)|k,...,y(k+p)|k] Τ 2p×1 It is predicted by the system model at time k. Based on the actuator saturation of the steering system, a control input constraint u is introduced. max (k)=-u min (k). The state constraint Hx(k)≤G is defined by the stable handling envelope to ensure vehicle stability, which relates to limits on the vehicle roll angle and yaw rate. These limits represent the maximum capacity for a given tire and are based on steady-state assumptions. The vehicle yaw rate limit is...

[0203]

[0204] Where g is the acceleration due to gravity, and μ is the road adhesion coefficient. The constraint condition for the vehicle's center of gravity sideslip angle is:

[0205]

[0206] in, It is the slip angle related to the maximum lateral force, and the state constraint matrices H and G are respectively

[0207]

[0208] Solving the above constrained optimization problem, we obtain the optimal solution u(k) at time k;

[0209] Step 3: Design a driving permission allocation strategy based on fuzzy logic method

[0210] Fuzzy control is used to determine the coordination coefficient under rainy and foggy weather conditions. This is due to the lateral displacement deviation y between the vehicle trajectory and the reference trajectory. e and angular deviation ψ e These are the input components of the driver-targeting model and the automated system, reflecting the vehicle's accuracy in tracking precision. Due to the variable y... e and ψ e The sign of y only indicates the opposite direction, therefore the absolute value |y| is used. e | and |ψ e | Replace.

[0211] Lateral displacement deviation y e The fundamental domain is [0, 1.4], and the angular deviation |ψ e The fundamental universe of discourse for | is [0, 0.25], and the fundamental universe of discourse for the synergy coefficient λ is [0, 1]. Vehicle lateral displacement deviation |y e |and vehicle angle deviation|ψ e The fuzzy subset of | is {S,MS,M,MB,B}, representing the vehicle's lateral displacement deviation |y e |and vehicle angle deviation|ψ e The five states of |small, relatively small, moderate, relatively large, and large, and the fuzzy subset of the coordination coefficient λ is {S,MS,M,MB,B}, representing the five states of coordination coefficient λ, respectively. The lateral displacement deviation |y e |and yaw angle deviation|ψ e The graphs of the functions are as follows: Figure 9 and Figure 10 As shown, the synergy coefficient λ is as follows Figure 11 As shown. Due to the lateral displacement deviation of the vehicle |y e |and vehicle angle deviation|ψ e There are 5 basic domains of discourse, so a total of 5*5=25 rules need to be defined. The basis for formulating fuzzy rules is: when the lateral displacement deviation |y e |Large and angular deviation|ψ e When the deviation is large, it is assumed that the driver is significantly affected by the rain and fog environment, resulting in a high coordination coefficient or complete control of the vehicle's lateral displacement by the auxiliary system; when the lateral displacement deviation is large... e |Smaller and angular deviation|ψ eWhen the coefficient is relatively small, it is assumed that the driver's ability to adapt to rainy and foggy environments is good, so the coordination coefficient should be small or negligible. Specific rules are shown in Table 2. The fuzzy rule surface for the coordination coefficient is as follows: Figure 12 As shown.

[0212] Table 2 Fuzzy Control Rules for Coefficient λ

[0213]

[0214] Step 4: Execution of driver-vehicle cooperative steering control:

[0215] like Figure 2 As shown in the control framework diagram, based on the inputs of road surface adhesion coefficient and environmental visibility, driver torque is generated through a pre-aiming model and a driver model. Combined with the system torque output by the automation system, driving permissions are allocated through a coordination coefficient, forming a total torque that acts on the vehicle steering system, driving the vehicle dynamics to update its state, and acting on the driver and automation system through state feedback. In the next moment, based on the updated vehicle state and environmental conditions, the control quantity is recalculated, and the constraint optimization problem of human-machine torque cooperative steering control is solved. A new control quantity is selected to act on the steering system. This process is repeated to achieve rolling optimization control of human-machine cooperative steering in rainy and foggy weather. The control quantity is applied to the vehicle steering system, and corresponding feedback effects are generated based on the vehicle state, acting on the driver and automation system. In the next moment, based on the current vehicle state, the constraint optimization problem of the human-machine torque cooperative steering control method is resolved, the control quantity is selected to act on the vehicle steering system, and corresponding feedback effects are generated based on the vehicle state, acting on the driver and automation system. This process is repeated to achieve rolling optimization control.

[0216] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A human-machine cooperative steering control method considering driving characteristics in rainy and foggy weather, characterized in that: Includes the following steps: S1. Based on the ground coordinate system and the vehicle body coordinate system, establish a driver model and a human-machine co-driving vehicle model for rain and fog environments; where: The driver model, taking into account changes in environmental visibility and road surface adhesion coefficient, dynamically adjusts the distance of long-distance aiming points and steering wheel torque through aiming weight coefficients determined by fuzzy control methods to improve the driver's ability to perceive the path. It is used to describe the driver's visual behavior and steering control behavior when driving a vehicle in rainy or foggy conditions and performing path tracking. The human-machine co-driving vehicle model uses a two-degree-of-freedom vehicle dynamics model to describe the vehicle's lateral and yaw motions, and employs a magic tire model to simulate tire force changes in rain and fog environments. S2. Using the human-machine co-driving vehicle model established in step S1, design a human-machine cooperative steering control strategy through model predictive control method, which is used to dynamically adjust the steering control quantity according to the vehicle status and environmental conditions. S3. Design a driving permission allocation strategy based on fuzzy control method; the driving permission allocation strategy uses fuzzy control method to determine the human-machine collaboration coefficient under rain and fog weather conditions, so as to dynamically adjust the degree of human-machine collaboration and reasonably allocate the driving permissions of human and machine. S4. Based on the human-machine cooperative steering control strategy and driving authority allocation strategy, and combined with the human-machine control quantities, execute human-machine cooperative driving.

2. The human-machine cooperative steering control method considering driving characteristics in rainy and foggy weather according to claim 1, characterized in that: The ground coordinate system is constructed with the origin O fixed at the current position of the vehicle's center of mass O, the X-axis pointing directly in front of the vehicle at the current moment, and the positive Y-axis direction being the direction in which the X-axis is rotated 90 degrees counterclockwise.

3. The human-machine cooperative steering control method considering driving characteristics in rainy and foggy weather according to claim 1, characterized in that: The vehicle coordinate system is constructed with the origin coinciding with the vehicle's center of mass O, the X-axis pointing directly forward of the vehicle, the Y-axis rotated 90 degrees counterclockwise to form the positive direction of the X-axis, and the Z-axis pointing directly upward of the vehicle and perpendicular to the X-axis and Y-axis.

4. The human-machine cooperative steering control method considering driving characteristics in rainy and foggy weather according to claim 1, characterized in that: The distance of the long-range pre-aiming point is expressed as: L eff =κL f +(1-k)L n , In the formula, L n L is the distance to the near target point N, i.e., the distance from the vehicle's center of gravity O to the near target point N; f The distance for the far-field aiming is F, which is the distance from the vehicle's center of gravity O to the fixed far point F; L eff The distance to the dynamically adjusted long-range preview point is the distance from the vehicle's center of gravity O to the dynamic long-range preview point F. eff The distance; κ is the aiming weight coefficient designed based on the fuzzy control method, taking into account environmental visibility and road surface adhesion coefficient.

5. A human-machine cooperative steering control method considering driving characteristics in rainy and foggy weather according to claim 4, characterized in that: The fuzzy control rules for the pre-aiming weight coefficient κ are shown in the table below: 。 6. A human-machine cooperative steering control method considering driving characteristics in rainy and foggy weather according to claim 5, characterized in that: The fuzzy control rules are formulated based on the following: when the environmental visibility V is high and the road surface adhesion coefficient μ is high, the far-point pre-aiming weight is considered to be large, and the weight coefficient is large; when the environmental visibility V is small and the road surface adhesion coefficient μ is small, the near-point pre-aiming weight is considered to be large, and the weight coefficient is small.

7. The human-machine cooperative steering control method considering driving characteristics in rainy and foggy weather according to claim 1, characterized in that: The process of establishing the human-machine co-driving vehicle model includes the following steps: S71. Establish the vehicle dynamics model: Based on the torque and moment balance equations, the lateral velocity v of the vehicle is obtained. y The expression for the vehicle's yaw rate r: Where m is the mass of the vehicle, v y Let v be the lateral velocity of the vehicle in the vehicle coordinate system. x Let F be the vehicle's longitudinal velocity in the vehicle coordinate system, r be the vehicle's yaw rate, and F be the vehicle's longitudinal velocity. yf F is the lateral force on the front wheels of the vehicle. yr I is the lateral force on the rear wheel of the vehicle. z Let be the moment of inertia of the vehicle about the z-axis, and a and b be the distances from the front and rear axles to the center of mass, respectively. The normal load of a tire is expressed as: The approximate slip angles of the front and rear wheels of a vehicle are expressed as follows: Linear tire models cannot accurately reflect the actual changing trend of tire forces in rain and fog conditions. This method ignores the influence of longitudinal tire forces and uses the magic tire formula under pure lateral slip conditions to calculate the lateral tire forces: Where μ is the road surface adhesion coefficient, F zf and F zr These are the tire normal loads, B f C f and D f B is the Magic Formula empirical parameter for the front wheels of the vehicle. r C r and D r The Magic Formula empirical parameters for the rear wheels of the vehicle; Although the magic formula can accurately represent the nonlinear characteristics of vehicle tires in rain and fog conditions, its complex form makes it computationally intensive and difficult to solve when substituted into the vehicle dynamics equations and integrated into the vehicle model. Therefore, this method performs continuous local linearization on the tire model at each sampling time to obtain a linearized tire lateral force equation: in, This indicates the front wheel slip angle of the vehicle at the current sampling time. This indicates the rear wheel slip angle of the vehicle at the current sampling time; This represents the nominal lateral stiffness of the front wheel at the current sampling moment. This represents the nominal lateral stiffness of the rear wheel at the current sampling moment; This represents the residual lateral force of the front wheel at the current sampling moment. This represents the residual lateral force of the rear wheel at the current sampling moment; At each sampling moment, after updating the vehicle state information, the vertical load F of the vehicle's front wheels is calculated using the above formula. zf and rear wheel vertical load F zr And the front wheel slip angle and the rear wheel slip angle; substituting them into the formula, the front wheel slip force at the current sampling time can be calculated. and rear wheel lateral force nominal lateral stiffness of the front wheel at the current sampling time Rear wheel nominal lateral stiffness Residual lateral force of the front wheel and the residual lateral force of the rear wheels Calculated using the following formula The resulting vehicle dynamic equations at each sampling time are: In the formula, m is the mass of the vehicle, and β is the sideslip angle of the vehicle's center of gravity. x Let F be the vehicle's longitudinal velocity in the vehicle coordinate system, r be the vehicle's yaw rate, and F be the vehicle's longitudinal velocity. yf F is the lateral force on the front wheels of the vehicle. yr I is the lateral force on the rear wheel of the vehicle. z Let be the moment of inertia of the vehicle about the z-axis, and a and b be the distances from the front and rear axles to the center of mass, respectively. The nominal lateral stiffness of the front wheel at the current sampling moment. The nominal lateral stiffness of the rear wheel at the current sampling moment. The residual lateral force of the front wheel, The residual lateral force of the rear wheel, δ f The steering angle of the vehicle's front wheels; S72. Establish the vehicle kinematics model: Assuming angular deviation ψ e In the case of smaller displacements, the deviations in lateral displacement and angle are expressed by the following formulas: In the formula, the vertical distance between the near aiming point N and the road in the direction of vehicle movement is defined as the lateral displacement deviation y. e =yy ref Where y is the lateral displacement, y ref This is a reference value along the road centerline; v x L is the vehicle's longitudinal velocity in the vehicle coordinate system; β is the vehicle's sideslip angle; r is the vehicle's yaw rate; L n The distance to the near target point N is the distance from the vehicle's center of gravity O to the near target point N; the reference curvature ρ ref =1 / R ref It is the curvature of the inner lane, where R ref It is the radius of curvature; angular deviation ψ e It is the angle between the vehicle's forward direction and the tangent to the road centerline, with the angle deviation ψ. e =ψ ref -ψ, where ψ is the vehicle's yaw angle. ref It is the reference point for ψ along the tangent direction of the road centerline; S73. Establish a vehicle steering system model. Vehicle steering wheel angle δ s and the vehicle's front wheel steering angle δ f The relationship is as follows: d s =g s ·d f , Where, δ s The steering wheel angle of a vehicle; g s This refers to the transmission ratio coefficient of the vehicle steering system; Based on the principles of vehicle dynamics, the expression for the wheel return torque of the steering system is as follows: Among them, K s The aligning moment coefficient and the sideslip angle α f The expression for the linear region model is as follows: K s =-K p C f or t , Where, η t It is the sum of tire drag distance and tilt moment arm, K p It is the steering system coefficient; Externally applied total torque T tot The expression is as follows: T tot =λT auto +(1-λ)T dr , Among them, T auto T is the torque output by the automated system. dr λ represents the torque output by the driver model, and λ is the coordination coefficient. The torque balance equation for the vehicle steering system is as follows: Among them, b s and J s These are the coefficient of friction and moment of inertia of the steering column, ω. s This represents the angular velocity of the steering wheel. S74. Establish a human-machine co-driving vehicle model. Select the vehicle's steering wheel angular velocity ω s Steering wheel angle δ s , center of mass sideslip angle β, yaw rate r, lateral displacement deviation y e and yaw angle deviation ψ e As a system state, the total steering wheel torque T of the vehicle tot As a system input, the vehicle's lateral displacement deviation y e and yaw angle deviation ψ e As system output; Organizing the vehicle dynamics model, vehicle kinematics model, and vehicle steering system model, the vehicle model under rain and fog conditions can be written in state-space form as follows: Where w = ρ ref As external input, the state vector and output vector are x = [ω] s ,δ s ,β,r,y e ,ψ e ] and y = [y e ,ψ e and control input u=T tot The system matrix is: D d =[0 0 0 0 -v x L n v x ] Τ , To facilitate controller design, the above state-space model is discretized using Euler discretization to obtain the discretized vehicle model: in, C d =C0,T s Sampling time.

8. The human-machine cooperative steering control method considering driving characteristics in rainy and foggy weather according to claim 1, characterized in that: The design process of the human-machine cooperative steering control strategy includes the following steps: S81, Design of Human-Machine Torque Cooperative Automation Controller Define the control quantity sequence U(k) as: Assuming the prediction time domain has P steps and the control time domain has N steps, with N ≤ P, and also assuming the control variables outside the control time domain remain constant, i.e., u(k+N)=u(k+N+1)=…=u(k+P-1), the prediction equations for the next P steps are derived as follows: Where x(k+i) is the system state variable at time k+i, i = 0, 1, ..., P; u(k+i) is the optimization variable at time k+i, i = 0, 1, ..., P-1; w(k+i) is the road curvature at time k+i, i = 0, 1, ..., P-1k+i; The output prediction equation for the P-step prediction in the time domain is as follows: Where y(k+i) is the system output at time k+i, i = 0, 1, ..., P; S82. The trajectory tracking problem is described as an optimization problem with the following constraints: Where R(K+1)=[r(k+1),r(k+2),…,r(k+p)] Τ 2p×1 The reference vector is ΔU(k); the control increment vector is ΔU(k) ​​= [Δu(k), Δu(k+1), ..., Δu(k+m-1)]. Τ m×1 The independent variable is the time-domain P output Y(k+1|k)=[y(k+1)|k,y(k+2)|k,…,y(k+p)|k] Τ 2p×1 It is predicted by the system model at time k; based on the actuator saturation of the steering system, a control input constraint u is introduced. max (k)=-u min (k); The state constraint Hx(k)≤G is defined by the stable control envelope to ensure vehicle stability, which is related to the limits on vehicle roll angle and yaw rate; these limits show the maximum capacity of a given tire and are based on steady-state assumptions; the vehicle yaw rate limit is: Where g is the acceleration due to gravity, and μ is the road adhesion coefficient. The constraint condition for the vehicle's center of gravity sideslip angle is: in, It is the slip angle related to the maximum lateral force, and the state constraint matrices H and G are respectively: Solving the above constrained optimization problem yields the optimal solution u(k) at time k.

9. A human-machine cooperative steering control method considering driving characteristics in rainy and foggy weather according to claim 1, characterized in that: The fuzzy control rules for the human-machine collaboration coefficients are shown in the table below: 。 10. A human-machine cooperative steering control method considering driving characteristics in rainy and foggy weather according to claim 9, characterized in that: The fuzzy control rule is based on: when the lateral displacement deviation |y e |Large and angular deviation|ψ e When the deviation is large, it is assumed that the driver is significantly affected by the rain and fog environment, resulting in a high coordination coefficient or complete control of the vehicle's lateral displacement by the auxiliary system; when the lateral displacement deviation is large... e |Smaller and angular deviation|ψ e When the coefficient is relatively small, it is assumed that the driver has a good ability to adapt to rain and fog conditions, so the coordination coefficient should be small or low.

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