AFWS / DYC transverse stability coordination control method considering driving style

Abstract

The invention relates to an AFWS / DYC transverse stability coordination control method considering a driving style, and the method comprises the steps: obtaining the driving style of a driver through a constructed driving style online identification model, obtaining a reference yaw velocity according to the driving style of the driver, and determining an instability degree index according to the reference yaw velocity and an ideal side slip angle; and performing a cooperative game and a non-cooperative game based on the reference yaw velocity, the ideal side slip angle and the instability degree index, and obtaining a final front wheel rotation angle increment and a final rear wheel rotation angle increment through the influence of the final direct yaw moment on the initial front wheel rotation angle increment and the initial rear wheel rotation angle increment. According to the invention, the driving style is applied to the vehicle transverse stability coordination control, and the cooperative game and the non-cooperative game are integrated, so that the vehicle transverse stability control can meet the individual demands of different drivers.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to an AFWS / DYC lateral stability coordinated control method taking driving style into consideration. Background Art

[0002] Vehicle driving safety has always been one of the important issues that road safety management departments around the world pay attention to. When a vehicle is driving on a low-adhesion road, the lateral force is easily saturated, resulting in side slip and even loss of control of the vehicle. Vehicle lateral stability control improves vehicle stability and safety by controlling the vehicle to track the ideal yaw rate and the ideal center of mass sideslip angle. At present, most of the focus of vehicle lateral dynamics control is on stability control under extreme conditions, but under normal driving conditions, extreme instability is not common.

[0003] At the same time, in the study of modern vehicle dynamics, researchers are paying more and more attention to the differences in needs between different individual drivers, and designing different control strategies according to the needs of different individual drivers in order to improve the driving experience of different drivers. The focus of optimizing the relationship between people and vehicles has shifted from meeting the general needs of ordinary drivers to paying more attention to the special needs of each individual driver. Controlling the vehicle only under extreme conditions is not only a waste of dynamic performance, but also the dynamic characteristics of the whole vehicle are difficult to meet the personalized needs of different drivers. There is a lack of personalized lateral stability control strategies that take driving style into account. Summary of the invention

[0004] In order to overcome at least one deficiency in the prior art, the present application provides an AFWS / DYC lateral stability coordinated control method considering driving style.

[0005] In a first aspect, a method for coordinated lateral stability control of AFWS / DYC considering driving style is provided, comprising:

[0006] Construct an online driving style identification model;

[0007] Obtain the driver's online lane-changing data and extract characteristic indicators;

[0008] Input the characteristic index into the online driving style identification model to obtain the driver's driving style;

[0009] Determine a vehicle stability factor according to the driver's driving style; determine a driving style gain coefficient according to the vehicle stability factor; obtain a reference yaw angular velocity according to the driving style gain coefficient;

[0010] Determine the instability index based on the reference yaw rate and the ideal center of mass sideslip angle;

[0011] Based on the reference yaw rate, the ideal center of mass sideslip angle and the instability index, cooperative games and non-cooperative games are performed to obtain a preliminary front wheel turning angle increment, a preliminary rear wheel turning angle increment and a final direct yaw moment; the final direct yaw moment is used to influence the preliminary front wheel turning angle increment and the preliminary rear wheel turning angle increment to obtain a final front wheel turning angle increment and a final rear wheel turning angle increment;

[0012] The final front wheel steering angle increment and the final rear wheel steering angle increment are applied to the steering wheel, and the final direct yaw moment is converted into tire longitudinal force and distributed to each wheel.

[0013] In one embodiment, building a driving style online identification model includes:

[0014] The lane changing process data of multiple drivers are obtained, and characteristic indicators are extracted from the lane changing process data of each driver as training samples;

[0015] Perform cluster analysis on all training samples to determine the driving style category of each training sample;

[0016] The training samples and the corresponding driving style categories constitute the driving style dataset;

[0017] The GRNN is trained based on the driving style dataset to obtain the driving style online identification model.

[0018] In one embodiment, obtaining the driver's online lane change data and extracting characteristic indicators include:

[0019] Step S21, obtaining the distance change Δdis between the vehicle and the lane line at the current time t0 lane , if Δdis lane >3, then go to step S22, otherwise, repeat step S21 at the next moment;

[0020] Step S22, calculate the distance dis between the vehicle and the obstacle at time t0-5 front , if 40>dis front >0, then go to step S23, otherwise go to step S21;

[0021] Step S23, determining the lane change start time and end time, includes:

[0022] If str sw (t)<10° and str sw (t+0.5)>15°, t represents the time, t∈[t0-5,t0+5], str sw (t) is the steering wheel angle at time t, then the first time t1 = t;

[0023] If strsw (t)<-15° and str sw (t+0.5)>-10°, then the second moment t2=t;

[0024] Start time t start =min(t1,t2), end time t end =mac(t1,t2);

[0025] Step S24, extracting the vehicle driving data between the lane changing start time and the lane changing end time, i.e., the driver's online lane changing data;

[0026] Step S25, extracting characteristic indicators based on the driver's online lane changing data, the characteristic indicators include speed standard deviation, maximum absolute value of lateral acceleration, standard deviation of lateral acceleration, average absolute value of steering wheel angle, and average absolute value of steering wheel angular velocity.

[0027] In one embodiment, the reference yaw rate is:

[0028]

[0029] in, is the reference yaw rate, Gain is the driving style gain coefficient, ω r_d is the ideal yaw rate.

[0030] In one embodiment, determining the instability index according to the reference yaw rate and the ideal center of mass sideslip angle includes:

[0031] The β-ω phase plane is divided into three regions, namely stable region I, transition region II, and unstable region III;

[0032] Determine the area to which the vehicle belongs according to the reference yaw rate and the ideal center of mass sideslip angle;

[0033] According to the area to which the vehicle belongs, the following formula is used to determine the instability index:

[0034]

[0035] Among them, S ta is the instability index, Δγ r is the deviation between the actual yaw rate and the ideal yaw rate, Δγ max is the maximum deviation between the actual yaw rate and the ideal yaw rate, Δβ r is the deviation between the actual center of mass sideslip angle and the ideal center of mass sideslip angle, Δβ max It is the maximum value of the deviation between the actual sideslip angle and the ideal sideslip angle.

[0036] In one embodiment, the resulting direct yaw moment is:

[0037]

[0038] in, is the final direct yaw moment, k is the sampling time, L1 is the coefficient matrix of the direct yaw moment, and θ1 is the variable; for The amount of decomposition, is the weight matrix, T stands for transpose; for The amount of decomposition, is the weight matrix, Ω is the coefficient matrix; is the predicted stability control target; Λ is the coefficient matrix, X(k) is the matrix variable; Υ is the coefficient matrix.

[0039] In one embodiment, the weight matrix for:

[0040]

[0041] r1=10 -(5+4LA)

[0042]

[0043] Among them, r1 is the control output weight of the direct yaw moment, LA is the parameter, S ta is an index of instability degree, and N represents the prediction time domain.

[0044] In one embodiment, the final front wheel steering angle increment and the final rear wheel steering angle increment are:

[0045]

[0046] in, is the final front wheel steering angle increment, is the final rear wheel steering angle increment, K1 and K2 are feedback gains, is the inverse of K2, E is the coefficient matrix, x(k) is the state variable, L2 is the coefficient matrix of the front wheel steering angle increment, L3 is the coefficient matrix of the rear wheel steering angle increment, is the coefficient matrix of the prediction equation, L1 is the coefficient matrix of the direct yaw moment, θ1 is the variable, and Z is the coefficient matrix.

[0047] In a second aspect, an AFWS / DYC lateral stability coordinated control device considering driving style is provided, comprising:

[0048] Model building module, used to build an online driving style identification model;

[0049] A characteristic index acquisition module is used to obtain the driver's online lane change data and extract characteristic indicators;

[0050] An identification module is used to input characteristic indicators into an online driving style identification model to obtain the driver's driving style;

[0051] A reference yaw rate acquisition module is used to determine a vehicle stability factor according to a driver's driving style; determine a driving style gain coefficient according to the vehicle stability factor; and obtain a reference yaw rate according to the driving style gain coefficient;

[0052] An instability index determination module is used to determine an instability index according to a reference yaw angular velocity and an ideal center of mass sideslip angle;

[0053] A control quantity acquisition module is used to perform cooperative games and non-cooperative games based on a reference yaw rate, an ideal center of mass sideslip angle, and an instability index to obtain a preliminary front wheel angle increment, a preliminary rear wheel angle increment, and a final direct yaw moment; and to use the final direct yaw moment to influence the preliminary front wheel angle increment and the preliminary rear wheel angle increment to obtain a final front wheel angle increment and a final rear wheel angle increment;

[0054] The control module is used for applying the final front wheel steering angle increment and the final rear wheel steering angle increment to the steering wheel, and converting the final direct yaw moment into tire longitudinal force and distributing it to each wheel.

[0055] In a third aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned AFWS / DYC lateral stability coordination control method considering driving style is implemented.

[0056] Compared with the prior art, the present application has the following beneficial effects: the present application obtains the driver's driving style through the constructed online driving style identification model, obtains the reference yaw rate according to the driver's driving style, and determines the instability index according to the reference yaw rate and the ideal center of mass sideslip angle; based on the reference yaw rate, the ideal center of mass sideslip angle and the instability index, cooperative game and non-cooperative game are carried out, and the initial front wheel angle increment and the initial rear wheel angle increment are affected by the final direct yaw torque to obtain the final front wheel angle increment and the final rear wheel angle increment. The present application applies driving style to the coordinated control of vehicle lateral stability, and combines cooperative game and non-cooperative game, so that the vehicle lateral stability control can meet the personalized needs of different drivers. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The present application may be better understood by referring to the following description given in conjunction with the accompanying drawings, which together with the following detailed description are included in this specification and form a part of this specification. In the drawings:

[0058] Figure 1 A flowchart of the AFWS / DYC lateral stability coordinated control method considering driving style is shown;

[0059] Figure 2 The schematic diagram of the test road setup for the driver in the ring is shown, (a) is a small turning road, (b) is a large turning road;

[0060] Figure 3 The vehicle stability factor distribution diagrams corresponding to various driving styles are shown, wherein (a) is the vehicle stability factor distribution diagram corresponding to the cautious type, (b) is the vehicle stability factor distribution diagram corresponding to the general type, and (c) is the vehicle stability factor distribution diagram corresponding to the aggressive type;

[0061] Figure 4 A schematic diagram of the division of the β-ω phase plane is shown;

[0062] Figure 5 A schematic diagram of the phase plane transition domain division is shown;

[0063] Figure 6 The block diagram of the game-based personalized AFWS / DYC lateral stability controller is shown;

[0064] Figure 7 The established linear two-degree-of-freedom vehicle model of a four-wheel steering vehicle is shown;

[0065] Figure 8 The structural block diagram of the AFWS / DYC lateral stability coordination control device considering driving style is shown. DETAILED DESCRIPTION

[0066] The exemplary embodiments of the present application will be described below in conjunction with the accompanying drawings. For the sake of clarity and conciseness, not all features of the actual embodiments are described in the specification. However, it should be understood that many implementation-specific decisions can be made in the process of developing any such actual embodiments in order to achieve the specific goals of the developer, and these decisions may vary from embodiment to embodiment.

[0067] It is also necessary to explain here that, in order to avoid obscuring the present application due to unnecessary details, only the device structure closely related to the scheme according to the present application is shown in the drawings, while other details that are not closely related to the present application are omitted.

[0068] It should be understood that the present application is not limited to the described implementation forms due to the following description with reference to the accompanying drawings. In this article, where feasible, the embodiments can be combined with each other, features between different embodiments can be replaced or borrowed, and one or more features can be omitted in one embodiment.

[0069] The present application embodiment provides an AFWS / DYC lateral stability coordinated control method considering driving style, Figure 1 A flowchart of the AFWS / DYC lateral stability coordinated control method considering driving style is shown in FIG. Figure 1 , methods include:

[0070] Step S1, constructing an online driving style recognition model.

[0071] Step S2, obtaining the driver's online lane changing data and extracting characteristic indicators.

[0072] Step S3: input the characteristic index into the online driving style identification model to obtain the driver's driving style.

[0073] Step S4, determining a vehicle stability factor according to the driver's driving style; determining a driving style gain coefficient according to the vehicle stability factor; and obtaining a reference yaw angular velocity according to the driving style gain coefficient.

[0074] Step S5, determining an instability degree index according to a reference yaw angular velocity and an ideal center of mass sideslip angle.

[0075] Step S6, performing cooperative game and non-cooperative game based on the reference yaw rate, the ideal center of mass sideslip angle and the instability index, obtaining a preliminary front wheel angle increment, a preliminary rear wheel angle increment and a final direct yaw moment (DYS); using the final direct yaw moment to influence the preliminary front wheel angle increment and the preliminary rear wheel angle increment, obtaining a final front wheel angle increment and a final rear wheel angle increment (AFWS);

[0076] Step S7: Apply the final front wheel steering angle increment and the final rear wheel steering angle increment to the steering wheel, and convert the final direct yaw moment into tire longitudinal force and distribute it to each wheel.

[0077] In this embodiment, the driving style of the driver is obtained through the constructed online driving style identification model, the reference yaw rate is obtained according to the driving style of the driver, and the instability index is determined according to the reference yaw rate and the ideal center of mass sideslip angle; based on the reference yaw rate, the ideal center of mass sideslip angle and the instability index, cooperative game and non-cooperative game are carried out, and the preliminary front wheel angle increment and the preliminary rear wheel angle increment are affected by the final direct yaw torque to obtain the final front wheel angle increment and the final rear wheel angle increment. This embodiment applies driving style to the coordinated control of vehicle lateral stability, and combines cooperative game and non-cooperative game, so that the vehicle lateral stability control can meet the personalized needs of different drivers.

[0078] In one embodiment, step S1, constructing an online driving style identification model, includes:

[0079] Step S11, obtaining lane changing process data of multiple drivers, and extracting feature indicators from the lane changing process data of each driver as training samples.

[0080] A driving style data acquisition platform is built using Matlab / Simulink, Prescan and Logitech G29 driving simulator, so that PreScan is used to establish a standardized vehicle handling stability test scene, and a traffic scene infrastructure component is established through a GUI interface; the vehicle model adopts a 10-degree-of-freedom vehicle dynamics model including vehicle longitudinal, lateral, vertical, yaw, roll, pitch and tire movement. The present invention selects 27 drivers (including 24 males and 3 females) to conduct a driving style data acquisition experiment to collect data, collect vehicle speed, vehicle lateral acceleration, steering wheel angle and steering wheel angular velocity data, extract data from all lane change processes, and calculate driving style characteristic indicators, including: X1 speed standard deviation, X2 lateral acceleration absolute maximum value, X3 lateral acceleration standard deviation, X4 steering wheel angle absolute value average, X5 steering wheel angular velocity absolute value average. The characteristic index values ​​at the same time are combined as a single data sample x = (X1, X2, X3, X4, X5), and finally all data samples are constructed into a data set D = (x1, x2, x3, ..., xn), where n is the number of samples in the data set.

[0081] Step S12: performing cluster analysis on all training samples to determine the driving style category of each training sample.

[0082] Here, the K-means algorithm can be used to perform cluster analysis on the vehicle driving status. Drivers are divided into three categories through clustering, and numbers are used to represent different driving styles. Among them, 1 is a cautious driver, 2 is a general driver, and 3 is an aggressive driver. The driver's driving style category data set is obtained.

[0083] Step S13: training samples and corresponding driving style categories form a driving style dataset; merging the sample dataset and the driving style category dataset into a driving style dataset D ds , D ds =[D,D driver ].

[0084] Step S14: training a GRNN (Generalized Regression Neural Network) based on the driving style data set to obtain an online driving style recognition model.

[0085] Here, the driving style dataset D ds As the model training set. X1 speed standard deviation, X2 lateral acceleration absolute maximum, X3 lateral acceleration standard deviation, X4 steering wheel angle absolute average, X5 steering wheel angular velocity absolute average are used as the input of the model training set, "cautious type", "normal type" and "aggressive type" are represented by "1, 0, 0", "0, 1, 0" and "0, 0, 1" respectively and used as the output of the model training set.

[0086] The GRNN network mainly consists of four layers: input layer, mode layer, summation layer, and output layer.

[0087] Input layer: implements the data input process and passes the input data to the pattern layer. The number of nodes in this layer is the feature dimension of the input data. This article uses five feature indicators, so the number of nodes is 5, where x1 is the speed standard deviation, x2 is the maximum absolute value of the lateral acceleration, x3 is the lateral acceleration standard deviation, x4 is the average absolute value of the steering wheel angle, and x5 is the average absolute value of the steering wheel angular velocity.

[0088] Pattern layer: Generally, Gaussian function is used to process input data, and the number of nodes is the number of training samples:

[0089] Summation layer: The summation layer mainly performs two summation calculations, one of which is the arithmetic sum of the outputs of all pattern layer neurons, and the other is the weighted sum of the outputs of all pattern layer neurons:

[0090] Output layer: The number of nodes in the output layer is the output sample dimension. The arithmetic sum and weighted sum obtained by the summation layer are calculated and output to obtain the network prediction output result. When the output result is (1,0,0), the driving style is cautious, when the output result is (0,1,0), the driving style is general, and when the output result is (0,0,1), the driving style is aggressive.

[0091] In one embodiment, step S2, obtaining the driver's online lane change data and extracting characteristic indicators, includes:

[0092] Step S21, obtaining the distance change Δdis between the vehicle and the lane line at the current time t0 lane , if Δdis lane >3, then go to step S22, otherwise, repeat step S21 at the next moment;

[0093] Step S22, calculate the distance dis between the vehicle and the obstacle at time t0-5 front , if 40>dis front >0, then go to step S23, otherwise go to step S21; here, the obstacle can be a vehicle or other obstacles, and the purpose of this step is to determine whether the driver changes lanes because there is a vehicle or obstacle ahead.

[0094] Step S23, determining the lane change start time and end time, includes:

[0095] If str sw (t)<10° and str sw (t+0.5)>15°, t represents the time, t∈[t0-5,t0+5], str sw (t) is the steering wheel angle at time t, then the first time t1 = t;

[0096] If str sw (t)<-15° and str sw (t+0.5)>-10°, then the second moment t2=t;

[0097] Start time t start =min(t1,t2), end time t end =max(t1,t2);

[0098] This step is done by calculating the steering wheel angle str 10s before and after time t0. sw Perform cyclic judgment to determine the start and end time of lane change.

[0099] Step S24, extracting the vehicle driving data between the lane changing start time and the lane changing end time, that is, the driver's online lane changing data; here, the vehicle driving data may include vehicle speed, vehicle lateral acceleration, steering wheel angle, and steering wheel angular velocity data, etc.

[0100] Step S25, extracting characteristic indicators based on the driver's online lane changing data, the characteristic indicators include speed standard deviation, maximum absolute value of lateral acceleration, standard deviation of lateral acceleration, average absolute value of steering wheel angle, and average absolute value of steering wheel angular velocity.

[0101] In one embodiment, step S4, determining a vehicle stability factor according to the driver's driving style; determining a driving style gain coefficient according to the vehicle stability factor; and obtaining a reference yaw rate according to the driving style gain coefficient, includes:

[0102] First, a vehicle stability factor distribution map corresponding to each driving style is obtained.

[0103] Design two types of roads: roads with small curvature radius and roads with large curvature radius. Figure 2 The figure shows the schematic diagram of the driver setting on the ring test road, (a) is a small turning road and (b) is a large turning road. The large curvature radius road is a curve with a length of 85m and a radius of 700m, and the small curvature radius road is a double lane change road. Drivers with different driving styles were tested under 18 different vehicle speeds, roads and road adhesion coefficients as shown in Table 1, and the driving style gain coefficient Gain was adjusted to the ideal response of the vehicle, and the corresponding vehicle stability factor K was calculated. style , calculate the vehicle stability factor K for each type of driving style driver style The average value is used as the control parameter of each type of controller, and the vehicle stability factor K of drivers with different driving styles is finally obtained. style data; Figure 3 The vehicle stability factor distribution diagrams corresponding to various driving styles are shown, where (a) is the vehicle stability factor distribution diagram corresponding to the cautious type, (b) is the vehicle stability factor distribution diagram corresponding to the general type, and (c) is the vehicle stability factor distribution diagram corresponding to the aggressive type.

[0104] Table 1 Driver in-circuit test conditions

[0105]

[0106] Then, after obtaining the driver's driving style, according to the known sliding friction coefficient μ, vehicle longitudinal speed v x The vehicle stability factor K can be determined style ;

[0107] Then, the driving style gain factor Gain can be determined according to the following formula:

[0108]

[0109] Among them, K is the original stability factor.

[0110] Then, according to the following formula, the reference yaw rate is obtained

[0111]

[0112] in, is the reference yaw rate, Gain is the driving style gain coefficient, ω r_d is the ideal yaw rate.

[0113] In one embodiment, step S5, determining the instability index according to the reference yaw rate and the ideal center of mass sideslip angle, includes:

[0114] Firstly, the β-ω phase plane is divided into three regions, namely stable region I, transition region II, and unstable region III. Figure 4 A schematic diagram of the β-ω phase plane division is shown. Figure 5 A schematic diagram of the phase plane transition domain division is shown.

[0115] Then, according to the reference yaw rate and the ideal center of mass sideslip angle β des , determine the area to which the vehicle belongs; here, set the ideal center of mass sideslip angle β des =0.

[0116] according to Figure 4 , given a known reference yaw rate and the ideal center of mass sideslip angle β des In this case, the area to which the vehicle belongs can be determined.

[0117] Then, according to the area to which the vehicle belongs, the instability index is determined using the following formula:

[0118]

[0119] Among them, S ta is the instability index, Δγ r is the deviation between the actual yaw rate and the ideal yaw rate, Δγ max is the maximum deviation between the actual yaw rate and the ideal yaw rate, Δβ r is the deviation between the actual center of mass sideslip angle and the ideal center of mass sideslip angle, Δβ max It is the maximum value of the deviation between the actual sideslip angle and the ideal sideslip angle.

[0120] In this embodiment, the instability index S is obtained by calculation ta ,The purpose is to coordinate the weight matrices of AFWS and DYC in the middle level controller in the subsequent steps.

[0121] In one embodiment, the specific implementation of step S6 is a personalized AFWS / DYC lateral stability controller based on game theory, which adopts hierarchical control and is divided into three layers: upper, middle and lower. Figure 6 The block diagram of the game-based personalized AFWS / DYC lateral stability controller is shown. Here, the upper controller is used to implement step S4 and step S5, which will not be described in detail here. Figure 7 The established linear two-degree-of-freedom vehicle model of a four-wheel steering vehicle is shown.

[0122] The middle-level controller is used to implement step S6. Specifically, a cooperative game is performed based on the reference yaw rate, the ideal center of mass sideslip angle and the instability index to obtain a preliminary front wheel steering angle increment and a preliminary rear wheel steering angle increment using the following formula:

[0123]

[0124] Among them, U′2(k) is the initial front wheel steering angle increment, U′3(k) is the initial rear wheel steering angle increment, K1 and K2 are feedback gains, is the inverse of K2, E is the coefficient matrix, x(k) is the state variable, L2 is the coefficient matrix of the front wheel steering angle increment, L3 is the coefficient matrix of the rear wheel steering angle increment, is the coefficient matrix of the prediction equation, Z is the coefficient matrix, and U1(k) is the direct yaw moment.

[0125] The specific representation of each parameter is given below:

[0126]

[0127] Among them, A (2) is the coefficient matrix of the tracker (front wheel angle increment and rear wheel angle increment);

[0128]

[0129] Wherein, i is the index of the control quantity, i=2,3, when i=2 it represents the front wheel steering angle increment, i=3 represents the rear wheel steering angle increment. is the coefficient matrix of the prediction equation corresponding to the i-th control quantity;

[0130] in, is the coefficient matrix of the prediction equation corresponding to the rear wheel steering angle increment;

[0131] S Q is the decomposition of Q, Q is the weight matrix;

[0132]

[0133] in, is the weight matrix, λ i is the weight coefficient of the participant cost function, and λ i >0,∑λ i =1;Q i is the weight coefficient, q β is the weight coefficient corresponding to the sideslip angle of the center of mass, is the weight coefficient corresponding to the yaw angular velocity;

[0134] For R i The decomposition amount, R i is the weight matrix;

[0135]

[0136] in, is the weight matrix;

[0137]

[0138] r1=10 -(5+4LA)

[0139] r2=10+140LA

[0140] r3=r2

[0141]

[0142] Among them, r i is the control output weight of the i-th control variable, i=1,2,3, i=1 represents the direct yaw moment, LA is the parameter, S ta is an index of instability degree, and N represents the prediction time domain.

[0143]

[0144] in, is the coefficient matrix of the prediction equation, n=1,2,3,4, respectively, are the index of the direct yaw moment, the index of the front wheel angle increment, the index of the rear wheel angle increment and the index of the front wheel angle. j=1,2, respectively, are the index of the leader (direct yaw moment), the index of the follower (front wheel angle increment and rear wheel angle increment). N represents the prediction time domain. It should be noted that in this embodiment, i, j and n are all indexes, which belong to different representation methods of the control quantity index.

[0145] C (j) For the matrix:

[0146]

[0147] Parameter matrix A = I + A0T s , I is the identity matrix, A0 is the coefficient matrix, T s is the sampling interval of the discrete model.

[0148]

[0149] Among them, k f , k ris the tire cornering stiffness of the front and rear wheels, a and b are the distances from the center of mass to the front and rear axles, m is the vehicle mass; u is the longitudinal speed, I z is the moment of inertia of the vehicle around the z-axis.

[0150] Coefficient matrix B n =B 0n T s , B 0n is the coefficient, n=1,2,3,4.

[0151]

[0152] B 04 =B 02

[0153]

[0154] in, Predict output for followers, is the output at time k predicted at the current time k; δ f is the front wheel turning angle.

[0155] Specifically, the final direct yaw moment is:

[0156]

[0157] in, is the final direct yaw moment, k is the sampling time, L1 is the coefficient matrix of the direct yaw moment, and θ1 is the variable; for The amount of decomposition, is the weight matrix, T stands for transpose; for The amount of decomposition, is the weight matrix, Ω is the coefficient matrix; is the predicted stability control target; Λ is the coefficient matrix, X(k) is the matrix variable; Υ is the coefficient matrix.

[0158]

[0159] in, Predict output for leaders, is the output at time k predicted at the current time k. is the reference yaw rate, is the ideal center of mass sideslip angle.

[0160] Specifically, the final direct yaw moment is used to influence the preliminary front wheel steering angle increment and the preliminary rear wheel steering angle increment to obtain the final front wheel steering angle increment and the final rear wheel steering angle increment, using the following formula:

[0161]

[0162] in, is the final front wheel steering angle increment, is the final rear wheel steering angle increment, K1 and K2 are feedback gains, is the inverse of K2, E is the coefficient matrix, x(k) is the state variable, L2 is the coefficient matrix of the front wheel steering angle increment, L3 is the coefficient matrix of the rear wheel steering angle increment, is the coefficient matrix of the prediction equation, L1 is the coefficient matrix of the direct yaw moment, θ1 is the variable, and Z is the coefficient matrix.

[0163] Based on the same inventive concept as the AFWS / DYC lateral stability coordinated control method considering driving style, this embodiment also provides a corresponding AFWS / DYC lateral stability coordinated control device considering driving style. Figure 8 The structural block diagram of the AFWS / DYC lateral stability coordination control device considering the driving style is shown, including:

[0164] A model building module 81 is used to build an online driving style identification model;

[0165] A characteristic index acquisition module 82 is used to acquire the driver's online lane change data and extract characteristic indicators;

[0166] An identification module 83, used to input the characteristic index into the online driving style identification model to obtain the driving style of the driver;

[0167] The reference yaw rate acquisition module 84 is used to determine the vehicle stability factor according to the driver's driving style; determine the driving style gain coefficient according to the vehicle stability factor; and obtain the reference yaw rate according to the driving style gain coefficient;

[0168] An instability index determination module 85 is used to determine an instability index according to a reference yaw rate and an ideal center of mass sideslip angle;

[0169] The control amount acquisition module 86 is used to perform cooperative game and non-cooperative game based on the reference yaw rate, the ideal center of mass sideslip angle and the instability index to obtain the preliminary front wheel angle increment, the preliminary rear wheel angle increment and the final direct yaw moment; and use the final direct yaw moment to affect the preliminary front wheel angle increment and the preliminary rear wheel angle increment to obtain the final front wheel angle increment and the final rear wheel angle increment;

[0170] The control module 87 is used to apply the final front wheel steering angle increment and the final rear wheel steering angle increment to the steering wheel, and convert the final direct yaw moment into tire longitudinal force and distribute it to each wheel.

[0171] The AFWS / DYC lateral stability coordination control device considering driving style of this embodiment has the same inventive concept as the AFWS / DYC lateral stability coordination control method considering driving style mentioned above. Therefore, the specific implementation method of the device can be seen in the implementation example part of the AFWS / DYC lateral stability coordination control method considering driving style mentioned above, and its technical effect corresponds to the technical effect of the above method, which will not be repeated here.

[0172] An embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned AFWS / DYC lateral stability coordinated control method considering driving style is implemented.

[0173] The above are only various implementations of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An AFWS / DYC lateral stability coordinated control method considering driving style, characterized in that: include: Construct an online driving style identification model; Obtain the driver's online lane-changing data and extract characteristic indicators; Inputting the characteristic index into the driving style online identification model to obtain the driver's driving style; determining a vehicle stability factor based on the driver's driving style; determining a driving style gain coefficient according to the vehicle stability factor; obtaining a reference yaw angular velocity according to the driving style gain coefficient; Determining an instability degree index according to the reference yaw angular velocity and the ideal center of mass sideslip angle; Based on the reference yaw rate, the ideal center of mass sideslip angle and the instability index, cooperative game and non-cooperative game are performed to obtain a preliminary front wheel turning angle increment, a preliminary rear wheel turning angle increment and a final direct yaw moment; the final direct yaw moment is used to influence the preliminary front wheel turning angle increment and the preliminary rear wheel turning angle increment to obtain a final front wheel turning angle increment and a final rear wheel turning angle increment; The final front wheel steering angle increment and the final rear wheel steering angle increment act on the steering wheel, and the final direct yaw moment is converted into tire longitudinal force and distributed to each wheel.

2. The method according to claim 1, characterized in that in, Construct an online driving style identification model, including: The lane changing process data of multiple drivers are obtained, and characteristic indicators are extracted from the lane changing process data of each driver as training samples; Perform cluster analysis on all training samples to determine the driving style category of each training sample; The training samples and the corresponding driving style categories constitute a driving style data set; The GRNN is trained based on the driving style data set to obtain a driving style online identification model.

3. The method according to claim 1, characterized in that in, Obtain the driver's online lane-changing data and extract characteristic indicators, including: Step S21, obtaining the distance change Δdis between the vehicle and the lane line at the current time t0 lane , if Δdis lane >3, then go to step S22, otherwise, repeat step S21 at the next moment; Step S22, calculate the distance dis between the vehicle and the obstacle at time t0-5 front , if 40>dis front >0, then go to step S23, otherwise go to step S21; Step S23, determining the lane change start time and end time, includes: If str sw (t)<10° and str sw (t+0.5)>15°, t represents the time, t∈[t0-5,t0+5], str sw (t) is the steering wheel angle at time t, then the first time t1 = t; If str sw (t)<-15° and str sw (t+0.5)>-10°, then the second moment t2=t; Start time t start =min(t1,t2), end time t end =max(t1,t2); Step S24, extracting the vehicle driving data between the lane changing start time and the lane changing end time, i.e., the driver's online lane changing data; Step S25, extracting characteristic indicators based on the driver's online lane changing data, wherein the characteristic indicators include speed standard deviation, maximum absolute value of lateral acceleration, standard deviation of lateral acceleration, average absolute value of steering wheel angle, and average absolute value of steering wheel angular velocity.

4. The method according to claim 1, characterized in that in, The reference yaw angular velocity is: in, is the reference yaw rate, Gain is the driving style gain coefficient, ω r_d is the ideal yaw rate.

5. The method according to claim 1, characterized in that in, According to the reference yaw rate and the ideal center of mass sideslip angle, an instability index is determined, including: The β-ω phase plane is divided into three regions, namely stable region I, transition region II, and unstable region III; Determining the area to which the vehicle belongs according to the reference yaw rate and the ideal center of mass sideslip angle; According to the area to which the vehicle belongs, the instability index is determined using the following formula: Among them, S ta is the instability index, Δγ r is the deviation between the actual yaw rate and the ideal yaw rate, Δγ max is the maximum deviation between the actual yaw rate and the ideal yaw rate, Δβ r is the deviation between the actual center of mass sideslip angle and the ideal center of mass sideslip angle, Δβ max It is the maximum value of the deviation between the actual sideslip angle and the ideal sideslip angle.

6. The method according to claim 1, characterized in that The final direct yaw moment is: in, is the final direct yaw moment, k is the sampling time, L1 is the coefficient matrix of the direct yaw moment, and θ1 is the variable; for The amount of decomposition, is the weight matrix, T stands for transpose; for The amount of decomposition, is the weight matrix, Ω is the coefficient matrix; is the predicted stability control target; Λ is the coefficient matrix, X(k) is the matrix variable; Υ is the coefficient matrix.

7. The method according to claim 6, characterized in that Weight Matrix for: r1＝10 -(5+4LA) Among them, r1 is the control output weight of the direct yaw moment, LA is the parameter, S ta is an index of instability degree, and N represents the prediction time domain.

8. The method according to claim 1, characterized in that The final front wheel turning angle increment and the final rear wheel turning angle increment are: in, is the final front wheel steering angle increment, is the final rear wheel steering angle increment, K1 and K2 are feedback gains, is the inverse of K2, E is the coefficient matrix, x(k) is the state variable, L2 is the coefficient matrix of the front wheel steering angle increment, L3 is the coefficient matrix of the rear wheel steering angle increment, is the coefficient matrix of the prediction equation, LK1 is the coefficient matrix of the direct yaw moment, θ1 is the variable, and Z is the coefficient matrix.

9. An AFWS / DYC lateral stability coordinated control device considering driving style, characterized in that: include: Model building module, used to build an online driving style identification model; A characteristic index acquisition module is used to obtain the driver's online lane change data and extract characteristic indicators; An identification module, used for inputting the characteristic index into the driving style online identification model to obtain the driving style of the driver; A reference yaw rate acquisition module is used to determine a vehicle stability factor according to the driving style of the driver; determining a driving style gain coefficient according to the vehicle stability factor; obtaining a reference yaw angular velocity according to the driving style gain coefficient; An instability index determination module, used to determine an instability index according to the reference yaw angular velocity and the ideal center of mass sideslip angle; a control amount acquisition module, configured to perform cooperative game and non-cooperative game based on the reference yaw rate, the ideal center of mass sideslip angle and the instability index to obtain a preliminary front wheel turning angle increment, a preliminary rear wheel turning angle increment and a final direct yaw moment; and utilize the final direct yaw moment to influence the preliminary front wheel turning angle increment and the preliminary rear wheel turning angle increment to obtain a final front wheel turning angle increment and a final rear wheel turning angle increment; The control module is used for applying the final front wheel steering angle increment and the final rear wheel steering angle increment to the steering wheel, and converting the final direct yaw moment into tire longitudinal force and distributing it to each wheel.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the AFWS / DYC lateral stability coordinated control method considering driving style as described in any one of claims 1-8 is implemented.

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