阅读说明：本技术 一种基于深度学习的个性化车道保持辅助方法及装置 (Personalized lane keeping auxiliary method and device based on deep learning ) 是由 孙棣华 赵敏 袁尔会 于 2021-06-08 设计创作，主要内容包括：本发明公开了一种基于深度学习的个性化车道保持辅助方法,包括：获取驾驶人驾驶过程中的车辆运动状态信息、道路环境信息作为轨迹模型的参数表征；基于深度卷积模糊系统和轨迹模型的参数,建立轨迹模型；对所述轨迹模型进行优化,根据驾驶人的驾驶特性设定约束条件；获取时实车辆运动状态信息和实时道路环境信息,并基于轨迹模型输出驾驶员的横向位置信息,控制车辆对横向位置进行跟踪。本发明利用深度学习这种端到端的方法,学习驾驶人在轨迹上的驾驶习性,实现个性化的车道保持驾驶辅助,能够提高驾驶人对车道保持系统的接受度,降低人机冲突,改善驾驶人的舒适性,也有利于减轻辅助系统规划层的负荷,保证驾驶安全,进一步提高行驶效率。(The invention discloses a personalized lane keeping auxiliary method based on deep learning, which comprises the following steps: acquiring vehicle motion state information and road environment information in the driving process of a driver as parameter representation of a track model; establishing a track model based on parameters of a depth convolution fuzzy system and the track model; optimizing the track model, and setting constraint conditions according to the driving characteristics of a driver; and acquiring real-time vehicle motion state information and real-time road environment information, outputting transverse position information of a driver based on the track model, and controlling the vehicle to track the transverse position. The invention utilizes the deep learning end-to-end method to learn the driving habit of the driver on the track, realizes the individualized lane keeping driving assistance, can improve the acceptance of the driver to the lane keeping system, reduce the man-machine conflict, improve the comfort of the driver, is also beneficial to reducing the load of the planning layer of the auxiliary system, ensures the driving safety and further improves the driving efficiency.)

1. A personalized lane keeping auxiliary method based on deep learning is characterized by comprising the following steps:

acquiring vehicle motion state information and road environment information in the driving process of a driver as parameter representation of a track model;

establishing a track model based on parameters of a depth convolution fuzzy system and the track model;

optimizing the track model, and setting constraint conditions according to the driving characteristics of a driver;

and acquiring real-time vehicle motion state information and real-time road environment information, outputting transverse position information of a driver based on the track model, and controlling the vehicle to track the transverse position.

2. The personalized lane keeping assist method based on deep learning of claim 1, wherein the vehicle motion state information comprises: lateral acceleration ay, lateral velocity vy, yaw rate yawrate, and lateral position lateral_(t-1)The road environment information includes: the road curvature curve.

3. The personalized lane keeping assist method based on deep learning of claim 2, wherein the building of the track model based on the parameters of the deep convolution fuzzy system and the track model comprises:

determining input and output parameters for establishing a track model;

performing rapid training based on a deep convolution fuzzy system and input and output parameters, and establishing an initial track model;

and performing online learning in the driving process of the driver, and updating the initial track model to obtain a track model.

4. The personalized lane keeping auxiliary method based on deep learning of claim 3, wherein the fast training is performed based on the deep convolution fuzzy system and the input and output parameters, and an initial trajectory model is established, which comprises:

(1) setting the window size of the algorithm as m, the step length as s, and the number of fuzzy sets as q;

(2) blurring each of the l layersThe following steps are performed:

in the following expression, Cell represents a unit, i.e. a fuzzy system, such as Cell (0) represents the 1 st fuzzy system unit, and Cell (1) represents the 2 nd fuzzy system unit. j is a function of_i,j₂,L,j_m1,2, L and q refer to the number of fuzzy sets,refers to the input weight parameters of a fuzzy system,refers to the output weight parameter of the fuzzy system,parameters representing fast training of the fuzzy system based on input and output,indicating the ith output of the t-th layer, i.e.Represents the output of layer 0, i.e., the input of layer 1:

first, Cell (x) is initialized⁽¹⁾,x⁽²⁾,L_,x^(m)) Parameter (d) of

Determining an end point of each input;

determining the maximum membership value of each input-output data pair fuzzy set;

updating parameters

Fifthly, determiningA value of (d);

sixthly, determining the neighborhood of the Cell (0) to obtain the Cell (1);

seventhly, obtaining the cells (2), (2) and (i) one by one until a complete fuzzy rule base is generated;

update i → i + 1;

update l → l + 1;

(3) and finishing the rapid training and establishing an initial track model.

5. The personalized lane keeping assist method based on deep learning of claim 4, wherein the constraint condition of the lateral position is:

wherein the latex_(t)Represents the lateral position at time t, average_(t-1)Is the lateral position at time t-1, mu and sigma²The data sets are expected and variance, respectively.

6. A personalized lane keeping aid based on deep learning, the device comprising:

the information acquisition module is used for acquiring vehicle motion state information and road environment information in the driving process of a driver as the parameter representation of the track model;

the model establishing module is used for establishing a track model based on the parameters of the depth convolution fuzzy system and the track model;

the optimization module is used for optimizing the track model and setting constraint conditions according to the driving characteristics of a driver;

and the tracking module is used for acquiring real-time vehicle motion state information and real-time road environment information, outputting transverse position information of a driver based on the track model and controlling the vehicle to track the transverse position.

7. The deep learning based personalized lane keeping aid of claim 6, wherein the vehicle motion state information comprises: lateral acceleration ay, lateral velocity vy, yaw rate yawrate, and lateral position lateral_(t-1)The road environment information includes: the road curvature curve.

8. The apparatus of claim 7, wherein the track model is built based on the parameters of the deep convolution fuzzy system and the track model, and the track model comprises:

determining input and output parameters for establishing a track model;

performing rapid training based on a deep convolution fuzzy system and input and output parameters, and establishing an initial track model;

and performing online learning in the driving process of the driver, and updating the initial track model to obtain a track model.

9. The apparatus for personalized lane keeping aid based on deep learning of claim 8, wherein the deep convolution fuzzy system and input and output parameters are used for fast training, and an initial trajectory model is established, comprising:

(1) setting the window size of the algorithm as m, the step length as s, and the number of fuzzy sets as q;

(2) blurring each of the l layersThe following steps are performed:

in the following expression, Cell represents a unit, i.e. a fuzzy system, such as Cell (0) represents the 1 st fuzzy system unit, and Cell (1) represents the 2 nd fuzzy system unit. j is a function of_i,j₂,L,j_m1,2, L and q refer to the number of fuzzy sets,refers to the input weight parameters of a fuzzy system,refers to the output weight parameter of the fuzzy system,parameters representing fast training of the fuzzy system based on input and output,indicating the ith output of the t-th layer, i.e.Represents the output of layer 0, i.e., the input of layer 1:

first, Cell (x) is initialized⁽¹⁾,x⁽²⁾,L_,x^(m)) Parameter (d) of

Determining an end point of each input;

determining the maximum membership value of each input-output data pair fuzzy set;

updating parameters

Fifthly, determiningA value of (d);

sixthly, determining the neighborhood of the Cell (0) to obtain the Cell (1);

seventhly, obtaining the cells (2), (2) and (i) one by one until a complete fuzzy rule base is generated;

update i → i + 1;

update l → l + 1;

(3) and finishing the rapid training and establishing an initial track model.

10. The personalized lane keeping assist device based on deep learning of claim 9, wherein the constraints on the lateral position are:

wherein the latex_(t)Represents the lateral position at time t, average_(t-1)Is the lateral position at time t-1, mu and sigma²The data sets are expected and variance, respectively.

Technical Field

The invention belongs to the technical field of driving assistance, and particularly relates to a personalized lane keeping assistance method and device based on deep learning.

Background

Studies have shown that most traffic accidents are caused by lateral movement of vehicles, and european regions have studied the initiation of traffic accidents, indicating that if all vehicles are equipped with lane keeping assistance systems, a 12% reduction in traffic accidents is likely. Therefore, the research on the lane keeping auxiliary system of the vehicle is helpful to reduce the occurrence of traffic accidents, and meanwhile, the development process of automobile intellectualization can be promoted.

In the existing studies on the lane keeping assist system, the vehicle is mainly controlled to travel on the center line of the lane. However, the trajectory of a human driver does not always remain on the center line of a lane during actual driving, and different people may have a preference to the left or the right. If the smart car always adopts the uniform design mode of driving along the center line of the lane, on one hand, the driving behavior is greatly different from the expectation of the human driver, and the human driver is inevitably uncomfortable. On the other hand, other human-driven vehicles and pedestrians exist in the traffic, and if the behaviors of the intelligent automobile are too different from those of other human drivers, discomfort and misjudgment of the other vehicle drivers can be caused, so that a dangerous situation can be caused. Therefore, in the process of researching the lane keeping auxiliary method, the personalized lane keeping driving auxiliary method is designed for each driver, and the experience, the acceptance and the satisfaction of the user are improved.

Disclosure of Invention

In view of the above, the present invention provides an individualized lane keeping assisting method and apparatus based on deep learning, which are used to solve at least one defect in the prior art.

The purpose of the invention is realized by the following technical scheme: a personalized lane keeping assist method based on deep learning, the method comprising:

acquiring vehicle motion state information and road environment information in the driving process of a driver as parameter representation of a track model;

establishing a track model based on parameters of a depth convolution fuzzy system and the track model;

optimizing the track model, and setting constraint conditions according to the driving characteristics of a driver;

and acquiring real-time vehicle motion state information and real-time road environment information, outputting transverse position information of a driver based on the track model, and controlling the vehicle to track the transverse position.

Optionally, the vehicle motion state information includes: lateral acceleration ay, lateral velocity vy, yaw rate yawrate, and lateral position lateral_(t-1)The road environment information includes: the road curvature curve.

Optionally, the establishing a trajectory model based on the parameters of the depth convolution fuzzy system and the trajectory model includes:

determining input and output parameters for establishing a track model;

performing rapid training based on a deep convolution fuzzy system and input and output parameters, and establishing an initial track model;

and performing online learning in the driving process of the driver, and updating the initial track model to obtain a track model.

Optionally, the fast training is performed based on the depth convolution fuzzy system and the input and output parameters, and the establishing of the initial trajectory model includes:

(1) setting the window size of the algorithm as m, the step length as s, and the number of fuzzy sets as q;

(2) blurring each of the l layersThe following steps are performed:

in the following expression, Cell represents a unit, i.e. a fuzzy system, such as Cell (0) represents the 1 st fuzzy system unit, and Cell (1) represents the 2 nd fuzzy system unit. j is a function of_i,j₂,L,j_m1,2, L and q refer to the number of fuzzy sets,to a fuzzy systemThe weight parameter is input to the computer system,refers to the output weight parameter of the fuzzy system,parameters representing fast training of the fuzzy system based on input and output,indicating the ith output of the t-th layer, i.e.Represents the output of layer 0, i.e., the input of layer 1:

first, Cell (x) is initialized⁽¹⁾,x⁽²⁾,L,x^(m)) Parameter (d) of

Determining an end point of each input;

determining the maximum membership value of each input-output data pair fuzzy set;

updating parameters

Fifthly, determiningA value of (d);

sixthly, determining the neighborhood of the Cell (0) to obtain the Cell (1);

seventhly, obtaining the cells (2), (2) and (i) one by one until a complete fuzzy rule base is generated;

update i → i + 1;

update l → l + 1;

(3) and finishing the rapid training and establishing an initial track model.

Optionally, the constraint conditions of the lateral position are:

wherein the latex_(t)Represents the lateral position at time t, average_(t-1)Is the lateral position at time t-1, mu and sigma²The data sets are expected and variance, respectively.

The purpose of the invention is realized by the following technical scheme: a personalized lane keeping aid based on deep learning, the device comprising:

the information acquisition module is used for acquiring vehicle motion state information and road environment information in the driving process of a driver as the parameter representation of the track model;

the model establishing module is used for establishing a track model based on the parameters of the depth convolution fuzzy system and the track model;

the optimization module is used for optimizing the track model and setting constraint conditions according to the driving characteristics of a driver;

and the tracking module is used for acquiring real-time vehicle motion state information and real-time road environment information, outputting transverse position information of a driver based on the track model and controlling the vehicle to track the transverse position.

Optionally, the vehicle motion state information includes: lateral acceleration ay, lateral velocity vy, yaw rate yawrate, and lateral position lateral_(t-1)The road environment information includes: the road curvature curve.

Optionally, the establishing a trajectory model based on the parameters of the depth convolution fuzzy system and the trajectory model includes:

determining input and output parameters for establishing a track model;

performing rapid training based on a deep convolution fuzzy system and input and output parameters, and establishing an initial track model;

and performing online learning in the driving process of the driver, and updating the initial track model to obtain a track model.

Optionally, the fast training is performed based on the depth convolution fuzzy system and the input and output parameters, and the establishing of the initial trajectory model includes:

(1) setting the window size of the algorithm as m, the step length as s, and the number of fuzzy sets as q;

(2) blurring each of the l layersThe following steps are performed:

in the following expression, Cell represents a unit, i.e. a fuzzy system, such as Cell (0) represents the 1 st fuzzy system unit, and Cell (1) represents the 2 nd fuzzy system unit. j is a function of_i,j₂,L,j_m1,2, L and q refer to the number of fuzzy sets,refers to the input weight parameters of a fuzzy system,refers to the output weight parameter of the fuzzy system,parameters representing fast training of the fuzzy system based on input and output,indicating the ith output of the t-th layer, i.e.Represents the output of layer 0, i.e., the input of layer 1:

first, Cell (x) is initialized⁽¹⁾,x⁽²⁾,L,x^(m)) Parameter (d) of

Determining an end point of each input;

determining the maximum membership value of each input-output data pair fuzzy set;

updating parameters

Fifthly, determiningA value of (d);

sixthly, determining the neighborhood of the Cell (0) to obtain the Cell (1);

seventhly, obtaining the cells (2), (2) and (i) one by one until a complete fuzzy rule base is generated;

update i → i + 1;

update l → l + 1;

(3) and finishing the rapid training and establishing an initial track model.

Optionally, the constraint conditions of the lateral position are:

wherein the latex_(t)Represents the lateral position at time t, average_(t-1)Is the lateral position at time t-1, mu and sigma²The data sets are expected and variance, respectively.

Due to the adoption of the technical scheme, the invention has the following advantages:

the invention aims at the problems that in the actual driving process, the driving track of a driver on a road does not follow the central line of a lane, but the central line of the lane is taken as the target track by the conventional lane keeping driving auxiliary system, so that the driver feels uncomfortable and the auxiliary system is not trusted, and designs an individualized lane keeping auxiliary strategy based on deep learning. Firstly, determining the road curvature, the vehicle lateral acceleration, the vehicle lateral speed, the yaw rate and the historical lateral position as the parameters of the track representation; then, a human-simulated trajectory plan is researched based on a deep learning system, optimal parameters of model training are set, an off-line trajectory model of a driver is established, the model is updated on line, and scene applicability is improved; and finally, optimizing the track model, and setting constraint conditions on the premise of considering safety and personalized characteristics. The end-to-end method for deep learning is utilized to learn the driving habit of the driver on the track, realize personalized lane keeping driving assistance, improve the acceptance of the driver to a lane keeping system, reduce man-machine conflict, improve the comfort of the driver, be beneficial to reducing the load of a planning layer of the assistance system, ensure the driving safety and further improve the driving efficiency.

Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.

Drawings

The drawings of the present invention are described below.

Fig. 1 is a flowchart of an embodiment of an individualized lane keeping assist method based on deep learning according to the present invention;

FIG. 2 is a distribution diagram of the abnormal values of the lateral position of the driver according to an embodiment of the present invention;

fig. 3 is a schematic diagram of an apparatus for assisting personalized lane keeping based on deep learning according to an embodiment of the present invention.

Detailed Description

Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application. On the contrary, the embodiments of the application include all changes, modifications and equivalents coming within the spirit and terms of the claims appended hereto.

The invention provides an individualized lane keeping auxiliary method based on deep learning, which decomposes lane keeping into two stages of trajectory planning and trajectory tracking control, and importantly learns the decision characteristics of a driver in the trajectory planning stage so as to lay a foundation for realizing individualized lane keeping driving auxiliary. Firstly, collecting vehicle motion state information and road environment information in the driving process of a driver as parameter representations; then bringing the experience and preference of the driver into an intelligent driving environment, establishing a track model based on a deep learning algorithm, and planning a track; then optimizing the track model, and setting constraint conditions according to the driving characteristics of a driver; and finally, accurately tracking and controlling the track by adopting a control algorithm. In order to enhance the generalization ability of the personalized lane keeping assistance, the method is suitable for different working conditions, online learning is carried out in the driving process of a driver, and the track model is continuously updated online. The specific flow is shown in figure 1.

S1: and acquiring vehicle motion state information and road environment information in the driving process of a driver as the parameter representation of the track model.

In the actual driving process, the driving behavior of a driver is influenced by the road environment and the current driving state of the vehicle, and vehicle motion state information and road environment information in the driving process of the driver are collected as parameter representations of a track model to carry out human-simulated track planning;

the method specifically comprises the following steps: in the lane keeping driving assisting process, the vehicle motion state is mainly designed according to lateral motion parameters, namely lateral acceleration ay, lateral speed vy, yaw rate yawrate and historical lateral position laterals_(t-1)The road environment information is mainly considered as the road curvature. In the process of planning the track, a track curve of the transverse position of the vehicle on the lane is generated according to the vehicle motion state information and the road environment information, and the track can be represented as follows:

lateral_(t)＝f(curvature_(t),ay_(t),vy_(t),yawrate_(t),lateral_(t-1))

s2: and establishing a track model based on the parameters of the depth convolution fuzzy system and the track model.

In order to improve the generalization capability of the model and be suitable for different scenes, the driver needs to learn online in the driving process and continuously update and improve the model;

because the Deep Convolutional Fuzzy system has the characteristic of fast training, the Deep learning algorithm in the preferred embodiment is introduced by selecting a Deep Convolutional Fuzzy System (DCFS). The step of establishing the trajectory model comprises the following steps:

s21: determining input and output parameters for establishing a track model;

the road curvature curve, the vehicle lateral acceleration ay, the vehicle lateral speed vy, the road yaw rate yawrate, the vehicle historical lateral position laterals are determined in S1_(t-1)These 5 parameters are used for trajectory learning, i.e. the trajectory model contains 5 inputs and 1 output, which can be expressed as:

(x₁,x₂,x₃,x₄,x₅；y)＝(curvature_(t),ay_(t),vy_(t),yawrate_(t),lateral_(t-1)；lateral_(t))

s22: performing rapid training based on a deep convolution fuzzy system and input and output parameters, and establishing an initial track model;

the training process of the model mainly comprises initialization, fuzzy system updating and updating of each layer, and the algorithm is only trained layer by layer from bottom to top without repeated iteration, so that the training speed is extremely high, online learning can be realized, and the generalization capability is improved. The method specifically comprises the following steps:

(1) changing the number of sliding windows and fuzzy sets of the fuzzy system to realize parameter optimization;

design of sliding window

Setting a window size m, a step length s, a total of l layers of fuzzy systems, and the number of fuzzy sets q, wherein m is 2, s is 1, l is 3, and q is 5 according to specific situations in the present example;

(2) for each layer L ═ 1,2, …, L:

blurring each of the l layersIs performed, in the following formula, Cell represents one Cell, i.e., one modeFuzzy systems, such as Cell (0) for the 1 st fuzzy system Cell and Cell (1) for the 2 nd fuzzy system Cell. j is a function of_i,j₂,L,j_m1,2, L and q refer to the number of fuzzy sets,refers to the input weight parameters of a fuzzy system,refers to the output weight parameter of the fuzzy system,parameters representing fast training of the fuzzy system based on input and output,indicating the ith output of the t-th layer, i.e.Represents the output of layer 0, i.e., the input of layer 1:

first, Cell (x) is initialized⁽¹⁾,x⁽²⁾,L,x^(m)) Parameter (d) of

Determining an end point of each input;

determining the maximum membership value of each input-output data pair fuzzy set;

updating parameters

Fifthly, determiningA value of (d);

sixthly, determining the neighborhood of the Cell (0) to obtain the Cell (1);

seventhly, obtaining the cells (2), (2) and (i) one by one until a complete fuzzy rule base is generated;

update i → i + 1;

update l → l + 1;

(3) completing rapid training, and establishing an initial track model;

s23: and performing online learning in the driving process of the driver, and updating the initial track model to obtain the track model.

S3: and optimizing the track model, and setting constraint conditions according to the driving characteristics of the driver so as to ensure the safety and the personalized characteristics of the driver. The method specifically comprises the following steps:

s31: counting transverse position information of a driver in the driving process, removing abnormal values, and correcting abnormal track points;

for each driver, the driving position can be adjusted according to own driving preference during driving, the lane center line is not always taken as a target driving track, and the driver can consider the driver to be safe within a certain range of the lateral position. In statistics, see FIG. 2, a measurement that deviates more than two standard deviations from the mean in a group of measurements is referred to as abnormalThe value is obtained. In the outlier decision problem, which obeys a Gaussian distribution, for a given data set x⁽¹⁾,x⁽²⁾,…,x^(m)I.e. byWherein, mu and sigma²The estimated value of (d) is expressed as:

after a new sample is given, the probability that the new sample belongs to the group of data is determined according to p (x), which can be expressed as:

wherein x is_jDenotes the jth sample, u_jThe mean value is indicated.

For an abnormal value of the lateral position, the critical range can be expressed as (μ -2 σ, μ +2 σ), and the boundary is the value of p (x), which is a normal value when the value of x is within (μ -2 σ, μ +2 σ), and is an abnormal value otherwise. The number of samples in this range accounts for 95.4% of the total sample size, and can reflect the overall condition of the sample.

S32: setting a constraint condition for trajectory information represented by a lateral position according to driving behavior characteristics of a driver;

when the value of the lateral position output by the track planner at time t is not in the range of (mu-2 sigma, mu +2 sigma), the value of the lateral position average is the value of the last time, that is, the lateral position constraint condition of the track is set as:

wherein the latex_(t)Represents the lateral position at time t, average_(t-1)The lateral position at time t-1.

S4: and acquiring real-time vehicle motion state information and real-time road environment information, outputting transverse position information of a driver based on the track model, and controlling the vehicle to track the transverse position.

According to the vehicle road model, the steering wheel angle can be obtained by the curvature of the motion track of the vehicle, and the steering wheel angle expected by the vehicle is as follows:

wherein L is the wheelbase of the vehicle and is in m, v_yThe unit is the speed of the vehicle, the unit is m/s, d is the distance from the current position of the vehicle to the pre-aiming point, and T is at v_yTime required to travel d distance at speed, e_lThe lateral deviation of the vehicle's intended point.

And then the effective tracking and control of the vehicle are realized according to a related control algorithm, such as a PID algorithm.

As shown in fig. 3, an example of the present application provides a personalized lane keeping aid based on deep learning, which includes:

the information acquisition module is used for acquiring vehicle motion state information and road environment information in the driving process of a driver as the parameter representation of the track model;

the model establishing module is used for establishing a track model based on the parameters of the depth convolution fuzzy system and the track model;

the optimization module is used for optimizing the track model and setting constraint conditions according to the driving characteristics of a driver;

and the tracking module is used for acquiring real-time vehicle motion state information and real-time road environment information, outputting transverse position information of a driver based on the track model and controlling the vehicle to track the transverse position.

In one embodiment, the vehicle motion state information includes: transverse acceleration ay, transverseVelocity vy, yaw rate yawrate and lateral position laterals_(t-1)The road environment information includes: the road curvature curve.

In one embodiment, the building a trajectory model based on the parameters of the depth convolution fuzzy system and the trajectory model includes:

determining input and output parameters for establishing a track model;

performing rapid training based on a deep convolution fuzzy system and input and output parameters, and establishing an initial track model;

and performing online learning in the driving process of the driver, and updating the initial track model to obtain a track model.

In an embodiment, the fast training based on the deep convolution fuzzy system and the input and output parameters to establish the initial trajectory model includes:

(1) setting the window size of the algorithm as m, the step length as s, and the number of fuzzy sets as q;

(2) blurring each of the l layersThe following steps are performed:

in the following expression, Cell represents a unit, i.e. a fuzzy system, such as Cell (0) represents the 1 st fuzzy system unit, and Cell (1) represents the 2 nd fuzzy system unit. j is a function of_i,j₂,L,j_m1,2, L and q refer to the number of fuzzy sets,refers to the input weight parameters of a fuzzy system,refers to the output weight parameter of the fuzzy system,parameters representing fast training of the fuzzy system based on input and output,indicating the ith output of the t-th layer, i.e.Represents the output of layer 0, i.e., the input of layer 1:

first, Cell (x) is initialized⁽¹⁾,x⁽²⁾,L,x^(m)) Parameter (d) of

Determining an end point of each input;

determining the maximum membership value of each input-output data pair fuzzy set;

updating parameters

Fifthly, determiningA value of (d);

sixthly, determining the neighborhood of the Cell (0) to obtain the Cell (1);

seventhly, obtaining the cells (2), (2) and (i) one by one until a complete fuzzy rule base is generated;

update i → i + 1;

update l → l + 1;

(3) and finishing the rapid training and establishing an initial track model.

Optionally, the constraint conditions of the lateral position are:

wherein the latex_(t)Represents the lateral position at time t, average_(t-1)Is the lateral position at time t-1, mu and sigma²The data sets are expected and variance, respectively.

It should be noted that the explanation of the embodiment of the method in the foregoing fig. 1-2 is also applicable to the apparatus proposed in this embodiment, and the implementation principle is similar, and is not repeated here.

It should be noted that, in the description of the present application, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, in the description of the present application, "a plurality" means two or more unless otherwise specified.

Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.

It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.

It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.

In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.

The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.

In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.