1. The automatic driving automobile complex environment model based on the complex network is characterized in that a motion subject is used as a node, and a time-varying complex dynamic network G is constructed to be used as a complex environment model:

G＝(V,B,X,P,Θ) (3)

g is a time-varying complex dynamic network, V is a node set in the time-varying complex dynamic network G, B is a set of edges in the time-varying complex dynamic network G and represents a connecting line between nodes, X is a state vector of the nodes in the time-varying complex dynamic network G, P is an intensity function of the edges in the complex dynamic network G and represents a coupling relation between the nodes, and theta is a region function of the time-varying complex dynamic network G and represents dynamic constraint on the time-varying complex dynamic network G;

the time-varying complex dynamic network G is equivalent to a continuous time dynamic system with N nodes, and the state variable of the ith node is x_iThen, the kinetic equation of the ith node is:

wherein, f (x)_i) Is an independent function of the state variable of the ith node, xi>0 is the strength coefficient of the common connection relation, p_ij(t) is a coupling coefficient between the ith node and the jth node, H (x)_j) Is an inline function between nodes, which is a function of the driving style and the distance between the nodes;

let X be ═ X₁,x₂,…,x_N]^T，F(X)＝[f(x₁),f(x₂),…,f(x_N)]^T，P(t)＝[(p_ij(t))]∈R^N×N,H(X)＝[H(x₁),H(x₂),…,H(x_N)]^TSection of time-varying complex dynamic network GThe point system kinetic equation is:

wherein, X is a state vector of a node in the time-varying complex dynamic network G, f (X) is a dynamic equation vector of the node in the time-varying complex dynamic network G, p (t) is a coupling matrix between the nodes in the time-varying complex dynamic network G, and h (X) is an inline vector of the node in the time-varying complex dynamic network G;

in the complex environment model, along with the movement of the nodes and the change of the environment, the positions and the states of the nodes are in dynamic change, the nodes are imported into and flow out of the network, the coupling relation among the nodes and the network area function are changed along with the change of the positions and the states of the nodes, and a complex network system is continuously evolved and developed along with time.

2. The cognitive system of the automatic driving automobile based on the complex network is characterized by comprising the following components: the system comprises a driving style identification module, a complex environment model module, a node differentiation cognition module, a layering cognition module and a global risk situation cognition module;

the driving style identification module constructs a driving style characteristic matrix C on the basis of extracting the driving characteristic parameters_JA driving style feature matrix C_JInput random forest classifier R_fBy means of a random forest classifier R_fOutputting a driving style category K_drive；

The complex environment model module is the complex environment model of claim 1;

the node differentiation cognition module utilizes the quantity g of nodes in a complex environment model_iDegree k of_iAnd point right s_iExpressing the difference of the network nodes by four parameters of the importance I (i), and performing difference analysis on all the nodes by using a normal distribution diagram;

the hierarchical cognition module is used for hierarchically dividing nodes in the complex environment model by adopting a condensation algorithm so as to realize hierarchical and stepped cognition on the complex environment of the automatic driving automobile;

the global risk situation cognition module measures the disorder degree of the complex environment model by using the system entropy and entropy change, describes the overall risk and change situation and realizes global commonality state cognition.

3. The cognitive system of an autonomous vehicle as in claim 2, wherein the driving characteristic parameters comprise a longitudinal driving characteristic parameter, a lateral driving characteristic parameter and a mode transition characteristic parameter. The longitudinal driving characteristic parameter refers to longitudinal acceleration a in a finite time window₊Distance between heel and relaxation d_timeThe transverse driving characteristic parameter refers to a transverse acceleration root mean square RMS (a) in a finite time window_-) The standard deviation SD (r) of the yaw rate, and the left lane change state transition probability P (l) in the limited time window of the mode transition characteristic parameter_c) And right lane change state transition probability P (r)_c)。

4. The cognitive system of an autonomous vehicle as claimed in claim 2, wherein said driving style characteristic matrix C_JThe method is a three-dimensional six-degree-of-freedom characteristic matrix formed by longitudinal driving characteristic parameters, transverse driving characteristic parameters and mode transfer characteristic parameters:

5. the autonomous vehicle learning system of claim 2 wherein the random forest classifier R_fThe method comprises the following steps: randomly sampling an original training set formed by driving style data in a put-back mode to generate m training sets, selecting n features for each training set, respectively training m decision tree classification models, selecting the best sample feature for each classification model according to an information gain ratio to split until all training samples belong to the same class, and finally dividing all generated decision trees into classesForming random forest by the class model, and outputting driving style class K by voting method_dri；

The driving style class K_driveThe method comprises three types of an aggressive type, a mild type and a conservative type:

K_drive＝R_f(C_J) (2)。

6. the autodrive vehicle awareness system of claim 2, wherein the quantity g of nodes_iExpressed by the structural size of the ith node;

degree k of the node_iExpressed by the number of nodes directly connected to the ith node;

point weight s of the node_iRepresenting the edge weight sum of all adjacent edges of the ith node;

importance of the node i (i):

I(i)＝K(i)+∑_jp_ij(t) (6)

(6) in the formula, p_ij(t) is the coupling coefficient between nodes, K (i) is the centricity factor of the ith node:

(7) in the formula (I), the compound is shown in the specification,<k>＝∑k_ithe/N represents the average degree of the module;representing the average unit weight of the module.

7. The cognitive system of an autonomous vehicle as claimed in claim 2, wherein the hierarchical cognitive module comprises an inner layer module composed of nodes coupled with a central node and the central node, wherein the central node is the autonomous vehicle; secondly, sorting the importance of the non-central nodes of the intermediate layer module, and sequentially searching the points with the maximum coupling coefficient to form the intermediate layer module; then, sorting the importance of the nodes of the middle layer module, and sequentially searching the points with the maximum coupling coefficient to form an outer layer module; and finally, forming an edge layer module by other nodes.

8. The autodrive vehicle awareness system of claim 2 wherein the global risk situation awareness module wherein the system entropy is designed to:

S＝V_n/Θ+D(P)+D(U) (8)

wherein, V_nThe number of the nodes of the complex environment model is theta, the network area of the complex environment model is theta, D (P) represents the variance of the coupling coefficient, and D (U) represents the variance of the node speed in the complex environment model;

the entropy change is designed as:

wherein d represents the calculation of the differential of the corresponding variable, indicating the trend of the variation thereof.

9. The cognitive method of the automatic driving automobile cognitive system based on the complex network is characterized by comprising the following steps of:

step 1) extracting longitudinal driving characteristic parameters, transverse driving characteristic parameters and mode transfer characteristic parameters, and constructing a driving style characteristic matrix C_JGenerating a random forest classifier R_fA driving style feature matrix C_JInput random forest classifier R_fRandom forest classifier R_fOutput driving style category K_driveIdentifying the driving style as an aggressive type, a flat type and a conservative type;

step 2), constructing a time-varying complex dynamic network G as a complex environment model for describing overall associated features of the complex environment, further establishing a node dynamic equation in the complex environment model, combining features of all nodes in the time-varying complex dynamic network G to form a dynamic equation vector F (X), a coupling matrix P (t) among the nodes in the time-varying complex dynamic network G and an inline vector H (X) of the nodes, and establishing a node system dynamic equation of the time-varying complex dynamic network G for describing dynamic characteristics of the complex environment;

step 3) constructing the quantity g of nodes in the complex environment model_iDegree k of_iAnd point right s_iAnd four parameters of importance degree I (i), and carrying out differentiation analysis on the nodes by using a normal distribution diagram to realize node differentiation cognition;

step 4) carrying out hierarchical division on the nodes in the complex environment model by adopting a condensation algorithm to realize hierarchical and stepped cognition on the complex environment of the automatic driving automobile;

and 5) measuring the disorder degree of the complex environment model by using the system entropy and entropy change according to the basic idea of the entropy theory, describing the overall risk and change situation, and realizing the state cognition of the global commonality.

10. The cognitive method according to claim 9, wherein the complex network based autonomous vehicle cognitive system is the cognitive system of any one of claims 2-8.

Background

Complex networks are networks that exhibit a high degree of complexity, are abstractions of complex systems, and generally have some or all of the properties of self-organization, self-similarity, attractors, worlds, and unscaled. The characteristic of a complex network is complexity, which is expressed in: the network has large scale, complex connection structure, complex node complexity (such as node dynamics complexity and node diversity), complex network time-space evolution process, sparsity of network connection, fusion of various repeated sundries and the like. The complexity research method of the complex network comprises the following steps: research methods such as node complexity, connection structure complexity and network space-time evolution process complexity become important tools for complex system modeling and research.

The automatic driving automobile is a comprehensive system integrating the functions of environmental perception, planning decision, control execution and the like. Due to the rapid development of sensor technologies such as laser radar, millimeter wave radar and cameras, the environmental perception method is deeply researched and greatly developed. At present, the establishment of the association between the individual type, position, movement and other bottom layer perception information of the environment and the individual behavior style, the hierarchical local environment and the global environment cognition supports the development from the environment perception to the individual cognition, the local cognition to the traffic comprehensive situation global cognition, and becomes an important precondition for ensuring the safety of autonomous decision making and movement planning of the automatic driving automobile. However, the environment faced by an autonomous vehicle is a complex system in which the individual's motor behavior is not only dependent on the individual itself, but is also influenced by the surrounding other individual's motor behavior and driving environment, with complex multidimensional coupling and dynamic uncertainty. Therefore, based on a complex network, a complex environment model, a cognitive method and a device of the automatic driving automobile are established, and a nonlinear dynamic evolution rule of the environment of the automatic driving automobile is revealed, so that the method becomes an important link for solving the cognitive problem of the high-level automatic driving environment.

Disclosure of Invention

In order to solve the technical problems, the invention provides a complex environment model, a cognitive system and a cognitive method of an automatic driving automobile based on a complex network, on the basis of perceiving the external environment of the automatic driving automobile, firstly, aiming at the complexity problem of individual driving behavior cognition, driving style recognition is carried out according to driving characteristic parameters for expressing driving control radical degree and mode transfer preference; secondly, establishing a time-varying complex dynamic network as an automatic driving automobile complex environment model based on a complex network, with the motion subject as a node and a road as a constraint on the basis of the driving style identification according to the group behavior characteristics of the motion subject in the environment; and finally, carrying out parameterization expression on the nodes in the complex environment model to realize node differentiation cognition of the complex environment, layering the nodes in the complex environment model by adopting a condensation algorithm to realize layering cognition of the complex environment, establishing a disorder degree measurement method of the complex environment model, and realizing global risk situation cognition of the complex environment.

The cognitive system of the automatic driving automobile based on the complex network comprises: the system comprises a driving style identification module, a complex environment model module, a node differentiation cognition module, a layering cognition module and a global risk situation cognition module.

The driving style identification module constructs a driving style characteristic matrix C on the basis of extracting the driving characteristic parameters_JA driving style feature matrix C_JInput random forest classifier R_fBy means of a random forest classifier R_fOutputting a driving style category K_drive。

The driving characteristic parameters comprise longitudinal driving characteristic parameters, transverse driving characteristic parameters and mode transfer characteristic parameters. The longitudinal driving characteristic parameter refers to longitudinal acceleration a in a finite time window₊Distance between heel and relaxation d_timeThe transverse driving characteristic parameter refers to a transverse acceleration root mean square RMS (a) in a finite time window_-) The standard deviation SD (r) of the yaw rate, and the left lane change state transition probability P (l) in the limited time window of the mode transition characteristic parameter_c) And right lane change state transition probability P (r)_c)。

The driving style characteristic matrix C_JBy longitudinal drivingA three-dimensional six-degree-of-freedom characteristic matrix formed by the characteristic parameters, the transverse driving characteristic parameters and the mode transfer characteristic parameters:

the random forest classifier R_fThe method comprises the following steps: the method comprises the steps of performing replacement random sampling on an original training set consisting of driving style data to generate m training sets, selecting n features for each training set, respectively training m decision tree classification models, selecting the best sample feature for each classification model according to an information gain ratio to split until all training samples belong to the same class, finally forming a random forest by all the generated decision tree classification models, and outputting a driving style class K through a voting method_drive。

The driving style class K_driveThe method comprises three types of an aggressive type, a mild type and a conservative type:

K_drive＝R_f(C_J) (2)

the complex environment model module is used for depicting the random, dynamic and nonlinear evolution rules of the complex environment of the automatic driving automobile, based on the complex network theory, taking a motion main body as a node, and constructing a time-varying complex dynamic network G as a complex environment model:

G＝(V，B，X，P，Θ) (3)

g is a time-varying complex dynamic network, V is a node set in the time-varying complex dynamic network G, B is a set of edges in the time-varying complex dynamic network G and represents a connecting line between nodes, X is a state vector of the nodes in the time-varying complex dynamic network G, P is an intensity function of the edges in the complex dynamic network G and represents a coupling relation between the nodes, and theta is a region function of the time-varying complex dynamic network G and represents dynamic constraint on the time-varying complex dynamic network G.

The time-varying complex dynamic network G is equivalent to a continuous time dynamic system with N nodes, and the state variable of the ith node is x_iThen, the kinetic equation of the ith node is:

wherein, f (x)_i) Is the independent function of the ith node state variable, xi > 0 is the strength coefficient of the common connection relation, p_ij(t) is a coupling coefficient between the ith node and the jth node, H (x)_j) Is an inline function between nodes, which is a function of driving style and node distance.

Let X be ═ X₁，x₂，…，x_N]^T，F(X)＝[f(x₁)，f(x₂)，…，f(x_N)]^T，P(t)＝[(p_ij(t))]∈R^N×N，H(X)＝[H(x₁)，H(x₂)，…，H(x_N)]^TThen, the node system dynamic equation of the time-varying complex dynamic network G is:

wherein, X is a state vector of a node in the time-varying complex dynamic network G, f (X) is a dynamic equation vector of a node in the time-varying complex dynamic network G, p (t) is a coupling matrix between nodes in the time-varying complex dynamic network G, and h (X) is an inline vector of a node in the time-varying complex dynamic network G.

In the complex environment model, along with the movement of nodes and the change of environment, the positions and the states of the nodes are in dynamic change, the nodes are imported into and flow out of the network, the coupling relation among the nodes and the network area function are changed along with the change of the positions and the states of the nodes, and the complex network system is continuously evolved and developed along with time.

The node differentiation cognition module is the quantity g of nodes in a complex environment model_iDegree k of_iAnd point right s_iAnd the importance I (i) expresses the difference of the network nodes by four parameters, and the difference of all the nodes is analyzed by a normal distribution diagram.

Amount of said node g_iAnd is expressed by the structural size of the ith node.

Degree k of the node_iIt is expressed by the number of nodes directly connected to the ith node.

Point weight s of the node_iAnd represents the edge weight sum of all adjacent edges of the ith node.

Importance of the node i (i):

I(i)＝K(i)+∑_jp_ij(t) (6)

(6) in the formula, p_ij(t) is the coupling coefficient between nodes, K (i) is the centricity factor of the ith node:

(7) in the formula (I), the compound is shown in the specification,<k>＝∑k_ithe/N represents the average degree of the module;representing the average unit weight of the module.

The hierarchical cognition module is used for hierarchically dividing nodes in the complex environment model by adopting a condensation algorithm to realize hierarchical and stepped cognition of the complex environment of the automatic driving automobile, and the operation steps are as follows:

the method comprises the following steps that firstly, an automatic driving automobile is used as a central node, and nodes which are in coupling relation with the central node and the central node form an inner layer module;

secondly, sorting the importance of the non-central nodes of the intermediate layer module, and sequentially searching the points with the maximum coupling coefficient to form the intermediate layer module;

thirdly, sorting the importance of the nodes of the middle layer module, and sequentially searching the points with the maximum coupling coefficient to form an outer layer module;

fourthly, other nodes form an edge layer module.

The global risk situation cognition module measures the disorder degree of the complex environment model by using the system entropy and entropy change according to the basic idea of the entropy theory, describes the overall risk and change situation, and realizes global commonality state cognition.

The system entropy is as follows:

S＝V_n/Θ+D(P)+D(U) (8)

wherein, V_nThe number of nodes of the complex environment model is theta, the network area of the complex environment model is theta, D (P) represents the variance of the coupling coefficient, and D (U) represents the variance of the node speed in the complex environment model.

The entropy change is as follows:

wherein d represents the calculation of the differential of the corresponding variable, indicating the trend of the variation thereof.

According to the cognitive system of the automatic driving automobile based on the complex network, the cognitive method of the automatic driving automobile provided by the invention comprises the following steps:

step 1) extracting longitudinal driving characteristic parameters, transverse driving characteristic parameters and mode transfer characteristic parameters, and constructing a driving style characteristic matrix C_JGenerating a random forest classifier R_fA driving style feature matrix C_JInput random forest classifier R_fRandom forest classifier R_fOutput driving style category K_driveIdentifying the driving style as an aggressive type, a flat type and a conservative type;

step 2), constructing a time-varying complex dynamic network G as a complex environment model for describing overall associated features of the complex environment, further establishing a node dynamic equation in the complex environment model, combining features of all nodes in the time-varying complex dynamic network G to form a dynamic equation vector F (X), a coupling matrix P (t) among the nodes in the time-varying complex dynamic network G and an inline vector H (X) of the nodes, and establishing a node system dynamic equation of the time-varying complex dynamic network G for describing dynamic characteristics of the complex environment;

step 3) constructing the quantity g of nodes in the complex environment model_iDegree k of_iAnd point right s_iAnd four parameters of importance degree I (i), and performing differential analysis on the nodes by using a normal distribution diagramNode differentiation cognition is performed;

step 4) carrying out hierarchical division on the nodes in the complex environment model by adopting a condensation algorithm to realize hierarchical and stepped cognition on the complex environment of the automatic driving automobile;

and 5) measuring the disorder degree of the complex environment model by using the system entropy and entropy change according to the basic idea of the entropy theory, describing the overall risk and change situation, and realizing the state cognition of the global commonality.

On the basis of sensing the external environment of the automatic driving automobile, firstly, aiming at the complexity problem of individual driving behavior cognition, the driving style is identified according to the driving characteristic parameters representing the driving control acceleration degree and the mode transfer preference; secondly, constructing a time-varying complex dynamic network G as an automatic driving automobile complex environment model based on a complex network, taking the motion subject as a node and taking a road as a constraint according to the group behavior characteristics of the motion subject in the complex environment and on the basis of driving style identification; and finally, carrying out parameterization expression on the nodes in the complex environment model to realize node differentiation cognition of the complex environment, layering the nodes in the complex environment model by adopting a condensation algorithm to realize layering cognition of the complex environment, establishing a disorder degree measuring method of the complex environment model, and realizing global risk situation cognition of the complex environment, thereby establishing a complex environment model, a cognitive method and a device of the automatic driving automobile based on the complex network, and laying a good foundation for the design of safe driving and control strategies of the automatic driving automobile.

The invention has the beneficial effects that:

1. the invention establishes a driving style identification method, and constructs a driving style characteristic matrix C on the basis of extracting driving characteristic parameters_jA driving style feature matrix C_JInput random forest classifier R_fRandom forest classifier Rf output Driving Style class K_driveRealizing the driving style identification;

2. based on a complex network theory, a motion body is taken as a node, a time-varying complex dynamic network G is constructed as a complex environment model, the random, dynamic and nonlinear evolution rules of the complex environment of the automatic driving automobile are described, a node system dynamic equation of the time-varying complex dynamic network G is established, and the dynamic characteristics of the complex environment are described;

3. the invention constructs the quantity g of nodes in a complex environment model_iDegree k of_iAnd point right s_iAnd four parameters of importance I (i), and carrying out differentiation analysis on the nodes by using a normal distribution diagram to realize node differentiation cognition on the complex environment of the automatic driving automobile;

4. according to the method, the nodes in the complex environment model are hierarchically divided by adopting a condensation algorithm on the basis of the node coupling relationship, so that hierarchical and stepped cognition on the complex environment of the automatic driving automobile is realized;

5. the method constructs the system entropy and entropy change of the complex environment model of the automatic driving automobile to measure the disorder degree of the complex environment model, describes the overall risk and change situation, and realizes the state cognition of the overall commonality of the complex environment of the automatic driving automobile.

Drawings

Fig. 1 is a flow chart of a driving style recognition module structure.

FIG. 2 is a flow chart of a complex environment model module structure of an autonomous driving vehicle.

Fig. 3 is a structural diagram of a node differentiation cognition module.

FIG. 4 is a flow diagram of a hierarchical cognitive module architecture.

FIG. 5 is a block diagram of a global risk situation awareness module.

FIG. 6 is a schematic structural diagram of an automatic driving automobile cognitive system based on a complex network.

Detailed Description

The invention will be further explained with reference to the drawings.

As shown in fig. 1, it is a structural flow of a driving style recognition module, and first, longitudinal driving characteristic parameters, transverse driving characteristic parameters and mode transfer characteristic parameters are extracted, where the longitudinal driving characteristic parameters refer to longitudinal acceleration a in a finite time window₊Distance between heel and relaxation d_timeThe transverse driving characteristic parameter refers to a transverse direction in a finite time windowRoot mean square RMS (a) of acceleration_{_}) The standard deviation SD (r) of the yaw rate, and the left lane change state transition probability P (l) in the limited time window of the mode transition characteristic parameter_c) And right lane change state transition probability P (r)_c) (ii) a Next, a driving style characteristic matrix C is constructed_JThe driving style characteristic matrix C_JThe method is a three-dimensional six-degree-of-freedom characteristic matrix formed by longitudinal driving characteristic parameters, transverse driving characteristic parameters and mode transfer characteristic parameters; then, the driving style characteristic matrix C_JInput random forest classifier R_fOutput driving style class K_driveSaid driving style category K_driveThe driving style recognition is realized by three types including an aggressive type, a flat type and a conservative type.

As shown in fig. 2, in the structure flow of the complex environment model module of the automatic driving vehicle, first, a time-varying complex dynamic network G is constructed as the complex environment model: g ═ G (V, B, X, P, Θ); then, the time-varying complex dynamic network G is equivalent to a continuous time dynamic system with N nodes, and a dynamic equation of the nodes is established:

then, according to the dynamic equation of the nodes, establishing a node system dynamic equation:and finally, inputting a node system kinetic equation into the complex environment model for describing the dynamic characteristics of the complex environment.

As shown in FIG. 3, the node differentiation cognition module structure jointly uses the quantity g of the nodes in the complex environment model_iDegree k of_iAnd point right s_iAnd the importance I (i) are used for expressing the difference of the network nodes, and the normal distribution graph is used for carrying out differential analysis on all the nodes so as to realize differential cognition on the nodes.

As shown in fig. 4, in the hierarchical cognition module structure flow, a coacervation algorithm is adopted to perform hierarchical division on nodes in the complex environment model, and the nodes in the second environment model are sequentially divided and respectively form an inner layer module, a middle layer module, an outer layer module and an edge layer module, so that hierarchical cognition on the complex environment is realized.

As shown in fig. 5, the global risk situation awareness module structure jointly uses the system entropy: s ═ V_n/[ theta ] + D (P) + D (U) and entropy change:and measuring the disorder degree of the complex environment model, describing the overall risk and change situation, and realizing the state cognition of the overall commonality of the complex environment.

As shown in fig. 6, the cognitive system of the autonomous vehicle based on the complex network includes a driving style recognition module, a complex environment model module, a node differentiation cognitive module, a layering cognitive module, and a global risk situation cognitive module. The driving style recognition module inputs the recognized node driving style into the complex environment model module for constructing an inline function H (x) between nodes_j) (ii) a The node differentiation cognition module, the layering cognition module and the global risk situation cognition module receive data of V, B, X, P and theta parameters in the complex environment model module and respectively realize node differentiation cognition, layering cognition and global risk situation cognition.

A cognitive method of an automatic driving automobile based on a complex network comprises the following steps:

step 1) extracting longitudinal driving characteristic parameters, transverse driving characteristic parameters and mode transfer characteristic parameters, and constructing a driving style characteristic matrix C_JGenerating a random forest classifier R_fA driving style feature matrix C_JInput random forest classifier R_fRandom forest classifier R_fOutput driving style category K_driveThe driving style is identified as an aggressive type, a flat type and a conservative type, and the specific steps are as follows:

(A) extracting longitudinal driving characteristic parameters, transverse driving characteristic parameters and mode transfer characteristic parameters;

(B) constructing a driving style feature matrix C_J；

(C) Generating random forest classifier R_f；

(D) Driving style characteristic matrix C_JInput random forest classifier R_fRandom forest classifier R_fOutput driving style category K_driveIdentifying the driving style as an aggressive type, a flat type and a conservative type;

step 2), constructing a time-varying complex dynamic network G as a complex environment model for describing overall associated features of the complex environment, further establishing a node dynamic equation in the complex environment model, combining features of all nodes in the time-varying complex dynamic network G to form a dynamic equation vector F (X), a coupling matrix P (t) among the nodes in the time-varying complex dynamic network G and an inline vector H (X) of the nodes, establishing a node system dynamic equation of the time-varying complex dynamic network G for describing dynamic characteristics of the complex environment, and specifically comprising the following steps:

(A) constructing a time-varying complex dynamic network G as a complex environment model;

(B) establishing a node dynamic equation in the complex environment model based on parameters in the complex environment model;

(C) based on a node dynamic equation, establishing a node system dynamic equation of the time-varying complex dynamic network G, wherein the node system dynamic equation is used for describing the dynamic characteristics of the complex environment;

step 3) constructing the quantity g of nodes in the complex environment model_iDegree k of_iAnd point right s_iAnd four parameters of importance I (i), and performing differentiation analysis on all nodes by using a normal distribution graph to realize node differentiation cognition, and the method comprises the following specific steps:

(A) quantity g of nodes in the construction of complex environmental model_iDegree k of_iAnd point right s_iAnd importance i (i) four parameters;

(B) respectively describing all nodes in the complex environment model by using the four parameters;

(C) carrying out differentiation analysis on all nodes by using a normal distribution map to realize differentiation cognition of the nodes;

and 4) carrying out hierarchical division on the nodes in the complex environment model by adopting a condensation algorithm to realize hierarchical and stepped cognition on the complex environment of the automatic driving automobile, and specifically comprising the following steps:

(A) an inner layer module is formed by nodes which take the automatic driving automobile as a central node and have a coupling relation with the central node and the central node;

(B) sorting the importance of the non-central nodes of the inner layer module, and sequentially searching the points with the maximum coupling coefficient to form the middle layer module;

(C) sorting the importance of the nodes of the middle layer module, and sequentially searching the points with the maximum coupling coefficient to form an outer layer module;

(D) other nodes form an edge layer module;

step 5) according to the basic idea of the entropy theory, measuring the disorder degree of the complex environment model by using the system entropy and the entropy change, describing the overall risk and the change situation, and realizing the state cognition of the global commonality, wherein the specific steps are as follows:

(A) using the system entropy: s ═ V_nMeasuring the disorder degree of the complex environment model, and describing the overall risk of the complex environment;

(B) using entropy change: d (V)_nMeasuring the disorder degree of the complex environment model by the aid of/theta) + d (D (P)) + d (D (U)), describing the change situation of the overall risk of the complex environment, and realizing state cognition of global commonality.

The specific embodiment of the invention: writing a driving style recognition module by using Python, and constructing a driving style feature matrix C based on a Sciket-leam third-party machine learning library_JGenerating a random forest classifier R_fRealizing the driving style identification; writing a mathematical model by using MATLAB/Simulink to form a complex environment model module; a node differentiation cognition module, a layering cognition module and a global risk situation cognition module are compiled by using Python, and a differentiation, layering and global risk situation cognition method of the complex environment of the automatic driving automobile is realized in a PyTorch frame; MATLAB, Scikit-spare and PyTorch interfaces are compiled based on a Ubuntu system, and are installed and configured in an industrial control computer, so that a complex network-based complex environment model, a cognitive method and a device of an automatic driving automobile are realized.

The above-listed series of detailed descriptions are merely specific illustrations of possible embodiments of the present invention, and they are not intended to limit the scope of the present invention, and all equivalent means or modifications that do not depart from the technical spirit of the present invention are intended to be included within the scope of the present invention.