WO2022237212A1 - 基于复杂网络的自动驾驶汽车复杂环境模型、认知***及认知方法 - Google Patents

基于复杂网络的自动驾驶汽车复杂环境模型、认知***及认知方法 Download PDF

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WO2022237212A1
WO2022237212A1 PCT/CN2022/070671 CN2022070671W WO2022237212A1 WO 2022237212 A1 WO2022237212 A1 WO 2022237212A1 CN 2022070671 W CN2022070671 W CN 2022070671W WO 2022237212 A1 WO2022237212 A1 WO 2022237212A1
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complex
nodes
node
network
driving
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French (fr)
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蔡英凤
滕成龙
熊晓夏
王海
孙晓东
刘擎超
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江苏大学
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Priority to JP2022553145A priority patent/JP7464236B2/ja
Publication of WO2022237212A1 publication Critical patent/WO2022237212A1/zh

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • B60W40/09—Driving style or behaviour
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/18—Propelling the vehicle
    • B60W30/182—Selecting between different operative modes, e.g. comfort and performance modes
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/107—Longitudinal acceleration
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/08—Interaction between the driver and the control system
    • B60W50/082—Selecting or switching between different modes of propelling
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • B60W2520/105—Longitudinal acceleration
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/30—Driving style

Definitions

  • the invention relates to the technical field of self-driving cars, in particular to a complex network-based self-driving car complex environment model, a cognitive system and a cognitive method.
  • a complex network is a highly complex network, an abstraction of a complex system, and generally has some or all of the properties of self-organization, self-similarity, attractor, small world, and scale-free.
  • the characteristics of a complex network are complexity, which is specifically manifested in: large network scale, complex connection structure, node complexity (such as: node dynamic complexity and node diversity), complex spatiotemporal evolution process of the network, sparseness of network connections, multiple A heavy complexity fusion etc.
  • Complexity research methods for complex networks such as node complexity, connection structure complexity, and network spatiotemporal evolution process complexity, have become important tools for modeling and researching complex systems.
  • An autonomous vehicle is a comprehensive system that integrates environmental perception, planning and decision-making, and control execution. Due to the rapid development of sensor technologies such as lidar, millimeter-wave radar, and camera, environmental perception methods have been studied in depth and great progress has been made. At present, the establishment of the relationship between the individual type, location, movement and other low-level perception information of the environment and the individual behavior style, hierarchical local environment, and global environmental cognition supports the transition from environmental perception to individual cognition, local cognition to traffic comprehensive situation The development of global cognition has become an important prerequisite for ensuring the safety of autonomous decision-making and motion planning of autonomous vehicles. However, the environment faced by self-driving cars is a complex system.
  • the present invention provides a complex network-based self-driving car complex environment model, a cognitive system and a cognitive method.
  • the driving style is identified based on the driving characteristic parameters used to represent the aggressiveness of driving manipulation and the mode transfer preference; secondly, based on the group behavior characteristics of the moving subjects in the environment, on the basis of the driving style identification, based on the complex network,
  • a time-varying complex dynamic network is established as the complex environment model of the autonomous vehicle;
  • the nodes in the complex environment model are parametrically expressed to realize the node differential cognition of the complex environment,
  • the agglomerative algorithm is used to stratify the nodes in the complex environment model to realize the hierarchical cognition of the complex environment, establish the disorder degree measurement method of the complex environment model, and realize the global risk situation cognition of the complex environment.
  • the cognition system of the self-driving car based on the complex network of the present invention includes: a driving style recognition module, a complex environment model module, a node differentiation cognition module, a hierarchical cognition module, and a global risk situation cognition module.
  • the driving style recognition module constructs a driving style feature matrix C J on the basis of extracting driving feature parameters, inputs the driving style feature matrix C J into a random forest classifier R f , and outputs the driving style through the random forest classifier R f Category K drive .
  • the driving characteristic parameters include longitudinal driving characteristic parameters, lateral driving characteristic parameters and mode transfer characteristic parameters.
  • the longitudinal driving characteristic parameters refer to the longitudinal acceleration a + and the heel-relaxation time d time within the limited time window
  • the lateral driving characteristic parameters refer to the lateral acceleration RMS(a - ), lateral
  • the driving style characteristic matrix C J refers to a three-dimensional six-degree-of-freedom characteristic matrix composed of longitudinal driving characteristic parameters, lateral driving characteristic parameters and mode transfer characteristic parameters:
  • the random forest classifier R f is generated through the following steps: the original training set composed of driving style data is randomly sampled with replacement, m training sets are generated, n features are selected for each training set, and m decision-making Tree classification model, for each classification model, select the best sample features according to the information gain ratio to split until all training samples belong to the same class, and finally form all the generated decision tree classification models into a random forest, and output driving by voting method Style category K drive .
  • the driving style category K drive includes three categories: aggressive, peaceful, and conservative:
  • the complex environment model module is to describe the random, dynamic and nonlinear evolution law of the complex environment of the self-driving car, based on the complex network theory, with the moving subject as the node, constructing a time-varying complex dynamic network G as the complex environment model:
  • G is a time-varying complex dynamic network
  • V is a set of nodes in a time-varying complex dynamic network G
  • B is a set of edges in a time-varying complex dynamic network G, representing the connection between nodes
  • X is a time-varying complex dynamic network
  • P is the strength function of the edge in the complex dynamic network G, which represents the coupling relationship between nodes
  • ⁇ is the area function of the time-varying complex dynamic network G, which represents the dynamic constraints 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 i-th node is set to x i , then the dynamic equation of the i-th node is:
  • f(x i ) is the independent function of the state variable of the i-th node
  • ⁇ >0 is the strength coefficient of the common connection relationship
  • p ij (t) is the coupling coefficient between the i-th node and the j-th node
  • H(x j ) is an inline function between nodes, which is a function of driving style and node distance.
  • X is the state vector of the nodes in the time-varying complex dynamic network G
  • F(X) is the dynamic equation vector of the nodes in the time-varying complex dynamic network G
  • P(t) is the coupling between nodes in the time-varying complex dynamic network G matrix
  • H(X) is the inline vector of nodes in the time-varying complex dynamic network G.
  • the node differentiation cognition module expresses the difference of network nodes with four parameters including the quantity g i , degree k i , point weight s i and importance I(i) of the nodes in the complex environment model, and uses Normal distribution plots are differentiated across all nodes.
  • the quantity g i of the nodes is represented by the structure size of the i-th node.
  • the degree ki of the node is represented by the number of nodes directly connected to the i-th node.
  • the point weight s i of the node represents the sum of edge weights of all adjacent edges of the i-th node.
  • p ij (t) is the coupling coefficient between nodes
  • K(i) is the degree centrality factor of the i-th node:
  • ⁇ k> ⁇ k i /N, which represents the average degree of the module; Indicates the average unit weight of the module.
  • the hierarchical cognition module adopts the agglomeration algorithm to divide the nodes in the complex environment model hierarchically, so as to realize the hierarchical and stepwise cognition of the complex environment of the self-driving car.
  • the operation steps are as follows:
  • the first step is to take the self-driving car as the central node, and the nodes that have a coupling relationship with the central node and the central node form the inner module;
  • the second step is to sort the non-central nodes of the inner module by importance, and find the point with the largest coupling coefficient in turn to form the middle module;
  • the third step is to sort the importance of the nodes of the middle layer module, and then find the point with the largest coupling coefficient to form the outer layer module;
  • the global risk situation cognition module is based on the basic idea of entropy theory, uses system entropy and entropy change to measure the degree of disorder of the complex environment model, describes the overall risk and change situation, and realizes the state cognition of the global commonality.
  • V n is the number of nodes in the complex environment model
  • ⁇ is the network area of the complex environment model
  • D(P) represents the variance of the coupling coefficient
  • D(U) is the variance of the node speed in the complex environment model.
  • d means to calculate the differential of the corresponding variable, indicating its changing trend.
  • the cognitive method of the self-driving car proposed by the present invention includes the following steps:
  • Step 1) Extract longitudinal driving characteristic parameters, lateral driving characteristic parameters and mode transfer characteristic parameters, construct driving style characteristic matrix C J , generate random forest classifier R f , input driving style characteristic matrix C J into random forest classifier R f , The output driving style category K drive of the random forest classifier R f recognizes the driving style as aggressive, peaceful and conservative;
  • Step 2) Construct a time-varying complex dynamic network G as a complex environment model, which is used to describe the overall correlation characteristics of the complex environment, further establish the node dynamic equation in the complex environment model, and then combine the characteristics of all nodes in the time-varying complex dynamic network G to form The dynamic equation vector F(X), the coupling matrix P(t) between nodes in the time-varying complex dynamic network G and the inline vector H(X) of the nodes, establish the node system dynamic equation of the time-varying complex dynamic network G, using to describe the dynamic characteristics of complex environments;
  • Step 3) Construct four parameters of the node quantity g i , degree k i , point weight s i and importance I(i) in the complex environment model, and use the normal distribution graph to conduct differential analysis on nodes to realize node differential recognition Know;
  • Step 4) Use the agglomeration algorithm to divide the nodes in the complex environment model into layers, so as to realize the hierarchical and step-by-step cognition of the complex environment of the self-driving car;
  • Step 5 According to the basic idea of entropy theory, use system entropy and entropy change to measure the degree of disorder of the complex environment model, describe the overall risk and change situation, and realize the state cognition of the global commonality.
  • the present invention first aims at the complexity of individual driving behavior cognition, and performs driving style recognition according to the driving characteristic parameters indicating the aggressiveness of driving manipulation and mode transfer preference; secondly, according to the complex Group behavior characteristics of moving subjects in the environment, on the basis of driving style recognition, based on complex networks, with moving subjects as nodes and roads as constraints, construct a time-varying complex dynamic network G as a complex environment model for autonomous vehicles; finally, the The nodes in the complex environment model are expressed parametrically to realize the differential cognition of the nodes in the complex environment, and the nodes in the complex environment model are layered by using the agglomeration algorithm, so as to realize the hierarchical cognition of the complex environment and establish the complex environment model
  • the measurement method of the degree of disorder can realize the global risk situation cognition of the complex environment, so as to establish the complex environment model, cognitive method and device of the self-driving car based on the complex network, and lay a solid foundation for the safe driving
  • the present invention establishes a driving style recognition method. On the basis of extracting the driving characteristic parameters, the driving style characteristic matrix C J is constructed, and the driving style characteristic matrix C J is input into the random forest classifier R f , and the random forest classifier R f outputs Driving style category K drive to realize driving style recognition;
  • the present invention is based on the complex network theory, takes the moving subject as the node, constructs a time-varying complex dynamic network G as a complex environment model, describes the random, dynamic, and nonlinear evolution law of the complex environment of the self-driving car, and also establishes a time-varying complex dynamic network G.
  • the dynamic equation of the node system of the dynamic network G which describes the dynamic characteristics of the complex environment;
  • the present invention constructs four parameters of nodes in the complex environment model, namely, the quantity g i , degree k i , point weight s i , and importance I(i), and uses a normal distribution diagram to perform differential analysis on the nodes, so as to realize automatic driving Differentiated cognition of nodes in the complex environment of automobiles;
  • the present invention adopts the agglomeration algorithm to divide the nodes in the complex environment model hierarchically, so as to realize the hierarchical and stepwise cognition of the complex environment of the self-driving car;
  • the present invention constructs the system entropy and entropy change of the complex environment model of the self-driving car to measure the degree of disorder of the complex environment model, describes the overall risk and change situation, and realizes the state recognition of the global commonality of the complex environment of the self-driving car.
  • Figure 1 is a flow chart of the structure of the driving style recognition module.
  • Fig. 2 Flow chart of the module structure of the complex environment model of the self-driving car.
  • Figure 3 is a structural diagram of the node differentiation cognitive module.
  • Figure 4 is a flow chart of the hierarchical cognitive module structure.
  • Figure 5 Structural diagram of the global risk situation awareness module.
  • Figure 6 is a schematic structural diagram of a self-driving car cognitive system based on a complex network.
  • the longitudinal driving characteristic parameters refer to the longitudinal acceleration a + , Heel-relaxation time d time
  • the lateral driving characteristic parameters refer to the lateral acceleration root mean square RMS(a - ) and the yaw rate standard deviation SD(r) within a limited time window
  • the mode transfer characteristic parameters have a limited time window
  • the left lane-changing state transition probability P(l c ) and the right lane-changing state transition probability P(r c ) then, construct the driving style characteristic matrix C J
  • the driving style characteristic matrix C J refers to the A three-dimensional six-degree-of-freedom feature matrix composed of feature parameters, lateral driving feature parameters, and mode transfer feature parameters; then, input the driving style feature matrix C J into the random forest classifier R f , and output the driving style category K drive
  • the driving style category K drive includes three types: aggressive, peaceful, and
  • G (V, B, X, P, ⁇ )
  • the time-varying complex The dynamic network G is equivalent to a continuous-time dynamic system with N nodes, and the dynamic equation of the nodes is established:
  • the dynamic equation of the node system is established: Finally, the node system dynamic equation is input into the complex environment model to describe the dynamic characteristics of the complex environment.
  • the node differentiated cognitive module structure uses four parameters of the node quantity g i , degree ki , point weight s i and importance I(i) in the complex environment model to describe the network node
  • the differences of all nodes are analyzed using the normal distribution graph to realize the differential cognition of the nodes.
  • the hierarchical cognition module structure process adopts the agglomerative algorithm to divide the nodes in the complex environment model hierarchically, and divides the nodes in the complex environment model in turn and forms the inner module, the middle module, and the outer module respectively.
  • the layer module and the edge layer module realize the hierarchical cognition of the complex environment.
  • the cognitive system of an autonomous vehicle based on a complex network includes a driving style recognition module, a complex environment model module, a node differentiation cognitive module, a hierarchical cognitive module, and a global risk situation cognitive module.
  • the driving style identification module inputs the identified node driving style into the complex environment model module, which is used to construct the inter-node interlink function H(x j ); the node differentiation cognitive module, hierarchical cognitive module, global risk
  • the situational cognition module receives the data of V, B, X, P, and ⁇ parameters in the complex environment model module, and realizes node differential cognition, hierarchical cognition and global risk situation cognition respectively.
  • a cognitive method for a self-driving car based on a complex network includes the following steps:
  • Step 1) Extract longitudinal driving characteristic parameters, lateral driving characteristic parameters and mode transfer characteristic parameters, construct driving style characteristic matrix C J , generate random forest classifier R f , input driving style characteristic matrix C J into random forest classifier R f , The output driving style category K drive of the random forest classifier R f identifies the driving style as aggressive, peaceful and conservative.
  • the specific steps are:
  • Step 2 Construct a time-varying complex dynamic network G as a complex environment model, which is used to describe the overall correlation characteristics of the complex environment, further establish the node dynamic equation in the complex environment model, and then combine the characteristics of all nodes in the time-varying complex dynamic network G to form The dynamic equation vector F(X), the coupling matrix P(t) between nodes in the time-varying complex dynamic network G and the inline vector H(X) of the nodes, establish the node system dynamic equation of the time-varying complex dynamic network G, using To describe the dynamic characteristics of complex environments, the specific steps are:
  • Step 3) Construct four parameters of the node quantity g i , degree k i , point weight s i and importance I(i) in the complex environment model, and use the normal distribution diagram to perform differential analysis on all nodes to realize node differentiation cognition, the specific steps are:
  • Step 4) Use the agglomerative algorithm to divide the nodes in the complex environment model hierarchically, and realize the hierarchical and step-by-step cognition of the complex environment of the self-driving car.
  • the specific steps are:
  • Step 5 According to the basic idea of entropy theory, use system entropy and entropy change to measure the degree of disorder of the complex environment model, describe the overall risk and change situation, and realize the state cognition of the global commonality.
  • the specific steps are as follows:
  • Specific embodiments of the present invention use Python to write the driving style recognition module, construct the driving style feature matrix C J based on the Scikit-learn third-party machine learning library, generate a random forest classifier R f , and realize the driving style recognition; use MATLAB/Simulink to write The mathematical model constitutes a complex environment model module; use Python to write the node differentiation cognitive module, hierarchical cognitive module, and global risk situation cognitive module, and realize the differentiation, hierarchy, and global risk of the complex environment of autonomous vehicles in the PyTorch framework Situational awareness method; write MATLAB, Scikit-learn and PyTorch interfaces based on Ubuntu system, install and configure them in industrial control computers, and realize complex environment models, cognitive methods and devices for autonomous vehicles based on complex networks.

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Abstract

基于复杂网络的自动驾驶汽车复杂环境模型、认知***及认知方法，在感知自动驾驶汽车外部环境的基础上，针对个体驾驶行为认知的复杂性问题，使用用于表示驾驶操控激进程度和模式转移偏好的驾驶特征参数进行驾驶风格识别；依据环境中运动主体的群体行为特征，基于复杂网络，以运动主体为节点，以道路为约束，建立时变复杂动态网络作为自动驾驶汽车复杂环境模型；对复杂环境模型中的节点进行参数化表述，实现对复杂环境的节点差异化认知，采用凝聚算法对复杂环境模型中的节点分层，实现对复杂环境的层次化认知，建立复杂环境模型的无序程度度量方法，实现对复杂环境的全局风险态势认知。

Description

基于复杂网络的自动驾驶汽车复杂环境模型、认知***及认知方法 技术领域
本发明涉及自动驾驶汽车应用技术领域，尤其涉及一种基于复杂网络的自动驾驶汽车复杂环境模型、认知***及认知方法。
背景技术
复杂网络是呈现高度复杂性的网络，是复杂***的抽象，一般具有自组织、自相似、吸引子、小世界、无标度中的部分或全部性质。复杂网络的特性是复杂性，具体表现在：网络规模大，连接结构复杂，节点复杂性(如:节点动力学复杂性和节点多样性)，网络时空演化过程复杂，网络连接的稀疏性，多种重复杂性融合等。复杂网络的复杂性研究方法，如：节点复杂性、连接结构复杂性和网络时空演化过程复杂性等研究方法，已成为复杂***建模和研究的重要工具。
自动驾驶汽车是一个集环境感知、规划决策、控制执行等功能于一体的综合***。由于激光雷达、毫米波雷达、摄像头等传感器技术的迅速发展，环境感知方法得到深入研究，取得了很大进展。当前，建立环境的个体类型、位置、运动等底层感知信息与个体行为风格、层次化局部环境、全局环境认知之间的关联，支撑从环境感知到个体认知、局部认知到交通综合态势全局认知的发展，已成为保障自动驾驶汽车自主决策与运动规划安全性的重要前提。然而，自动驾驶汽车所面临的环境是一个复杂***，在这个复杂***中，个体的运动行为不仅依赖于该个体自身，而且受周边其它个体运动行为及驾驶环境的影响，具有复杂的多维耦合性和动态不确定性。因此，基于复杂网络，建立自动驾驶汽车复杂环境模型、认知方法及装置，揭示自动驾驶汽车所面临环境的非线性动态演化规律，已成为解决高级别自动驾驶环境认知难题的重要环节。
发明内容
为解决上述技术问题，本发明提供一种基于复杂网络的自动驾驶汽车复杂环境模型、认知***及认知方法，在感知自动驾驶汽车外部环境的基础上，首先，针对个体驾驶行为认知的复杂性问题，依据用于表示驾驶操控激进程度和模式转移偏好的驾驶特征参数，进行驾驶风格识别；其次，依据环境中运动主体的群体行为特征，在驾驶风格识别的基础上，基于复杂网络，以运动主体为节点，以道路为约束，建立时变复杂动态网络作为自动驾驶汽车复杂环境模型；最后，对复杂环境模型中的节点进行参数化表述，实现对 复杂环境的节点差异化认知，采用凝聚算法对复杂环境模型中的节点进行分层，实现对复杂环境的层次化认知，建立复杂环境模型的无序程度度量方法，实现对复杂环境的全局风险态势认知。
本发明基于复杂网络的自动驾驶汽车的认知***包括：驾驶风格识别模块，复杂环境模型模块，节点差异化认知模块，层次化认知模块，全局风险态势认知模块。
所述驾驶风格识别模块，是在提取驾驶特征参数的基础上，构造驾驶风格特征矩阵C J，将驾驶风格特征矩阵C J输入随机森林分类器R f，通过随机森林分类器R f输出驾驶风格类别K drive。
所述驾驶特征参数，包括纵向驾驶特征参数、横向驾驶特征参数和模式转移特征参数。所述纵向驾驶特征参数是指有限时窗内的纵向加速度a +、跟弛时距d time，所述横向驾驶特征参数是指有限时窗内的横向加速度均方根RMS(a -)、横摆角速度标准差SD(r)，所述模式转移特征参数有限时窗内的左换道状态转移概率P(l c)和右换道状态转移概率P(r c)。
所述驾驶风格特征矩阵C J，是指由纵向驾驶特征参数、横向驾驶特征参数和模式转移特征参数构成的三维六自由度特征矩阵：
Figure PCTCN2022070671-appb-000001
所述随机森林分类器R f通过如下步骤生成：对驾驶风格数据组成的原始训练集进行有放回随机抽样，生成m个训练集，对每个训练集选择n个特征，分别训练m个决策树分类模型，对于每个分类模型根据信息增益比选择最好的样本特征进行***，直到所有训练样例都属于同一类，最后将生成的所有决策树分类模型组成随机森林，通过投票法输出驾驶风格类别K drive。
所述驾驶风格类别K drive包括激进型、平和型、保守型三类：
K drive＝R f(C J)      (2)
所述复杂环境模型模块，是为了刻画自动驾驶汽车复杂环境的随机、动态、非线性演化规律，基于复杂网络理论，以运动主体为节点，构造时变复杂动态网络G作为复杂环境模型：
G＝(V,B,X,P,Θ)     (3)
其中，G是时变复杂动态网络，V是时变复杂动态网络G中节点集合，B是时变复杂动态网络G中边的集合，表示节点之间的连线，X是时变复杂动态网络G中节点的状态向 量，P为复杂动态网络G中边的强度函数，表示节点间的耦合关系，Θ为时变复杂动态网络G的区域函数，表示对时变复杂动态网络G的动态约束。
将时变复杂动态网络G等效为具有N个节点的连续时间动态***，设第i节点的状态变量为x i，则第i节点的动力学方程为：
Figure PCTCN2022070671-appb-000002
其中，f(x i)为第i节点状态变量的自变函数，ξ>0为共同连接关系强度系数，p ij(t)为第i节点和第j节点之间的耦合系数，H(x j)为节点间的内联函数，是驾驶风格和节点距离的函数。
记X＝[x 1,x 2,…,x N] T，F(X)＝[f(x 1),f(x 2),…,f(x N)] T，P(t)＝[(p ij(t))]∈R N×N,H(X)＝[H(x 1),H(x 2),…,H(x N)] T，则时变复杂动态网络G的节点***动力学方程为：
Figure PCTCN2022070671-appb-000003
其中，X为时变复杂动态网络G中节点的状态向量，F(X)为时变复杂动态网络G中节点的动态方程向量，P(t)为时变复杂动态网络G中节点间的耦合矩阵，H(X)为时变复杂动态网络G中节点的内联向量。
在复杂环境模型中，随着节点的运动和环境的变化，节点的位置和状态处于动态变化中，有节点汇入和流出网络，节点间的耦合关系和网络区域函数也随之变化，复杂网络***随时间不断地演化发展。
所述节点差异化认知模块，就是用复杂环境模型中节点的量g i、度k i、点权s i和重要度I(i)共四个参数来表述了网络节点的差异性，并用正态分布图对所有节点进行差异化分析。
所述节点的量g i，用第i节点的结构尺寸表示。
所述节点的度k i，是用与第i节点直接相连的节点数目表示。
所述节点的点权s i，表示第i节点所有邻边的边权和。
所述节点的重要度I(i)：
I(i)＝K(i)+∑ jp ij(t)     (6)
(6)式中，p ij(t)为节点间的耦合系数，K(i)为第i节点的度中心性因子：
Figure PCTCN2022070671-appb-000004
(7)式中，<k>＝∑k i/N，表示模块的平均度；
Figure PCTCN2022070671-appb-000005
表示模块的平均单 位权。
所述层次化认知模块，是采用凝聚算法对复杂环境模型中的节点进行层次划分，实现对自动驾驶汽车复杂环境的层次化、阶梯性认知，操作步骤如下：
第一步，以自动驾驶汽车为中心节点，与中心节点存在耦合关系的节点和中心节点组成内层模块；
第二步，对内层模块的非中心节点进行重要度排序，依次寻找耦合系数最大的点组成中间层模块；
第三步，对中间层模块的节点进行重要度排序，依次寻找耦合系数最大的点组成外层模块；
第四步，其它节点组成边缘层模块。
所述全局风险态势认知模块，是依据熵理论的基本思想，用***熵和熵变对复杂环境模型的无序程度进行度量，描述整体风险及变化态势，实现对全局共性的状态认知。
所述***熵：
S＝V n/Θ+D(P)+D(U)      (8)
其中，V n为复杂环境模型的节点数量，Θ为复杂环境模型的网络区域，D(P)表示耦合系数的方差，D(U)为复杂环境模型中节点速度的方差。
所述熵变：
Figure PCTCN2022070671-appb-000006
其中，d表示计算相应变量的微分，表示其变化趋势。
根据上述基于复杂网络的自动驾驶汽车的认知***，本发明提出的自动驾驶汽车的认知方法包括如下步骤：
步骤1)提取纵向驾驶特征参数、横向驾驶特征参数和模式转移特征参数，构造驾驶风格特征矩阵C J，生成随机森林分类器R f，将驾驶风格特征矩阵C J输入随机森林分类器R f，随机森林分类器R f的输出驾驶风格类别K drive，将驾驶风格识别为激进型、平和型、保守型三类；
步骤2)构造时变复杂动态网络G作为复杂环境模型，用于描述复杂环境整体关联特征，进一步建立复杂环境模型中的节点动力学方程，再组合时变复杂动态网络G中所有节点的特征形成动态方程向量F(X)、时变复杂动态网络G中节点间的耦合矩阵P(t)和节点的内联向量H(X)，建立时变复杂动态网络G的节点***动力学方程，用于描述复杂环境的动态特性；
步骤3)构造复杂环境模型中节点的量g i、度k i、点权s i和重要度I(i)四个参数，并用正态分布图对节点进行差异化分析，实现节点差异化认知；
步骤4)采用凝聚算法对复杂环境模型中节点进行层次划分，实现对自动驾驶汽车复杂环境的层次化、阶梯性认知；
步骤5)依据熵理论的基本思想，用***熵和熵变对复杂环境模型的无序程度进行度量，描述整体风险及变化态势，实现对全局共性的状态认知。
本发明在感知自动驾驶汽车外部环境的基础上，首先，针对个体驾驶行为认知的复杂性问题，依据表示驾驶操控激进程度和模式转移偏好的驾驶特征参数，进行驾驶风格识别；其次，依据复杂环境中运动主体的群体行为特征，在驾驶风格识别的基础上，基于复杂网络，以运动主体为节点，以道路为约束，构造时变复杂动态网络G作为自动驾驶汽车复杂环境模型；最后，对复杂环境模型中的节点进行参数化表述，实现对复杂环境的节点差异化认知，采用凝聚算法对复杂环境模型中的节点进行分层，实现对复杂环境的层次化认知，建立复杂环境模型的无序程度度量方法，实现对复杂环境的全局风险态势认知，从而建立基于复杂网络的自动驾驶汽车复杂环境模型、认知方法及装置，为自动驾驶汽车的安全驾驶和控制策略设计打下了良好的基础。
本发明的有益效果：
1、本发明建立了驾驶风格识别方法，在提取驾驶特征参数的基础上，构造驾驶风格特征矩阵C J，将驾驶风格特征矩阵C J输入随机森林分类器R f，随机森林分类器R f输出驾驶风格类别K drive，实现驾驶风格识别；
2、本发明基于复杂网络理论，以运动主体为节点，构造时变复杂动态网络G作为复杂环境模型，刻画了自动驾驶汽车复杂环境的随机、动态、非线性演化规律，还建立了时变复杂动态网络G的节点***动力学方程，描述复杂环境的动态特性；
3、本发明构造复杂环境模型中节点的量g i、度k i、点权s i和重要度I(i)四个参数，并用正态分布图对节点进行差异化分析，实现对自动驾驶汽车复杂环境的节点差异化认知；
4、本发明以节点耦合关系为依据，采用凝聚算法对复杂环境模型中的节点进行层次划分，实现对自动驾驶汽车复杂环境的层次化、阶梯性认知；
5、本发明构造自动驾驶汽车复杂环境模型的***熵和熵变对复杂环境模型的无序程度进行度量，描述整体风险及变化态势，实现对自动驾驶汽车复杂环境全局共性的状态认知。
附图说明
图1是驾驶风格识别模块结构流程图。
图2自动驾驶汽车复杂环境模型模块结构流程图。
图3是节点差异化认知模块结构图。
图4是层次化认知模块结构流程图。
图5全局风险态势认知模块结构图。
图6基于复杂网络的自动驾驶汽车认知***的结构示意图。
具体实施方式
下面结合附图对本发明作进一步说明。
如图1所示，是驾驶风格识别模块结构流程，首先，提取纵向驾驶特征参数、横向驾驶特征参数和模式转移特征参数，所述纵向驾驶特征参数是指有限时窗内的纵向加速度a +、跟弛时距d time，所述横向驾驶特征参数是指有限时窗内的横向加速度均方根RMS(a -)、横摆角速度标准差SD(r)，所述模式转移特征参数有限时窗内的左换道状态转移概率P(l c)和右换道状态转移概率P(r c)；接着，构造驾驶风格特征矩阵C J，所述驾驶风格特征矩阵C J，是指由纵向驾驶特征参数、横向驾驶特征参数和模式转移特征参数构成的三维六自由度特征矩阵；然后，将驾驶风格特征矩阵C J输入随机森林分类器R f，输出驾驶风格类别K drive，所述驾驶风格类别K drive包括激进型、平和型、保守型三类，实现了驾驶风格识别。
如图2所示，自动驾驶汽车复杂环境模型模块结构流程，首先，构造时变复杂动态网络G作为复杂环境模型：G＝(V,B,X,P,Θ)；接着，将时变复杂动态网络G等效为具有N个节点的连续时间动态***，建立节点的动力学方程：
Figure PCTCN2022070671-appb-000007
然后，根据节点的动力学方程，建立节点***动力学方程：
Figure PCTCN2022070671-appb-000008
最后将节点***动力学方程输入复杂环境模型，用于描述复杂环境的动态特性。
如图3所示，节点差异化认知模块结构，联合使用复杂环境模型中节点的量g i、度k i、点权s i和重要度I(i)共四个参数来表述了网络节点的差异性，并用正态分布图对所有节点进行差异化分析，实现对节点的差异化认知。
如图4所示，层次化认知模块结构流程，采用凝聚算法，对复杂环境模型中的节点进行层次划分，将复杂环境模型中的节点依次划分并分别组成内层模块、中间层模块、 外层模块、边缘层模块，实现对复杂环境的层次化认知。
如图5所示，全局风险态势认知模块结构，联合使用***熵：S＝V n/Θ+D(P)+D(U)和熵变：
Figure PCTCN2022070671-appb-000009
对复杂环境模型的无序程度进行度量，描述整体风险及变化态势，实现对复杂环境全局共性的状态认知。
如图6所示，基于复杂网络的自动驾驶汽车的认知***包括驾驶风格识别模块，复杂环境模型模块，节点差异化认知模块，层次化认知模块，全局风险态势认知模块。所述驾驶风格识别模块将识别的节点驾驶风格输入复杂环境模型模块，用于构造节点间的内联函数H(x j)；所述节点差异化认知模块、层次化认知模块、全局风险态势认知模块接收复杂环境模型模块中V,B,X,P,Θ参数的数据，分别实现节点差异化认知、层次化认知和全局风险态势认知。
一种基于复杂网络的自动驾驶汽车的认知方法包括如下步骤：
步骤1)提取纵向驾驶特征参数、横向驾驶特征参数和模式转移特征参数，构造驾驶风格特征矩阵C J，生成随机森林分类器R f，将驾驶风格特征矩阵C J输入随机森林分类器R f，随机森林分类器R f的输出驾驶风格类别K drive，将驾驶风格识别为激进型、平和型、保守型三类，具体步骤为：
(A)提取纵向驾驶特征参数、横向驾驶特征参数和模式转移特征参数；
(B)构造驾驶风格特征矩阵C J；
(C)生成随机森林分类器R f；
(D)将驾驶风格特征矩阵C J输入随机森林分类器R f，随机森林分类器R f的输出驾驶风格类别K drive，将驾驶风格识别为激进型、平和型、保守型三类；
步骤2)构造时变复杂动态网络G作为复杂环境模型，用于描述复杂环境整体关联特征，进一步建立复杂环境模型中的节点动力学方程，再组合时变复杂动态网络G中所有节点的特征形成动态方程向量F(X)、时变复杂动态网络G中节点间的耦合矩阵P(t)和节点的内联向量H(X)，建立时变复杂动态网络G的节点***动力学方程，用于描述复杂环境的动态特性，具体步骤为：
(A)构造时变复杂动态网络G作为复杂环境模型；
(B)基于复杂环境模型中的参数，建立复杂环境模型中的节点动力学方程；
(C)基于节点动力学方程，建立时变复杂动态网络G的节点***动力学方程，用于描述复杂环境的动态特性；
步骤3)构造复杂环境模型中节点的量g i、度k i、点权s i和重要度I(i)四个参数，并用正态分布图对所有节点进行差异化分析，实现节点差异化认知，具体步骤为：
(A)构造复杂环境模型中节点的量g i、度k i、点权s i和重要度I(i)四个参数；
(B)用上述四个参数分别描述复杂环境模型中所有节点；
(C)用正态分布图对所有节点进行差异化分析，实现节点的差异化认知；
步骤4)采用凝聚算法对复杂环境模型中节点进行层次划分，实现对自动驾驶汽车复杂环境的层次化、阶梯性认知，具体步骤为：
(A)以自动驾驶汽车为中心节点，与中心节点存在耦合关系的节点和中心节点组成内层模块；
(B)对内层模块的非中心节点进行重要度排序，依次寻找耦合系数最大的点组成中间层模块；
(C)对中间层模块的节点进行重要度排序，依次寻找耦合系数最大的点组成外层模块；
(D)其它节点组成边缘层模块；
步骤5)依据熵理论的基本思想，用***熵和熵变对复杂环境模型的无序程度进行度量，描述整体风险及变化态势，实现对全局共性的状态认知，具体步骤为：
(A)使用***熵：S＝V n/Θ+D(P)+D(U)对复杂环境模型的无序程度进行度量，描述复杂环境整体风险；
(B)使用熵变：dS＝d(V n/Θ)+d(D(P))+d(D(U))对复杂环境模型的无序程度进行度量，描述复杂环境整体风险的变化态势，实现对全局共性的状态认知。
本发明的具体实施例：使用Python编写驾驶风格识别模块，基于Scikit-learn第三方机器学习库构造驾驶风格特征矩阵C J，生成随机森林分类器R f，实现驾驶风格识别；使用MATLAB/Simulink编写数学模型构成复杂环境模型模块；使用Python编写节点差异化认知模块、层次化认知模块、全局风险态势认知模块，在PyTorch框架中实现自动驾驶汽车复杂环境的差异化、层次化、全局风险态势认知方法；基于Ubuntu***编写MATLAB、Scikit-learn和PyTorch接口，在工业控制计算机中安装和配置，实现基于复杂网络的自动驾驶汽车复杂环境模型、认知方法及装置。
上文所列出的一系列的详细说明仅仅是针对本发明的可行性实施方式的具体说明，它们并非用以限制本发明的保护范围，凡未脱离本发明技术所创的等效方式或变更均应 包含在本发明的保护范围之内。

Claims (10)

  1. 基于复杂网络的自动驾驶汽车复杂环境模型，其特征在于，以运动主体为节点，构造时变复杂动态网络G作为复杂环境模型：
    G＝(V,B,X,P,Θ)  (3)
    其中，G是时变复杂动态网络，V是时变复杂动态网络G中节点集合，B是时变复杂动态网络G中边的集合，表示节点之间的连线，X是时变复杂动态网络G中节点的状态向量，P为复杂动态网络G中边的强度函数，表示节点间的耦合关系，Θ为时变复杂动态网络G的区域函数，表示对时变复杂动态网络G的动态约束；
    将时变复杂动态网络G等效为具有N个节点的连续时间动态***，设第i节点的状态变量为x i，则第i节点的动力学方程为：
    Figure PCTCN2022070671-appb-100001
    其中，f(x i)为第i节点状态变量的自变函数，ξ>0为共同连接关系强度系数，p ij(t)为第i节点和第j节点之间的耦合系数，H(x j)为节点间的内联函数，是驾驶风格和节点距离的函数；
    记X＝[x 1,x 2,…,x N] T，F(X)＝[f(x 1),f(x 2),…,f(x N)] T，P(t)＝[(p ij(t))]∈R N×N,H(X)＝[H(x 1),H(x 2),…,H(x N)] T，则时变复杂动态网络G的节点***动力学方程为：
    Figure PCTCN2022070671-appb-100002
    其中，X为时变复杂动态网络G中节点的状态向量，F(X)为时变复杂动态网络G中节点的动态方程向量，P(t)为时变复杂动态网络G中节点间的耦合矩阵，H(X)为时变复杂动态网络G中节点的内联向量；
    所述复杂环境模型中，随着节点的运动和环境的变化，节点的位置和状态处于动态变化中，有节点汇入和流出网络，节点间的耦合关系和网络区域函数也随之变化，复杂网络***随时间不断地演化发展。
  2. 基于复杂网络的自动驾驶汽车的认知***，其特征在于，包括：驾驶风格识别模块，复杂环境模型模块，节点差异化认知模块，层次化认知模块，全局风险态势认知模块；
    所述驾驶风格识别模块，是在提取驾驶特征参数的基础上，构造驾驶风格特征矩阵C J，将驾驶风格特征矩阵C J输入随机森林分类器R f，通过随机森林分类器R f输出驾驶风格类别K drive；
    所述复杂环境模型模块为权利要求1所述的复杂环境模型；
    所述节点差异化认知模块，利用复杂环境模型中节点的量g i、度k i、点权s i和重要度I(i)共四个参数来表述了网络节点的差异性，并用正态分布图对所有节点进行差异化分析；
    所述层次化认知模块，是采用凝聚算法对复杂环境模型中的节点进行层次划分，实现对自动驾驶汽车复杂环境的层次化、阶梯性认知；
    所述全局风险态势认知模块，利用***熵和熵变对复杂环境模型的无序程度进行度量，描述整体风险及变化态势，实现对全局共性的状态认知。
  3. 根据权利要求2所述的自动驾驶汽车的认知***，其特征在于，所述驾驶特征参数，包括纵向驾驶特征参数、横向驾驶特征参数和模式转移特征参数。所述纵向驾驶特征参数是指有限时窗内的纵向加速度a +、跟弛时距d time，所述横向驾驶特征参数是指有限时窗内的横向加速度均方根RMS(a -)、横摆角速度标准差SD(r)，所述模式转移特征参数有限时窗内的左换道状态转移概率P(l c)和右换道状态转移概率P(r c)。
  4. 根据权利要求2所述的自动驾驶汽车的认知***，其特征在于，所述驾驶风格特征矩阵C J，是指由纵向驾驶特征参数、横向驾驶特征参数和模式转移特征参数构成的三维六自由度特征矩阵：
    Figure PCTCN2022070671-appb-100003
  5. 根据权利要求2所述的自动驾驶汽车的认知***，其特征在于，所述随机森林分类器R f通过如下步骤生成：对驾驶风格数据组成的原始训练集进行有放回随机抽样，生成m个训练集，对每个训练集选择n个特征，分别训练m个决策树分类模型，对于每个分类模型根据信息增益比选择最好的样本特征进行***，直到所有训练样例都属于同一类，最后将生成的所有决策树分类模型组成随机森林，通过投票法输出驾驶风格类别K dri；
    所述驾驶风格类别K drive包括激进型、平和型、保守型三类：
    K drive＝R f(C J)  (2)。
  6. 根据权利要求2所述的自动驾驶汽车的认知***，其特征在于，所述节点的量g i，用第i节点的结构尺寸表示；
    所述节点的度k i，是用与第i节点直接相连的节点数目表示；
    所述节点的点权s i，表示第i节点所有邻边的边权和；
    所述节点的重要度I(i)：
    I(i)＝K(i)+∑ jp ij(t)  (6)
    (6)式中，p ij(t)为节点间的耦合系数，K(i)为第i节点的度中心性因子：
    Figure PCTCN2022070671-appb-100004
    (7)式中，(k)＝∑k i/N，表示模块的平均度；
    Figure PCTCN2022070671-appb-100005
    表示模块的平均单位权。
  7. 根据权利要求2所述的自动驾驶汽车的认知***，其特征在于，所述层次化认知模块中，首先以自动驾驶汽车为中心节点，与中心节点存在耦合关系的节点和中心节点组成内层模块；其次，对内层模块的非中心节点进行重要度排序，依次寻找耦合系数最大的点组成中间层模块；然后，对中间层模块的节点进行重要度排序，依次寻找耦合系数最大的点组成外层模块；最后，由其它节点组成边缘层模块。
  8. 根据权利要求2所述的自动驾驶汽车的认知***，其特征在于，所述全局风险态势认知模块中，所述***熵设计为：
    S＝V n/Θ+D(P)+D(U)  (8)
    其中，V n为复杂环境模型的节点数量，Θ为复杂环境模型的网络区域，D(P)表示耦合系数的方差，D(U)为复杂环境模型中节点速度的方差；
    所述熵变设计为：
    Figure PCTCN2022070671-appb-100006
    其中，d表示计算相应变量的微分，表示其变化趋势。
  9. 基于复杂网络的自动驾驶汽车认知***的认知方法，其特征在于，包括如下步骤：
    步骤1)提取纵向驾驶特征参数、横向驾驶特征参数和模式转移特征参数，构造驾驶风格特征矩阵C J，生成随机森林分类器R f，将驾驶风格特征矩阵C J输入随机森林分类 器R f，随机森林分类器R f的输出驾驶风格类别K drive，将驾驶风格识别为激进型、平和型、保守型三类；
    步骤2)构造时变复杂动态网络G作为复杂环境模型，用于描述复杂环境整体关联特征，进一步建立复杂环境模型中的节点动力学方程，再组合时变复杂动态网络G中所有节点的特征形成动态方程向量F(X)、时变复杂动态网络G中节点间的耦合矩阵P(t)和节点的内联向量H(X)，建立时变复杂动态网络G的节点***动力学方程，用于描述复杂环境的动态特性；
    步骤3)构造复杂环境模型中节点的量g i、度k i、点权s i和重要度I(i)四个参数，并用正态分布图对节点进行差异化分析，实现节点差异化认知；
    步骤4)采用凝聚算法对复杂环境模型中节点进行层次划分，实现对自动驾驶汽车复杂环境的层次化、阶梯性认知；
    步骤5)依据熵理论的基本思想，用***熵和熵变对复杂环境模型的无序程度进行度量，描述整体风险及变化态势，实现对全局共性的状态认知。
  10. 根据权利要求9所述的认知方法，其特征在于，所述基于复杂网络的自动驾驶汽车认知***为权利要求2-8任一项所述的认知***。
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