Driving rule extraction based on cognitive behavior analysis

作     者：ZHAO Yu-cheng LIANG Jun CHEN Long CAI Ying-feng YAO Ming HUA Guo-dong ZHU Ning 赵宇橙;梁军;陈龙;蔡英凤;姚明;华国栋;朱宁

作者机构：Automotive Engineering Research InstituteJiangsu UniversityZhenjiang 212013China School of Automotive and Transportation EngineeringJiangsu UniversityZhenjiang 212013China Jiangsu Zhixing Future Automobile Research InstituteNanjing 210000China Department of Mechanical EngineeringShizuoka University of Science and TechnologyShizuoka Bagu 437-0032Japan 

出 版 物：《Journal of Central South University》 (中南大学学报（英文版）)

年 卷 期：2020年第27卷第1期

页      面：164-179页

核心收录：

学科分类：082304[工学-载运工具运用工程] 08[工学] 080204[工学-车辆工程] 0802[工学-机械工程] 0823[工学-交通运输工程] 

基　　金：Project(2017YFB0102503)supported by the National Key Research and Development Program of China Projects(U1664258,51875255,61601203)supported by the National Natural Science Foundation of China Projects(DZXX-048,2018-TD-GDZB-022)supported by the Jiangsu Province’s Six Talent Peak,China Project(18KJA580002)supported by Major Natural Science Research Project of Higher Learning in Jiangsu Province,China 

主　　题：cognitive driving behavior driving rule extraction cognitive theory integrated algorithm 

摘      要：In order to make full use of the driver’s long-term driving experience in the process of perception, interaction and vehicle control of road traffic information, a driving behavior rule extraction algorithm based on artificial neural network interface(ANNI) and its integration is proposed. Firstly, based on the cognitive learning theory, the cognitive driving behavior model is established, and then the cognitive driving behavior is described and analyzed. Next, based on ANNI, the model and the rule extraction algorithm(ANNI-REA) are designed to explain not only the driving behavior but also the non-sequence. Rules have high fidelity and safety during driving without discretizing continuous input variables. The experimental results on the UCI standard data set and on the self-built driving behavior data set, show that the method is about 0.4% more accurate and about 10% less complex than the common C4.5-REA, Neuro-Rule and REFNE. Further, simulation experiments verify the correctness of the extracted driving rules and the effectiveness of the extraction based on cognitive driving behavior rules. In general, the several driving rules extracted fully reflect the execution mechanism of sequential activity of driving comprehensive cognition, which is of great significance for the traffic of mixed traffic flow under the network of vehicles and future research on unmanned driving.