A road hypnosis identification method for drivers based on fusion of biological characteristics  

作　　者：Longfei Chen Jingheng Wang Xiaoyuan Wang Bin Wang Han Zhang Kai Feng Gang Wang Junyan Han Huili Shi 

机构地区：[1]College of Electromechanical Engineering,Qingdao University of Science and Technology,Qingdao 266000,China [2]Department of Mathematics,Ohio State University,Columbus,Ohio 43220,USA

出　　处：《Digital Transportation and Safety》2024年第3期144-154,共11页数字交通与安全（英文）

基　　金：supported by the New Generation of Information Technology Innovation Project of China University Innovation Fund of Ministry of Education(Grant No.2022IT191);the Qingdao Top Talent Program of Innovation and Entrepreneurship(Grant No.19-3-2-8-zhc);the project'Research and Development of Key Technologies and Systems for Unmanned Navigation of Coastal Ships'of the National Key Research and Development Program(Grant No.2018YFB1601500);the General Project of Natural Science Foundation of Shandong Province of China(Grant No.ZR2020MF082);Shandong Intelligent Green Manufacturing Technology and Equipment Collaborative Innovation Center(Grant No.IGSD-2020-012).

摘　　要：Risky driving behaviors,such as driving fatigue and distraction have recently received more attention.There is also much research about driving styles,driving emotions,older drivers,drugged driving,DUI(driving under the influence),and DWI(driving while intoxicated).Road hypnosis is a special behavior significantly impacting traffic safety.However,there is little research on this phenomenon.Road hypnosis,as an unconscious state,is can frequently occur while driving,particularly in highly predictable,monotonous,and familiar environments.In this paper,vehicle and virtual driving experiments are designed to collect the biological characteristics including eye movement and bioelectric parameters.Typical scenes in tunnels and highways are used as experimental scenes.LSTM(Long Short-Term Memory)and KNN(K-Nearest Neighbor)are employed as the base learners,while SVM(Support Vector Machine)serves as the meta-learner.A road hypnosis identification model is proposed based on ensemble learning,which integrates bioelectric and eye movement characteristics.The proposed model has good identification performance,as seen from the experimental results.In this study,alternative methods and technical support are provided for real-time and accurate identification of road hypnosis.

关 键 词：Road hypnosis State identification Active safety DRIVERS Intelligent vehicles 

分 类 号：U495[交通运输工程—交通运输规划与管理] TP391[交通运输工程—道路与铁道工程]