Risk Status Identification During the Takeover of Conditionally Automated Vehicles
作者单位：School of AutomationChongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education
会议名称：《第32届中国控制与决策会议》
会议届次：32
主办单位：IEEE Control Systems Society (CSS);Northeastern University;State Key Laboratory of Synthetical Automation for Process Industries;Technical Committee on Control Theory, Chinese Association of Automation
会议日期：2020年
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：National Key R&D Program Natural Science Foundation of Chongqing, (cstc2017jcyjBX0001) National Key Research and Development Program of China, NKRDPC, (2016YFB0100904)
关 键 词：Automated Driving Experimental Research Takeover Risk Status Identification Driving Simulator
摘 要：In order to identify the risk status during the takeover from automated driving to manual driving in dangerous traffic situations,this study investigated their performance during the takeover based on *** experiment invited 38 *** all held a valid driving license.A risk status identification model was proposed based on the information of vehicle status and traffic environment *** to the rate of electrocardiogram（ECG） and the performance of takeover,the risk status was classified into three *** Pearson correlation coefficient algorithm,seven factors were selected as the feature ***,the algorithm of Random Forest（RF） was employed to establish the takeover risk status identification *** results show that the accuracy of RF is 98.8%,increasing 10.4%,17.7% and 7.3% compared with Support Vector Machine（SVM）,Classification and Regression Tree（CART） and Back Propagation Neural Network,and each risk level has good prediction ***,the results show that the space headway,longitudinal acceleration and lane departure have a great influence on the risk level,and the space headway has the strongest impact on the degree of danger during the takeover of control in Conditionally Automated Vehicles.