Driving sleepiness is still a major causes of traffic accidents. Individual drivers, under various conditions, act and respond in different manners. This article presents the attempt of a straight-line driving simulator study that examined the effects of driver behavior style differences and individual differences on driver sleepiness detection which is based on driving performance measures. A total of 15 drivers who were classified into two categories through subjective assessment based on a Driver Behavior Questionnaire participated in driving simulator experiments. A total of 18 detection models, including 15 SE models for each subject, an A model for the aggressive drivers, an NA model for the non-aggressive drivers, and a G model for all experiment participants, were developed using support vector machine method based on driving performance characteristic parameters. The results show that the G model is not suitable for all drivers due to its lower mean accuracy of 69.88% (standard deviation=7.70%) and higher standard deviation. The SE models for each subject show the best detection accuracy performance of 84.26% (standard deviation=5.38%); however, it is impossible to set up a special detection model for every individual driver. The SD models on different style categories show an accuracy value of 77.54% (standard deviation=5.78%). The results demonstrate that driver style differences as well as individual differences have great effects on driver sleepiness detection (F=19.148, p<0.000).
[1]Jilin Univ, Transportat Inst, Intelligent Vehicle Grp, Changchun 130033, Peoples R China.
[2]Jilin Univ, China Japan Union Hosp, Changchun 130033, Peoples R China.
[3]Shandong Jiaotong Univ, Transportat & Logist Engn Coll, Jinan 250023, Peoples R China.
[4]Tsinghua Univ, State Key Lab Automot Safety & Energy, Beijing 100084, Peoples R China.
(8.0+极速模式)