Study on effect of adding pupil diameter as recognition features for driver's cognitive distraction detection

Driver states adaptive drive supporting system is highly expected in use for the reduction of the number of traffic accidents. This study aimed at creating a constituent technology for detecting driver's cognitive distraction, which may be one of major factors of driver's psychosomatic states just before a traffic accident. We reproduced driver's cognitive distraction by means of imposing cognitive loads such as arithmetic and conversation to a subject on a driving simulator. Besides gaze angle, head rotation angle, and interval between heart R-waves (hereafter, heart rate RRI) from an ECG (electrocardiogram), we added pupil diameter of a subject as recognition features for pattern recognition. We established high accuracy and rapid detection methodology for driver's cognitive distraction by adopting the AdaBoost.