Identification of cognitive distraction using physiological features for adaptive driving safety supporting system

Abstract

It was identified that traffic accidents relate closely to the driver's mental and physical states immediately before the accident by our questionnaire survey. Distraction is one of the key human factors involved in traffic accidents. We reproduced driver's cognitive distraction on a driving simulator by means of imposing cognitive loads such as doing arithmetic and having conversation while driving. Visual features such as test subjects' gaze direction, pupil diameter, and head orientation, together with heart rate from ECG, were used in this study to detect the cognitive distraction. We improved detection accuracy obtained from earlier studies by using the AdaBoost. This paper also suggests a multiclass identification using Error-Correcting Output Coding, which can identify the degree of cognitive load. Finally, we verified the effectiveness of the multiclass identification by conducting a series of experiments. All these aimed at developing a constituent technology of a driver monitoring system that is expected to create adaptive driving safety supporting system to lower the number of traffic accidents. © 2013 Haruki Kawanaka et al.