Method of driver state detection for safety vehicle by means of using pattern recognition

Abstract

Evolution of preventive safety devices for vehicles is highly expected to reduce the number of traffic accidents. Driver’s state adaptive driving support safety function may be one of solutions of the challenges to lower the risk of being involved in the traffic accident. In the previous study, distraction was identified as one of anormal states of a driver by introducing the Internet survey. This study reproduced driver’s cognitive distraction on a driving simulator by imposing cognitive loads, which were arithmetic and conversation. For classification of a driver’s distraction state, visual features such as gaze direction and head orientation, pupil diameter and heart rate from ECG were employed as recognition features. This study focused to acquire the best classification performance of driver’s distraction by using the AdaBoost, the SVM and Loss-based Error-Correcting Output Coding (LD-ECOC) as classification algorithm. LD-ECOC has potential to further enhance the classification capability of the driver’s psychosomatic states. Finally this study proposed next generation driver’s state adaptive driving support safety function to be extendable to Vehicle-Infrastructure cooperative safety function.