Driver´s cognitive distraction detection using physiological features by the adaboost

Effects of driver´s states adaptive driving support systems is highly expected for the prevention of traffic accidents. In order to create this constituent technology, detecting driver´s psychosomatic states which occurs just before a traffic accident is essential. Therefore driver´s distraction is thought as one of important factors. This study focused on detecting driver´s cognitive distraction, a state which can easily lead to a traffic accident. We reproduced the cognitive distraction by imposing conversation or arithmetic loads to the subjects on a driving simulator. A stereo camera system were used as the means to track a subject´s eyes, and head movements, which were set as classification features for pattern recognition on the support vector machine (hereafter, SVM) basis used in the previous study of the AIDE project, a part of EU 6th framework programme. Diameter of pupil as well as the interval between heart R-waves (hereafter, heart rate RRI) from an ECG (electrocardiogram) were added for classification features to further improve the accuracy of driver´s cognitive distraction detection. Based on this study, we established the methodology for more precise and faster driver´s cognitive detection by using the AdaBoost.