Abstract
Many car accidents are caused by driver's deviation from normal condition like carelessness. We aim to construct a driving assist system that is able to detect driver's deviation signal from normal condition. The system detects the deviation signal using driver's time-series head motion information. In this paper, we analyze driving movies taken by monocular in-vehicle camera, and examine driver's head position category in safety verification at intersections for quantification of head motion information. Moreover, we propose a quantifiable categorizing algorithm of head motion using two kinds of unsupervised neural networks. Through an experiment on actual driving data, the results provide a possibility of quantification of individual head position in safety verifications.
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