Unsupervised Blink Detection and Driver Drowsiness Metrics on Naturalistic Driving Data

Driver drowsiness detection has always been center to research whether for accident risk minimization or recently for driver monitoring in the stages towards automated driving. In this work we analyse videos of visibly alert and less alert drivers collected within a naturalistic driving study in terms of different visual drowsiness metrics. The facial landmark method allows to compute the eye aperture remotely without additional wearables. From this an unsupervised blink detection algorithm is introduced that competes with other supervised methods on benchmark datasets. Common fatigue metrics such as blink rate are considered. We show that there is a significant difference in blink rate between different driver groups and also discuss fatigue levels during the course of a cruise. More importantly, we show that the distribution of eye aperture already displays valuable information on the driver's blinking patterns without the actual need to derive a blink detection system in the first place.