Currently, the traffic accidents study is become important because they produce several died and hurt around the world. In this paper, a new Driver Assistance System (DAS) for automatic driver’s drowsiness detection based on visual information is presented. This algorithm works on several stages. Once is to localize the face position using Viola and Jones (VJ) object detector. Another is to loc...

Accurate classification of eye state is a prerequisite for preventing automobile accidents due to driver drowsiness. Previous methods of classification, based on features extracted for a single eye, are vulnerable to eye localization errors and visual obstructions, and most use a fixed threshold for classification, irrespective of variations in the driver’s eye shape and texture. To address the...

Partial driving automation systems execute sustained lateral and longitudinal control, while humans are required to supervise the automated features. Similar with drivers using manual settings, those may also experience drowsiness. However, existing that aim detect driver drowsiness tend be unreliable. This study applies a dual-control scheme in partial context which must keep their hands on ve...