  Modern advanced driver-assistance systems analyze the driving performance to
gather information about the driver's state. Such systems are able, for
example, to detect signs of drowsiness by evaluating the steering or lane
keeping behavior and to alert the driver when the drowsiness state reaches a
critical level. However, these kinds of systems have no access to direct cues
about the driver's state. Hence, the aim of this work is to extend the driver
drowsiness detection in vehicles using signals of a driver monitoring camera.
For this purpose, 35 features related to the driver's eye blinking behavior and
head movements are extracted in driving simulator experiments. Based on that
large dataset, we developed and evaluated a feature selection method based on
the k-Nearest Neighbor algorithm for the driver's state classification. A
concluding analysis of the best performing feature sets yields valuable
insights about the influence of drowsiness on the driver's blink behavior and
head movements. These findings will help in the future development of robust
and reliable driver drowsiness monitoring systems to prevent fatigue-induced
accidents.
