Gaze Target Tracking for Driver Assistance Systems

Despite many supporting systems, so-called advanced driver assistance systems (ADAS), human error is still by far the main cause of traffic accidents. In the development of new driver assistance concepts, systems and functions monitoring the driver while driving and classifying their behavior in the driving context are therefore increasingly coming to the fore. In this context, this dissertation deals with the question what the driver perceived in their environment. For this purpose, the information of the environment model has to be merged with measured gaze data. Given a precise calibration of the individual sensors, visual fixations of the driver on road users are modeled. Based on the realization that simple geometric approaches cannot answer this question of visual fixations precisely enough, characteristics of human gaze behavior are identified and integrated as model knowledge into a probabilistic tracking approach.
The prototypical City Assistant System, which was co-developed as part of this work, shows how the newly gained information about the driver's gaze behavior can be incorporated into new assistance concepts. It adapts its warning and recommendation cascade in urban intersection scenarios to the driver's driving style and gaze behavior. Through this orientation towards the driver's need for support, the City Assistant System contributes to higher acceptance of warning and recommending systems and ultimately to increased road safety.