Investigating the relationships between gaze patterns, dynamic vehicle surround analysis, and driver intentions

Recent advances in driver behavior analysis for active safety have led to the ability to reliably predict certain driver intentions. Specifically, researchers have developed advanced driver assistance systems that produce an estimate of a driver's intention to change lanes, make an intersection turn, or brake, several seconds before the act itself. One integral feature in these systems is the analysis of driver visual search prior to a maneuver, using head pose and eye gaze as a proxy to determine focus of attention. However it is not clear whether visual distractions during a goal-oriented visual search could change the driver's behavior and thereby cause a degradation in the performance of the behavior analysis systems. In this paper we aim to determine whether it is feasible to use computer vision to determine whether a driver's visual search was affected by an external stimulus. A holistic ethnographic driving dataset is used as a basis to generate a motion-based visual saliency map of the scene. This map is correlated with predetermined eye gaze data in situations where a driver intends to change lanes. Results demonstrate the capability of this methodology to improve driver attention and behavior estimation, as well as intent prediction.