Gaze fixations and dynamics for behavior modeling and prediction of on-road driving maneuvers

From driver assistance in manual mode to takeover requests in highly automated mode, knowing the state of driver (e.g. sleeping, distracted, attentive) is critical for safe, comfortable and stress-free driving. Since driving is a visually demanding task, driver's gaze is especially important in estimating the state of driver; it has the potential to derive what the driver has attended to or is attending to and predict future actions. We developed a machine vision based framework to model driver's behavior by representing the gave dynamics over a time period using gaze fixations and transition frequencies. As a use case, we explore the driver's gaze patterns during maneuvers executed in freeway driving, namely, left lane change maneuver, right lane change maneuver and lane keep. It is shown that mapping gaze dynamics to gaze fixations and transition frequencies leads to recurring patterns based on driver activities. Furthermore, using data from on-road driving, we show that modeling these patterns show predictive powers in on-road driving maneuver detection around a few hundred milliseconds a priori.