Driver Gaze Fixation and Pattern Analysis in Safety Critical Events

Driver attention is one of the major factors for roadway safety. This paper presents a comprehensive analysis of context-aware driver gaze behavior using large-scale naturalistic data. This paper first demonstrates a Point of Gaze (PoG) estimation system that provides an understanding of the interactions between a driver and the driving environment outside the vehicle. The driver’s PoG positions were mapped to roadway actors that were detected using a road-facing camera, and a driver-facing camera detected the driver’s head and eye motions. This paper presents a new algorithm that can effectively predict gaze fixation to outside objects. The temporal fixation patterns of drivers are studied through a Yarbus-style analysis of PoG. Correlations between gaze direction, salient objects, and the driving scenario are described and analyzed. These correlations have the potential to reveal how drivers behave in various traffic situations and may have the predictive power to aid automated safety improvement systems. Our code is available at: https://github.com/VTTI/gaze-fixation-and-object-saliency and https://github.com/VTTI/Event-Correlation