An Analytical Model for Estimating Average Driver Attention Based on the Visual Field

Abstract

The direction of a driver’s visual attention plays a crucial role in the context of Advanced Driver Assistance Systems (ADASs) and semi-autonomous driving. The way a driver monitors traffic scene objects partially indicates the level of driver awareness. We propose an analytical method to estimate a driver’s average traffic scene attention based on the attentional visual field of the driver in urban and suburban areas. Three metrics are proposed to estimate a driver’s average attention. Our model is capable of identifying driver attention with respect to traffic objects including vehicles, traffic lights, traffic signs, and pedestrians within the attentional visual field of the driver at any moment while in the act of driving.