Characterizing Drivers’ Peripheral Vision via the Functional Field of View for Intelligent Driving Assistance

Many intelligent driver assistance algorithms try to improve on-road safety by using driver eye gaze, commonly using foveal gaze as an estimate of human attention. While human visual acuity is highest in the foveal field of view, drivers often use their peripheral vision to process scene elements. Previous work in psychology has modeled this combination of foveal and peripheral gaze as a construct known as Functional Field of View (FFoV). In this work, we study the shape and dynamics of the FFoV during active driving. We use a peripheral detection task in a virtual reality (VR) driving simulator with licensed drivers in urban driving settings. We find evidence that supports a vertically asymmetric (upward-inhibited) shape of the FFoV in our active driving task, similar to previous work in non-driving settings. Additionally, we show that this asymmetry disappears when the same peripheral detection task is conducted in a non-driving setting. Finally, we also examine the dynamic nature of the FFoV. Our data indicates that drivers’ peripheral target detection ability is inhibited right after saccades but recovers once drivers fixate for some time. The findings of the FFoV’s task-dependent nature as well as systematic asymmetries and inhibitions have implications for gaze-based intelligent driving assistance systems.