Abstract
Automated vehicles need to monitor not only the dynamic driving environment but also the human driver’s behavior for appropriate assistance or intervention. One such challenge in observing the driver is the ability for vehicles to recognize whether the driver is aware of specific road hazards or not. As a first step toward data-driven predictive models of driver awareness, we propose a driving-video-based simulation method for empirical data collection in an ecologically valid and safe environment. A human-subject experiment conducted in a driving simulator demonstrated the potential of the proposed method. Furthermore, this work investigated the relationship between driver awareness and eye movement. Our preliminary results support that human gaze behavior may be a promising but insufficient indicator of situation awareness, and, in particular, human perception of road hazards. The proposed approach can be further used for larger-scale data collections to inform machine learning algorithms for prediction of driver awareness based on observable driver behavior and the characteristics of road hazards.