Using eye-tracking to investigate the effects of pre-takeover visual engagement on situation awareness during automated driving. (July 2021) - Liang, Nade; Yang, Jing; Yu, Denny; Prakah-Asante, Kwaku O.; Curry, Reates; Blommer, Mike; Swaminathan, Radhakrishnan; Pitts, Brandon J.
Highlights: In conditionally automated vehicles, drivers may engage in non-driving-related tasks (NDRTs). Engagement in NDRTs may limit drivers' situation awareness (SA) when takeover is needed. Eye-tracking was used in a driving simulator study to understand the effects of visual engagement in NDRTs on SA. Longer times spent viewing the driving scene, and more dispersed visual attention allocation, resulted in better overall SA. Results can inform the development of real-time SA assessment techniques using eye movements. Abstract: Automated driving systems are becoming increasingly prevalent throughout society. In conditionally automated vehicles, drivers may engage in non-driving-related tasks (NDRTs), which can negatively affect their situation awareness (SA) and preparedness to resume control of the vehicle, when necessary. Previous work has investigated engagement in NDRTs, but questions remain unanswered regarding its effect on drivers' SA during a takeover event. The objective of the current study is to use eye-tracking to aid in understanding how visual engagement in NDRTs affects changes in SA of the driving environment after a takeover request (TOR) has been issued. Thirty participants rode in a simulated SAE Level 3 automated driving environment and engaged in three separate pre-TOR tasks (Surrogate Reference Task, Monitoring Task, and Peripheral Detection Task) until presented with a TOR. Situation Awareness Global Assessment Technique (SAGAT) scores and gaze behavior were recorded during the post-TOR segment. Overall, longer times spent viewing the driving scene, and more dispersed visual attention allocation, were observed to be associated with better overall SA. Also, location-based eye tracking metrics show most promise in differentiating between task conditions with significantly different SAGAT scores. Findings from this work can inform the development of real-time SA assessment techniques using eye movements and ultimately contribute to improved operator roadway awareness for next-generation automated transportation.