Using Eye-Tracking to Help Design HUD-Based Safety Indicators for Lane Changes

Abstract

Lane changing can slow down traffic flow and play an important role in traffic accidents, especially because the drivers' eyes-off road time and awareness of the rearward road scene are critical. Our work focuses on visual aids to reduce risks in such situations. During a real-world driving test of performing lane changes, we collected eye-tracking data and analysed the drivers' eye movement in lane changing scenarios, considering fixation areas and fixation moving paths frequency. The idea of this methodology is to inform a potential ideal design of a HUD-based warning indicator that supports lane changes. Our future research will validate the effectiveness of using the eye tracking method to inform the positioning of indicators, and use it for supporting safety of other driving tasks and trust in higher levels of automation.