@article{ref1,
title="Measuring driver perception: combining eye-tracking and automated road scene perception",
journal="Human factors",
year="2020",
author="Stapel, Jork and El Hassnaoui, Mounir and Happee, Riender",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="OBJECTIVE: To investigate how well gaze behavior can indicate driver awareness of individual road users when related to the vehicle's road scene perception.   BACKGROUND: An appropriate method is required to identify how driver gaze reveals awareness of other road users.   METHOD: We developed a recognition-based method for labeling of driver situation awareness (SA) in a vehicle with road-scene perception and eye tracking. Thirteen drivers performed 91 left turns on complex urban intersections and identified images of encountered road users among distractor images.   RESULTS: Drivers fixated within 2° for 72.8% of relevant and 27.8% of irrelevant road users and were able to recognize 36.1% of the relevant and 19.4% of irrelevant road users one min after leaving the intersection. Gaze behavior could predict road user relevance but not the outcome of the recognition task. Unexpectedly, 18% of road users observed beyond 10° were recognized.   CONCLUSIONS: Despite suboptimal psychometric properties leading to low recognition rates, our recognition task could identify awareness of individual road users during left turn maneuvers. Perception occurred at gaze angles well beyond 2°, which means that fixation locations are insufficient for awareness monitoring.  APPLICATION: Findings can be used in driver attention and awareness modelling, and design of gaze-based driver support systems.<p /> <p>Language: en</p>",
language="en",
issn="0018-7208",
doi="10.1177/0018720820959958",
url="http://dx.doi.org/10.1177/0018720820959958"
}