Measuring Driver Perception
Combining Eye-Tracking and Automated Road Scene Perception
Jork Stapel (TU Delft - Intelligent Vehicles)
Mounir El Hassnaoui (Student TU Delft)
R Happee (TU Delft - Intelligent Vehicles)
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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.
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