Abstract
ObjectiveTo investigate how well gaze behavior can indicate driver awareness of individual road users when related to the vehicle’s road scene perception.BackgroundAn appropriate method is required to identify how driver gaze reveals awareness of other road users.MethodWe 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.ResultsDrivers 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.ConclusionsDespite 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.ApplicationFindings can be used in driver attention and awareness modelling, and design of gaze-based driver support systems.
Highlights
Perceptual errors contribute 76% of situation awareness (SA) errors (Jones & Endsley, 1996) and are among the most frequently reported causes for accidents at intersections, which represent 20% of European road accidents (European Road Safety Observatory, 2018)
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
Despite suboptimal psychometric properties leading to low recognition rates, our recognition task could identify awareness of individual road users during left turn maneuvers
Summary
Perceptual errors contribute 76% of situation awareness (SA) errors (Jones & Endsley, 1996) and are among the most frequently reported causes for accidents at intersections, which represent 20% of European road accidents (European Road Safety Observatory, 2018). Machine perception can locate road users through detection and classification systems (Kooij et al, 2014; Liu et al, 2016). It processes raw sensor data in a series of filters trained to extract features, which collectively capture the concept of an object category. Machine perception has superior attention in detection tasks It can process the entire road scene without constraining to a region to attend, and does not suffer from vigilance decrement or biases from expectations. An appropriate method is required to identify how driver gaze reveals awareness of other road users
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