Pupillary and attention dynamics in response to road hazard detection

• We examined hazard perception in naturalistic driving videos using eye tracking. • Pupil dilation increased ∼ 1s before hazard detection and peaked ∼ 2s after. • Pupil dilation was larger for True-Positive than for False-Positive responses. • Fixation duration increased ∼ 2s before hazard detection, especially for True-Positives. • Attention remained predominantly focal, with no significant ambient-focal shifts. Hazard perception is the ability to anticipate and respond to potentially dangerous traffic situations, which is an important aspect of driving competence. This paper analyses pupil size fluctuations, changes in fixation duration, and the dynamics of ambient/focal attention in a laboratory hazard-perception task to measure underlying attentional and cognitive mechanisms that occur when drivers detect hazards. In the task, licensed drivers (n = 42) watched videos of natural driving scenarios recorded through a dashboard camera while their eye movements were recorded. They were asked to subjectively detect hazards (via a key press), which were later classified as either True- or False-Positive responses. We analyzed the time before and after the decision about the presence of a traffic hazard. As predicted, the pupil size increased over time. The pupil response was stronger for True than False hazard responses. The significant difference in pupil size between True and False responses appeared shortly before the decision and persisted for at least three seconds after it. There was no statistically significant differences in fixation duration over time, but True-Positive responses were related with higher fixation duration compared to False positive decisions. The increase in fixation duration was greater for True-Positive compared to False-Positive decisions. The analysis of ambient and focal attention dynamics revealed that participants maintained focal attention before and after hazard detection. The results show the potential for monitoring oculometrics in assistive driver systems for the detection of distraction and hazard perception in real time.