The transition period from automation to manual, known as the takeover process, presents challenges for drivers due to the deficiency in collecting requisite contextual information. The current study collected drivers' eye movement in a simulated takeover experiment, and their Situation Awareness (SA) was assessed using the Situation Awareness Global Assessment Technique (SAGAT) method. The drivers' Stationary Gaze Entropy (SGE) was calculated based on the percentages of time they spent on six pre-defined Areas of Interests (AOIs). Three critical time windows were extracted by using the takeover alert time spot and the hazard perceived time spot. The result indicated that drivers with higher SAGAT scores would spread their attention among multiple AOIs. Also, drivers' SGE and SA have a linear relationship only at the last time window (hazard perceived to the end) wherein SGE potentially functions as an evaluative metric for assessing SA in the future.
Keywords: eye tracking; level-3 autonomous driving; situation awareness; stationary gaze entropy.
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