Individual Differences in Glance Patterns under Distraction in Level 2 Automated Driving

Yang, Shiyan; Kuo, Jonny; Lenné, Michael G.

doi:10.1177/1071181320641477

Cited by 3 publications

(1 citation statement)

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“…Previous studies uncovered the patterns of visual timesharing between on-road and off-road states in cell phone distraction (Yang, Kuo, & Lenné, 2019) and found individual differences in the intensity of visual time-sharing: infrequent long glances vs. frequent short glances (Yang, Kuo, & Lenné, 2020b). But it remains unclear which glance strategy -owl or lizard -is used to obtain information from the cell phone, especially in assisted driving (SAE Level 2) where reliance on vehicle automation leads drivers to engage more in the secondary tasks.…”

Section: Introductionmentioning

confidence: 99%

Drivers Glance Like Lizards during Cell Phone Distraction in Assisted Driving

Yang¹,

Shiferaw²,

Roady³

et al. 2021

Proceedings of the Human Factors and Ergonomics Society Annual

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Head pose has been proposed as a surrogate for eye movement to predict areas of interest (AOIs) where drivers allocate their attention. However, head pose may disassociate with AOIs in glance behavior involving zero or subtle head movements, commonly known as “lizard” glance pattern. In contrast, “owl” glance pattern is used to describe glance behavior along with larger head movements. It remains unclear which glance pattern is prevalent during driver cell phone distraction and what are appropriate metrics to detect such distraction. To address this gap, we analyzed the gaze direction and head pose of 36 participants who completed an email-sorting task using a cell phone while driving a Tesla on the test track in Autopilot mode. The dispersion-threshold algorithm identified driver gaze fixations and synchronized them with head movements. The results showed that when using a cell phone either near the lap or behind the steering wheel, participants exhibited a dominant lizard-type glance pattern with minimal shift in head position. As a result, head pose alone may not provide sufficient information for cell phone distraction detection, and gaze metrics should be involved in enhancing this application.

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Section: Introductionmentioning

confidence: 99%