What Attracts the Driver's Eye? Attention as a Function of Task and Events
TLDR
In this article , the authors explore how drivers of an automated vehicle distribute their attention as a function of environmental events and driving task instructions, concluding that eye movements are strongly situation-dependent, with areas of interest (windshield, mirrors, and dashboard) attracting attention when events occurred in those areas.Abstract:
This study explores how drivers of an automated vehicle distribute their attention as a function of environmental events and driving task instructions. Twenty participants were asked to monitor pre-recorded videos of a simulated driving trip while their eye movements were recorded using an eye-tracker. The results showed that eye movements are strongly situation-dependent, with areas of interest (windshield, mirrors, and dashboard) attracting attention when events (e.g., passing vehicles) occurred in those areas. Furthermore, the task instructions provided to participants (i.e., speed monitoring or hazard monitoring) affected their attention distribution in an interpretable manner. It is concluded that eye movements while supervising an automated vehicle are strongly ‘top-down’, i.e., based on an expected value. The results are discussed in the context of the development of driver availability monitoring systems.read more
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Studying Driver’s Perception Arousal and Takeover Performance in Autonomous Driving
TL;DR: Zhang et al. as discussed by the authors analyzed the change in drivers' perception level and its influence on takeover performance during autonomous driving and found that male drivers have higher perception levels than female drivers, and they prioritize leisure tasks more than professional ones.
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The Effect of Multifactor Interaction on the Quality of Human–Machine Co-Driving Vehicle Take-Over
TL;DR: In this article , the effects of non-driving related tasks, take-over request time, and takeover mode interactions on takeover performance in human-machine cooperative driving in a highway environment were investigated.
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Where drivers are looking at during takeover: Implications for safe takeovers during conditionally automated driving.
TL;DR: In this paper , a study involving 27 subjects was conducted on a high-fidelity driving simulator with a Steward motion platform of six degrees of freedom, with their maneuvers recorded by the system and eye gazes recorded by Smart Eye Pro and Smart Recorder.
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