ArticleFrontiers in neuroergonomics2021
Eye Tracking in Driver Attention Research-How Gaze Data Interpretations Influence What We Learn.
Article in Frontiers in neuroergonomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Gaze Allocation and Performance Across Task-Demand Conditions During Squat-Based Exergaming: Pilot Study Using Eye Tracking.JMIR rehabilitation and assistive technologies · 2026Article
- Effects of Cognitive, Simulator, and Real-World Training on Novice Driver Gaze Behaviour: A Pre-Post Study.Journal of eye movement research · 2026Article
- Persistent visual impairments following mild-to-moderate ischemic stroke.Frontiers in ophthalmology · 2025Article
- Road Hazard Stimuli: Annotated naturalistic road videos for studying hazard detection and scene perception.Behavior research methods · 2024Article
- Article
- Augmented Recognition of Distracted Driving State Based on Electrophysiological Analysis of Brain Network.Cyborg and bionic systems (Washington, D.C.) · 2024Article
- Investigation of Camera-Free Eye-Tracking Glasses Compared to a Video-Based System.Sensors (Basel, Switzerland) · 2023Article
- A Systematic Review of Eye-Tracking Studies of Construction Safety.Frontiers in neuroscience · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Eye tracking (ET) has been used extensively in driver attention research. Amongst other findings, ET data have increased our knowledge about what drivers look at in different traffic environments and how they distribute their glances when interacting with non-driving related tasks. Eye tracking is also the go-to method when determining driver distraction via glance target classification. At the same time, eye trackers are limited in the sense that they can only objectively measure the gaze direction. To learn more about why drivers look where they do, what information they acquire foveally and peripherally, how the road environment and traffic situation affect their behavior, and how their own expertise influences their actions, it is necessary to go beyond counting the targets that the driver foveates. In this perspective paper, we suggest a glance analysis approach that classifies glances based on their
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Registered trials
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