Self-Assessing Visual Attention While Driving: Implications for Driver Monitoring Systems
Year: 2025
Type: conference-paper
Abstract: Driver Monitoring Systems (DMS) are now a requirement in all new vehicles. DMS aim to reduce crashes and improve driver attention by providing warnings or interventions to the driver. However, driver acceptance is crucial to ensuring their effectiveness. Various methods exist to detect inattention, but if these do not align with a driver's mental model, acceptance issues may arise regarding the warnings provided. The study outlined in this paper examines how drivers assess their own visual attention by comparing self-reported ratings to a visual attention algorithm. Using an on-road experiment with six participants, initial results highlight the importance of contextual information for accurately assessing visual attention and providing effective warning strategies.
Authors: Large, David, Harding, Max, Harvey, Catherine, Burnett, Gary, Hare, Chrisminder, Proctor, Karl
Language: English
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7062263271
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