Passenger physiology in self-driving vehicles during unexpected events

Abstract While using fully autonomous vehicles is expected to radically change the way we live our daily lives, it is not yet available in most parts of the world, so we only have sporadic results on passenger reactions. Furthermore, we have very limited insights into how passengers react to an unex...

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Bibliographic Details
Main Authors: Zsolt Palatinus, Miklós Lukovics, Márta Volosin, Zsolt Dudás, Szabolcs Prónay, Zoltán Majó-Petri, Henrietta Lengyel, Zsolt Szalay
Format: Article
Language:English
Published: Nature Portfolio 2025-03-01
Series:Scientific Reports
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Online Access:https://doi.org/10.1038/s41598-024-81960-4
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Summary:Abstract While using fully autonomous vehicles is expected to radically change the way we live our daily lives, it is not yet available in most parts of the world, so we only have sporadic results on passenger reactions. Furthermore, we have very limited insights into how passengers react to an unexpected event during the ride. Previous physiological research has shown that passengers have lower levels of anxiety in the event of a human-driven condition compared to a self-driving condition. The aim of our current study was to investigate these differences in unexpected road events in real-life passenger experiences. All subjects were driven through a closed test track in human-driven and then self-driving mode. During the journey, unforeseen obstacles were encountered on the path (deer and human-shaped dummies appeared). Using physiological measurements (EEG, eye movements, head movements and blinking frequencies) our results suggest that passengers had moderate affective preferences for human-driven conditions. Furthermore, multifractal spectra of eye movements and head movements were wider and blinking frequencies were decreased during unexpected events. Our findings further establish real-world physiological measurements as a source of information in researching the acceptance and usage of self-driving technologies.
ISSN:2045-2322