Exploring Mixed-Reality for Enhancing Driver Warning Systems: A Preliminary Study on Attention-Shifting Methods and Hazard Perception

Shih Yu Ma, Nolan Robert Brady, Xu Han, Neng Hao Yu, Tom Yeh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Modern vehicles are equipped with advanced sensors and capabilities, yet engaging human drivers effectively in hazard perception remains a challenging research area. This paper investigates the potential of mixed reality (MR) to enhance driver warning systems. The study examines the impact of attention-shifting methods, including visual cues, audio cues, and a combination of both, on drivers' hazard perception. A preliminary study involving six participants was conducted, and the NASA-TLX analysis did not yield significant differences. However, through the use of functional near-infrared spectroscopy (fNIRS), we unveil distinct brain activation patterns associated with visual and sound cues. Further research with larger sample sizes and diverse driving scenarios is required to validate and expand upon these preliminary results.

Original languageEnglish
Title of host publication15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2023 - Adjunct Conference Proceedings
PublisherAssociation for Computing Machinery
Pages87-92
Number of pages6
ISBN (Electronic)9798400701122
DOIs
StatePublished - 18 Sep 2023
Event15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2023 - Ingolstadt, Germany
Duration: 18 Sep 202321 Sep 2023

Publication series

NameACM International Conference Proceeding Series

Conference

Conference15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2023
Country/TerritoryGermany
CityIngolstadt
Period18/09/2321/09/23

Bibliographical note

Publisher Copyright:
© 2023 Owner/Author.

Keywords

  • Attention Shifting
  • Driving Simulator
  • Hazard Perception
  • Mixed Reality
  • fNIRS

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