EEG-based prediction of driving events from passenger cognitive state using Morlet Wavelet and Evoked Responses

This paper examines the predictability of driving events by passenger electroencephalography (EEG) data using Morlet wavelets and Evoked Responses (ER). As autonomous vehicles (AV) become more popular and the technology becomes more widely available, a major remaining challenge to overcome is passenger mistrust in the AV. People generally want to be in control of their vehicle, which is why human event predictability is such an important factor in AVs. This research study successfully predicted 93.61% of driving events including aggressive acceleration, braking, and lane changes on an open-road driving route.

• Record URL:
  https://doi.org/10.1016/j.treng.2022.100107
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S2666691X22000057
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/2666691X
• Supplemental Notes:
  • © 2022 Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Belcher, Morgan A
  • Hwang, InChan
  • Bhattacharya, Sylvia
  • Hairston, W David
  • Metcalfe, Jason S
• Publication Date: 2022-6

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: 100107
• Serial:
  • Transportation Engineering
  • Volume: 8
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2666-691X
  • Serial URL: https://www.journals.elsevier.com/transportation-engineering/
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Alertness; Autonomous vehicles; Behavior; Electroencephalography; Passengers; Traffic safety
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01840496
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 28 2022 10:28AM