Full metadata
Title
Buzz or Beep? How Mode of Alert Influences Driver Takeover Following Automation Failure
Description
Highly automated vehicles require drivers to remain aware enough to takeover
during critical events. Driver distraction is a key factor that prevents drivers from reacting
adequately, and thus there is need for an alert to help drivers regain situational awareness
and be able to act quickly and successfully should a critical event arise. This study
examines two aspects of alerts that could help facilitate driver takeover: mode (auditory
and tactile) and direction (towards and away). Auditory alerts appear to be somewhat
more effective than tactile alerts, though both modes produce significantly faster reaction
times than no alert. Alerts moving towards the driver also appear to be more effective
than alerts moving away from the driver. Future research should examine how
multimodal alerts differ from single mode, and see if higher fidelity alerts influence
takeover times.
during critical events. Driver distraction is a key factor that prevents drivers from reacting
adequately, and thus there is need for an alert to help drivers regain situational awareness
and be able to act quickly and successfully should a critical event arise. This study
examines two aspects of alerts that could help facilitate driver takeover: mode (auditory
and tactile) and direction (towards and away). Auditory alerts appear to be somewhat
more effective than tactile alerts, though both modes produce significantly faster reaction
times than no alert. Alerts moving towards the driver also appear to be more effective
than alerts moving away from the driver. Future research should examine how
multimodal alerts differ from single mode, and see if higher fidelity alerts influence
takeover times.
Date Created
2018
Contributors
- Brogdon, Michael A (Author)
- Gray, Robert (Thesis advisor)
- Branaghan, Russell (Committee member)
- Chiou, Erin (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
26 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.51699
Level of coding
minimal
Note
Masters Thesis Human Systems Engineering 2018
System Created
- 2019-02-01 07:03:52
System Modified
- 2021-08-26 09:47:01
- 3 years 3 months ago
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