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A Gaze-Based Driver Distraction Warning System and Its Effect on Visual Behavior
Swedish National Road and Transport Research Institute, Linköping, Sweden.
Swedish National Road and Transport Research Institute, Linköping, Sweden.ORCID iD: 0000-0002-1849-9722
Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
2013 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 14, no 2, p. 965-973Article in journal (Refereed) Published
Abstract [en]

Driver distraction is a contributing factor to many crashes; therefore, a real-time distraction warning system should have the potential to mitigate or circumvent many of these crashes. The objective of this paper is to investigate the usefulness of a real-time distraction detection algorithm called AttenD. The evaluation is based on data from an extended field study comprising seven drivers who drove on an average of 4351 +/- 2181 km in a naturalistic setting. Visual behavior was investigated both on a global scale and on a local scale in the surroundings of each warning. An increase in the percentage of glances at the rear-view mirror and a decrease in the amount of glances at the center console were found. The results also show that visual time sharing decreased in duration from 9.94 to 9.20 s due to the warnings, that the time from fully attentive to warning decreased from 3.20 to 3.03 s, and that the time from warning to full attentiveness decreased from 6.02 to 5.46 s. The limited number of participants does not allow any generalizable conclusions, but a trend toward improved visual behavior could be observed. This is a promising start for further improvements of the algorithm and the warning strategy.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2013. Vol. 14, no 2, p. 965-973
Keywords [en]
Distraction warning; driver distraction; eye movements; eye tracking; field study
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-96122DOI: 10.1109/TITS.2013.2247759ISI: 000319828800043OAI: oai:DiVA.org:liu-96122DiVA, id: diva2:640767
Available from: 2013-08-14 Created: 2013-08-14 Last updated: 2018-03-08Bibliographically approved

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Kircher, Katja

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