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Processing of eye/head-tracking data in large-scale naturalistic driving data sets
Swedish National Road and Transport Research Institute, Traffic and road users, Human-vehicle-transport system interaction.ORCID iD: 0000-0003-4134-0303
Chalmers University of Technology. (SAFER Vehicle & Traffic Safety)
SP Technical Research Institute Sweden.
2012 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 13, no 2, 553-564 p.Article in journal (Refereed) Published
Abstract [en]

Driver distraction and driver inattention are frequently recognized as leading causes of crashes and incidents. Despite this fact, there are few methods available for the automatic detection of driver distraction. Eye tracking has come forward as the most promising detection technology, but the technique suffers from quality issues when used in the field over an extended period of time. Eye-tracking data acquired in the field clearly differs from what is acquired in a laboratory setting or a driving simulator, and algorithms that have been developed in these settings are often unable to operate on noisy field data. The aim of this paper is to develop algorithms for quality handling and signal enhancement of naturalistic eye- and head-tracking data within the setting of visual driver distraction. In particular, practical issues are highlighted. Developed algorithms are evaluated on large-scale field operational test data acquired in the Sweden-Michigan Field Operational Test (SeMiFOT) project, including data from 44 unique drivers and more than 10000 trips from 13 eye-tracker-equipped vehicles. Results indicate that, by applying advanced data-processing methods, sensitivity and specificity of eyes-off-road glance detection can be increased by about 10%. In conclusion, postenhancement and quality handling is critical when analyzing large databases with naturalistic eye-tracking data. The presented algorithms provide the first holistic approach to accomplish this task. © 2011 IEEE.

Place, publisher, year, edition, pages
2012. Vol. 13, no 2, 553-564 p.
Keyword [en]
Eye movement, Distraction, Detection, Data processing
National Category
Signal Processing
Research subject
Road: Traffic safety and accidents, Road: Road user behaviour
Identifiers
URN: urn:nbn:se:vti:diva-274DOI: 10.1109/TITS.2011.2174786ISI: 000304907000013OAI: oai:DiVA.org:vti-274DiVA: diva2:662672
Funder
Vinnova
Available from: 2013-11-08 Created: 2013-11-08 Last updated: 2014-09-23Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf