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A Generalized Method to Extract Visual Time-Sharing Sequences From Naturalistic Driving Data
Swedish National Rd and Transport Research Institute VTI, S-58195 Linkoping, Sweden.
Linköping University, Department of Behavioural Sciences and Learning, Psychology. Linköping University, Faculty of Arts and Sciences. Swedish National Rd and Transport Research Institute VTI, S-58195 Linkoping, Sweden.
2017 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 18, no 11, 2929-2938 p.Article in journal (Refereed) Published
Abstract [en]

Indicators based on visual time-sharing have been used to investigate drivers visual behaviour during additional task execution. However, visual time-sharing analyses have been restricted to additional tasks with well-defined temporal start and end points and a dedicated visual target area. We introduce a method to automatically extract visual time-sharing sequences directly from eye tracking data. This facilitates investigations of systems, providing continuous information without well-defined start and end points. Furthermore, it becomes possible to investigate time-sharing behavior with other types of glance targets such as the mirrors. Time-sharing sequences are here extracted based on between-glance durations. If glances to a particular target are separated by less than a time-based threshold value, we assume that they belong to the same information intake event. Our results indicate that a 4-s threshold is appropriate. Examples derived from 12 drivers (about 100 hours of eye tracking data), collected in an on-road investigation of an in-vehicle information system, are provided to illustrate sequence-based analyses. This includes the possibility to investigate human-machine interface designs based on the number of glances in the extracted sequences, and to increase the legibility of transition matrices by deriving them from time-sharing sequences instead of single glances. More object-oriented glance behavior analyses, based on additional sensor and information fusion, are identified as the next future step. This would enable automated extraction of time-sharing sequences not only for targets fixed in the vehicles coordinate system, but also for environmental and traffic targets that move independently of the drivers vehicle.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2017. Vol. 18, no 11, 2929-2938 p.
Keyword [en]
Driver behaviour; glance analysis; visual time-sharing
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:liu:diva-143082DOI: 10.1109/TITS.2017.2658945ISI: 000414070100004OAI: oai:DiVA.org:liu-143082DiVA: diva2:1159435
Note

Funding Agencies|European Commission [288611]; Swedish Energy Agency

Available from: 2017-11-22 Created: 2017-11-22 Last updated: 2017-11-22

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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