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A Generalized Method to Extract Visual Time-Sharing Sequences From Naturalistic Driving Data
Swedish National Road and Transport Research Institute, Traffic and road users, Human Factors in the Transport System.ORCID iD: 0000-0003-4134-0303
Swedish National Road and Transport Research Institute, Traffic and road users, Human Factors in the Transport System. Linköpings Universitet.ORCID iD: 0000-0002-1849-9722
2017 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 18, no 11, p. 2929-2938Article 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 vehicle's coordinate system, but also for environmental and traffic targets that move independently of the driver's vehicle.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. Vol. 18, no 11, p. 2929-2938
Keywords [en]
Vision, Behaviour, Method, Measurement, Data acquisition, Eye movement
National Category
Transport Systems and Logistics
Research subject
80 Road: Traffic safety and accidents, 841 Road: Road user behaviour
Identifiers
URN: urn:nbn:se:vti:diva-12509DOI: 10.1109/TITS.2017.2658945ISI: 000414070100004Scopus ID: 2-s2.0-85016486695OAI: oai:DiVA.org:vti-12509DiVA, id: diva2:1158126
Available from: 2017-11-17 Created: 2017-11-17 Last updated: 2017-12-04Bibliographically approved

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