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Processing of Eye/Head-Tracking Data in Large-Scale Naturalistic Driving Data Sets

Christer Ahlström ; Trent Victor (SAFER - Fordons- och Trafiksäkerhetscentrum ) ; Claudia Wege ; Erik Steinmetz (Institutionen för signaler och system, Kommunikationssystem)
IEEE transactions on intelligent transportation systems (1524-9050). Vol. vol.13 (2012), no.2, p. pp.553-564.
[Artikel, refereegranskad vetenskaplig]

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 10 000 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.

Nyckelord: Dispersion, Interpolation, Reliability, Roads, Smoothing methods, Vehicles, Visualization, driver information systems, eye, object tracking, road accidents, road safety, signal processing, very large databases, SeMiFOT project, Sweden-Michigan field operational test, crashes, detection technology, driver distraction automatic detection, driver inattention, eye-tracker-equipped vehicle, eye/head-tracking data processing, eyes-off-road glance detection, incidents, large database, large-scale naturalistic driving data set, naturalistic eye-and head-tracking data, quality handling, sensitivity, signal enhancement, visual driver distraction, Data processing, driver distraction, eye tracking, naturalistic data,

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Denna post skapades 2012-12-21. Senast ändrad 2016-01-12.
CPL Pubid: 168547


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