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Processing of Eye/H...
Processing of Eye/Head-Tracking Data in Large-Scale Naturalistic Driving Data Sets
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- Ahlström, Christer (författare)
- Statens väg- och transportforskningsinstitut,Samspel människa, fordon, transportsystem, MFT
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- Victor, Trent, 1968 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology,SAFER Vehicle & Traffic Safety
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- Wege, Claudia (författare)
- Volvo Group
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- Steinmetz, Erik M, 1984 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology,SP Technical Research Institute Sweden
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(creator_code:org_t)
- 2012
- 2012
- Engelska.
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Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; vol.13:no.2, s. pp.553-564
- Relaterad länk:
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https://research.cha...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Transportteknik och logistik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Transport Systems and Logistics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
Nyckelord
- SeMiFOT project
- visual driver distraction
- eye
- driver distraction automatic detection
- large database
- eye/head-tracking data processing
- eye-tracker-equipped vehicle
- sensitivity
- quality handling
- driver information systems
- large-scale naturalistic driving data set
- signal enhancement
- object tracking
- Visualization
- driver distraction
- naturalistic eye-and head-tracking data
- Interpolation
- very large databases
- incidents
- road safety
- signal processing
- driver inattention
- Smoothing methods
- Dispersion
- eyes-off-road glance detection
- Data processing
- naturalistic data
- Roads
- road accidents
- eye tracking
- Sweden-Michigan field operational test
- crashes
- Vehicles
- Reliability
- detection technology
- Road: Road user behaviour
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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