Processing of Eye/Head-Tracking Data in Large-Scale Naturalistic Driving Data Sets
Artikel i vetenskaplig tidskrift, 2012
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.
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
Författare
Christer Ahlström
Statens Väg- och Transportforskningsinstitut (VTI)
Trent Victor
Chalmers, Vehicle and Traffic Safety Centre at Chalmers (SAFER)
Claudia Wege
Volvo Group
Erik M Steinmetz
Chalmers, Signaler och system, Kommunikation, Antenner och Optiska Nätverk
IEEE Transactions on Intelligent Transportation Systems
1524-9050 (ISSN) 1558-0016 (eISSN)
Vol. 13 2 pp.553-564 6093970Styrkeområden
Transport
Ämneskategorier
Transportteknik och logistik
Signalbehandling
DOI
10.1109/TITS.2011.2174786