Processing of Eye/Head-Tracking Data in Large-Scale Naturalistic Driving Data Sets
Journal article, 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.
driver information systems
SeMiFOT project
Data processing
Sweden-Michigan field operational test
Vehicles
signal enhancement
eye-tracker-equipped vehicle
large-scale naturalistic driving data set
eyes-off-road glance detection
Reliability
eye/head-tracking data processing
visual driver distraction
Visualization
driver distraction automatic detection
large database
Interpolation
Dispersion
eye tracking
road safety
object tracking
eye
road accidents
crashes
incidents
Smoothing methods
naturalistic eye-and head-tracking data
driver distraction
Roads
very large databases
detection technology
signal processing
sensitivity
driver inattention
naturalistic data
quality handling
Author
Christer Ahlström
Trent Victor
Chalmers, SAFER - Vehicle and Traffic Safety Centre
Claudia Wege
Volvo
Erik M Steinmetz
Chalmers, Signals and Systems, Kommunikationssystem, informationsteori och antenner
IEEE Transactions on Intelligent Transportation Systems
1524-9050 (ISSN)
Vol. vol.13 no.2 pp.553-564Areas of Advance
Transport
Subject Categories (SSIF 2011)
Transport Systems and Logistics
Signal Processing
DOI
10.1109/TITS.2011.2174786