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.

SeMiFOT project

visual driver distraction


driver distraction automatic detection

large database

eye/head-tracking data processing

eye-tracker-equipped vehicle


quality handling

driver information systems

large-scale naturalistic driving data set

signal enhancement

object tracking


driver distraction

naturalistic eye-and head-tracking data


very large databases


road safety

signal processing

driver inattention

Smoothing methods


eyes-off-road glance detection

Data processing

naturalistic data


road accidents

eye tracking

Sweden-Michigan field operational test




detection technology


Christer Ahlström

The Swedish National Road and Transport Research Institute (VTI)

Trent Victor

Vehicle and Traffic Safety Centre at Chalmers

Claudia Wege

Volvo Group

Erik M Steinmetz

Chalmers, Signals and Systems, Communication, Antennas and Optical Networks, Communication Systems

IEEE Transactions on Intelligent Transportation Systems

1524-9050 (ISSN)

Vol. vol.13 no.2 pp.553-564 6093970

Areas of Advance


Subject Categories

Transport Systems and Logistics

Signal Processing



More information

Latest update

4/5/2022 6