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.

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

Författare

Christer Ahlström

Trent Victor

Chalmers, SAFER - Fordons- och Trafiksäkerhetscentrum

Claudia Wege

Volvo

Erik M Steinmetz

Signaler och system, Kommunikationssystem, informationsteori och antenner, Kommunikationssystem

IEEE Transactions on Intelligent Transportation Systems

1524-9050 (ISSN)

Vol. vol.13 pp.553-564

Styrkeområden

Transport

Ämneskategorier

Transportteknik och logistik

Signalbehandling

DOI

10.1109/TITS.2011.2174786