Processing of Eye/Head-Tracking Data in Large-Scale Naturalistic Driving Data Sets

• 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)