@article{ref1,
title="Temporal dashboard gaze variance (TDGV) changes for measuring cognitive distraction while driving",
journal="Sensors (Basel)",
year="2022",
author="Marx, Cyril and Kalayci, Elem Güzel and Moertl, Peter",
volume="22",
number="23",
pages="e9556-e9556",
abstract="A difficult challenge for today's driver monitoring systems is the detection of cognitive distraction. The present research presents the development of a theory-driven approach for cognitive distraction detection during manual driving based on temporal control theories. It is based solely on changes in the temporal variance of driving-relevant gaze behavior, such as gazes onto the dashboard (TDGV). Validation of the detection method happened in a field and in a simulator study by letting participants drive, alternating with and without a secondary task inducing external cognitive distraction (auditory continuous performance task). The general accuracy of the distraction detection method varies between 68% and 81% based on the quality of an individual prerecorded baseline measurement. As a theory-driven system, it represents not only a step towards a sophisticated cognitive distraction detection method, but also explains that changes in temporal dashboard gaze variance (TDGV) are a useful behavioral indicator for detecting cognitive distraction.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s22239556",
url="http://dx.doi.org/10.3390/s22239556"
}