Temporal Dashboard Gaze Variance (TDGV) Changes for Measuring Cognitive Distraction While Driving

Cyril Marx*, Elem Güzel-Kalayci, Peter Moertl

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number9556
Issue number23
Publication statusPublished - 6 Dec 2022


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