Eye-Gaze Metrics for Cognitive Load Detection on a Driving Simulator

Document Type

Article

Publication Date

8-1-2022

Publication Title

IEEE/ASME Transactions on Mechatronics

Volume

27

Issue

4

First Page

2134

Keywords

Automated driving, cognitive load estimation, eye-tracking, gaze metrics, human-computer interface

Last Page

2141

Abstract

Automated driving systems (ADSs) are becoming ubiquitous to reduce the workload of drivers and improve road safety. However, present-day ADS lacks accurate and effective driver monitoring systems. Driver monitoring systems use physiological measurements, such as pupil dilation, eye-gaze, and eye-blinks, in order to monitor the cognitive load experienced by the drivers. With advances in eye-tracking technology, pupil dilation is emerging as a reliable measure of cognitive load in ADS. However, pupil dilation as a measure of cognitive load suffers from many factors, such as confounding effects, noise, and personal attributes, to name a few. Hence, in order to improve cognitive load estimation in ADS, other noninvasive measures must be studied and incorporated. In this article, various eye-gaze metrics are studied and evaluated as a measure of cognitive load based on data collected from 16 drivers in a simulated driving scenario using a driving simulator.

DOI

10.1109/TMECH.2022.3175774

ISSN

10834435

E-ISSN

1941014X

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