Location

Manchester Village, Vermont

Date

29-6-2017

Session

Session 6 — Hybrid Presentations

Abstract

Objective sleepiness evaluation is essential for the effect analysis of countermeasures for driver sleepiness, such as in-car stimulants. Furthermore, measuring drivers’ sleepiness in simulator studies also becomes important when investigating causes for task-related sleepiness, for example driving on monotonous routes, which requires little driver engagement. To evaluate driver sleepiness and the effect of countermeasures, we developed a model for predicting sleepiness using both simple logistic and linear regression of heart rate variability, skin conductance and pupil diameter. The algorithm was trained and tested with data from 88 participants in driving simulator studies. A prediction accuracy of 77% was achieved and the model’s sensitivity to thermal stimulation was shown.

Rights

Copyright © 2017 the author(s)

DC Citation

Proceedings of the Ninth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, June 26-29, 2017, Manchester Village, Vermont. Iowa City, IA: Public Policy Center, University of Iowa, 2017: 284-290.

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Jun 29th, 12:00 AM

Evaluating Drivers’ States in Sleepiness Countermeasures Experiments Using Physiological and Eye Data – Hybrid Logistic and Linear Regression Model

Manchester Village, Vermont

Objective sleepiness evaluation is essential for the effect analysis of countermeasures for driver sleepiness, such as in-car stimulants. Furthermore, measuring drivers’ sleepiness in simulator studies also becomes important when investigating causes for task-related sleepiness, for example driving on monotonous routes, which requires little driver engagement. To evaluate driver sleepiness and the effect of countermeasures, we developed a model for predicting sleepiness using both simple logistic and linear regression of heart rate variability, skin conductance and pupil diameter. The algorithm was trained and tested with data from 88 participants in driving simulator studies. A prediction accuracy of 77% was achieved and the model’s sensitivity to thermal stimulation was shown.