Location

Aspen, Colorado, USA

Date

15-8-2001

Session

Technical Session 3 - Fatigue and Impairment

Abstract

A series of driving simulation pilot studies on various technologies for alertness monitoring (head position sensor, eye-gaze system), fitness-for-duty testing (two pupil-based systems), and alertness promotion (in-seat vibration system) has been conducted in Circadian Technologies’ Alertness Testbed. The results indicate that, all tested technologies show promise for monitoring/testing or preventing driver fatigue, respectively. However, particularly for fatigue monitoring, no single measure alone may be sensitive and reliable enough to quantify driver fatigue. Since alertness is a complex phenomenon, a multi-parametric approach needs to be used. Such a multi-sensor approach imposes challenges for online data interpretation. We suggest using a neural-fuzzy hybrid system for the automatic assessment of complex data streams for driver fatigue. The final system output can then be used to trigger the activation of alertness countermeasures.

Rights

Copyright © 2001 the author(s)

DC Citation

Proceedings of the First International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, 14-17 August 2001, Aspen, Colorado. Iowa City, IA: Public Policy Center, of Iowa, 2001: 81-86.

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Aug 15th, 12:00 AM

Technologies for the Monitoring and Prevention of Driver Fatigue

Aspen, Colorado, USA

A series of driving simulation pilot studies on various technologies for alertness monitoring (head position sensor, eye-gaze system), fitness-for-duty testing (two pupil-based systems), and alertness promotion (in-seat vibration system) has been conducted in Circadian Technologies’ Alertness Testbed. The results indicate that, all tested technologies show promise for monitoring/testing or preventing driver fatigue, respectively. However, particularly for fatigue monitoring, no single measure alone may be sensitive and reliable enough to quantify driver fatigue. Since alertness is a complex phenomenon, a multi-parametric approach needs to be used. Such a multi-sensor approach imposes challenges for online data interpretation. We suggest using a neural-fuzzy hybrid system for the automatic assessment of complex data streams for driver fatigue. The final system output can then be used to trigger the activation of alertness countermeasures.