Driver propensity to fatigue and drowsiness: a probabilistic approach

Abstract

This analysis is aimed at quantification, on the probabilistic basis, of the significance of driver fatigue and drowsiness (DD) in automated and manual driving conditions; the impact of driving time on the probability of an accident, because of the driver’s fatigue and possible drowsiness, and the observation that age groups of 20–25 and 65–70 are more prone to making a human fatigue and/or DD related error than the 26–64 old group. Our analyses are based on the application of the recently suggested double exponential probability distribution function (DEPDF), and on using entropy and adequate-trust based considerations. The general concepts are illustrated by numerical examples. It is concluded that analytical modeling technique employed in this study should complement, whenever possible, statistical analyses and computer simulations in challenging ergonomics tasks, including those of the type in question.

Keywords:

• Driver drowsiness
• human factors
• automated driving

Disclosure statement

No potential conflict of interest was reported by the author.