Temporal Separation and Self-Rating of Alertness as Indicators of Driver Fatigue in Commercial Motor Vehicle Operators

Abstract

This on-road field investigation employed, for the first time, a completely automated trigger-based data collection system capable of evaluating driver performance in an extended-duration real-world commercial motor vehicle environment. The study examined the use of self-assessment of fatigue (Karolinska Sleepiness Scale) and temporal separation (minimum time to collision, minimum headway, and mean headway) as indicators of driver fatigue. Without exception, the correlation analyses for both the self-rating of alertness and temporal separation yielded models low in associative ability; neither metric was found to be a valid indicator of driver fatigue. In addition, based upon the data collected for this research, preliminary evidence suggests that driver fatigue onset within a real-world driving environment does not appear to follow the standard progression of events associated with the onset of fatigue within a simulated driving environment. Application of this research includes the development of an on-board driver performance/fatigue monitoring system that could potentially assist drivers in identifying the onset of fatigue.