Sleep loss and driver performance: Quantitative predictions with zero free parameters
Fatigue has been implicated in an alarming number of motor vehicle accidents, costing billions of dollars and thousands of lives. Unfortunately, the ability to predict performance impairments in complex task domains like driving is limited by a gap in our understanding of the explanatory mechanisms. In this paper, we describe an attempt to generate a priori predictions of degradations in driver performance due to sleep deprivation. We accomplish this by integrating an existing account of the effects of sleep loss and circadian rhythms on sustained attention performance with a validated model of driver behavior. The predicted results account for published qualitative trends for driving across multiple days of restricted sleep and total sleep deprivation. The quantitative results show that the models performance is worse at baseline and degrades less severely than human driving, and expose some critical areas for future research. Overall, the results illustrate the potential value of model reuse and integration for improving our understanding of important psychological phenomena and for making useful predictions of performance in applied, naturalistic task contexts.
fatigue sleep deprivation sustained attention driver behavior computational model