Overview. We captured data from 52 human drivers over 25 hours of urban driving in a driving simulator, including under alcohol-intoxicated and cognitively-distracted driving conditions and with a range of realistic driving hazards. Our dataset supports various analyses including: the overlap between different types of impairment, how to distinguish one from the other, and their impact on behaviors such as visual attention and responses to road hazards. The dataset will be publicly released on this site once available for publication.
Abstract
Despite recent advances in automated driving technology, impaired driving continues to incur a high cost to society. In this paper, we present a driving dataset designed to support the study of two common forms of driver impairment: alcohol intoxication and cognitive distraction. Our dataset spans 23.7 hours of simulated urban driving, with 52 human subjects under normal and impaired conditions, and includes both vehicle data (ground truth perception, vehicle pose, controls) and driver-facing data (gaze, audio, surveys). It supports analysis of changes in driver behavior due to alcohol intoxication (0.10\% blood alcohol content), two forms of cognitive distraction (audio n-back and sentence parsing tasks), and combinations thereof, as well as responses to a set of eight controlled road hazards, such as vehicle cut-ins.