A Simulator Dataset to Support the Study of Impaired Driving

TL;DR

This paper introduces a comprehensive simulated driving dataset to facilitate research on the effects of alcohol intoxication and cognitive distraction on driver behavior, including vehicle and driver data under various impairment conditions.

Contribution

The dataset uniquely combines simulated urban driving data with multiple impairment scenarios and driver responses, supporting detailed analysis of impaired driving behaviors.

Findings

Dataset includes 23.7 hours of simulated driving data.

Captures driver behavior under alcohol and cognitive distraction.

Includes responses to controlled road hazards.

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. The dataset will be made available at https://toyotaresearchinstitute.github.io/IDD/.