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Paper WE-LA-T14.2

Gideon, John (Toyota Research Institute), TAMURA, KIMIMASA (Toyota Research Institute), Sumner, Emily (Toyota Research Institute), Dees, Laporsha (Toyota Research Institute), Reyes Gomez, Patricio (Toyota Research Institute), Haq, Bassamul (Toyota Research Institute), Rowell, Todd (Toyota Research Institute), Balachandran, Avinash (Stanford University), Stent, Simon (Toyota Research Institute), Rosman, Guy (Toyota Research Institute (TRI))

A Simulator Dataset to Support the Study of Impaired Driving

Scheduled for presentation during the Regular Session "S14c-Human Factors and Human Machine Interaction in Automated Driving" (WE-LA-T14), Wednesday, November 19, 2025, 16:20−16:40, Currumbin

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 19, 2025

Keywords Human-Machine Interaction Systems for Enhanced Driver Assistance and Safety, Driver Behavior Monitoring and Feedback Systems for Semi-autonomous Vehicles

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. We validate the dataset through an analysis of vehicle control and gaze behavioral features, and provide a set of machine learning baselines for various forms of impairment detection task. The dataset will be made available at https://toyotaresearchinstitute.github.io/IDD/.

 

 

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