A Simulator Dataset to Support the Study of Impaired Driving

John Gideon, Kimimasa Tamura, Emily Sumner, Laporsha Dees, Patricio Reyes Gomez +5 more
4/15/2025
cs.CVcs.LGcs.RO

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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/.

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Cite this paper

@article{gideon2025simulator,
  title  = {A Simulator Dataset to Support the Study of Impaired Driving},
  author = {John Gideon and Kimimasa Tamura and Emily Sumner and Laporsha Dees and Patricio Reyes Gomez and Bassamul Haq and Todd Rowell and Avinash Balachandran and Simon Stent and Guy Rosman},
  year   = {2025},
  eprint = {2507.02867},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2507.02867v1}
}

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