构建23.7小时模拟驾驶数据集，研究酒驾与分心对驾驶行为的影响。
A Simulator Dataset to Support the Study of Impaired Driving
- 在模拟城市环境中采集52名受试者正常与受损状态下的驾驶数据
- 涵盖0.10%血醇浓度及两种认知分心任务下的行为变化
- 适合自动驾驶安全评估、人因工程与驾驶员状态识别研究
尽管自动驾驶技术取得进展，酒驾和分心驾驶仍造成重大社会成本。本文提出一个驾驶数据集，用于研究酒精中毒与认知分心两种常见驾驶损伤。数据集包含23.7小时模拟城市驾驶，覆盖52名受试者在正常与受损状态下的表现，涵盖车辆数据（感知真值、车辆位姿、控制输入）与面向驾驶员的数据（注视轨迹、音频、问卷）。支持分析0.10%血醇浓度下的酒精影响、两种认知分心任务（听觉n-back、句子解析）及其组合，以及对8种可控道路危险（如车辆切入）的响应。数据集将公开于 https://toyotaresearchinstitute.github.io/IDD/。
原文摘要 · Abstract (English)
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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