标题： 一种支持受损驾驶研究的模拟数据集 显示英文标题

标题： A Simulator Dataset to Support the Study of Impaired Driving

摘要： 尽管自动驾驶技术最近取得了进展，但酒后驾驶仍然给社会带来了高昂的代价。 在本文中，我们提出一个驾驶数据集，旨在支持对两种常见的驾驶员能力受损形式：酒精中毒和认知分心的研究。 我们的数据集涵盖了23.7小时的模拟城市驾驶，有52名人类受试者在正常和受损条件下的数据，包括车辆数据（真实感知、车辆姿态、控制）和面向驾驶员的数据（注视、音频、调查）。 它支持分析由于酒精中毒（0.10%血液酒精含量）、两种形式的认知分心（音频n-back任务和句子解析任务）及其组合导致的驾驶员行为变化，以及对一组八个受控道路危险的反应，例如车辆切入。 该数据集将在https://toyotaresearchinstitute.github.io/IDD/上提供。 显示英文摘要

摘要： 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/.