Computational Physiology Laboratory, University of Houston, Houston, Texas 77204, USA.
Department of Statistics, Athens University of Economics and Business, Athens 104 34, Greece.
Sci Data. 2017 Aug 15;4:170110. doi: 10.1038/sdata.2017.110.
We describe a multimodal dataset acquired in a controlled experiment on a driving simulator. The set includes data for n=68 volunteers that drove the same highway under four different conditions: No distraction, cognitive distraction, emotional distraction, and sensorimotor distraction. The experiment closed with a special driving session, where all subjects experienced a startle stimulus in the form of unintended acceleration-half of them under a mixed distraction, and the other half in the absence of a distraction. During the experimental drives key response variables and several explanatory variables were continuously recorded. The response variables included speed, acceleration, brake force, steering, and lane position signals, while the explanatory variables included perinasal electrodermal activity (EDA), palm EDA, heart rate, breathing rate, and facial expression signals; biographical and psychometric covariates as well as eye tracking data were also obtained. This dataset enables research into driving behaviors under neatly abstracted distracting stressors, which account for many car crashes. The set can also be used in physiological channel benchmarking and multispectral face recognition.
我们描述了一个在驾驶模拟器上进行的受控实验中获得的多模态数据集。该数据集包括 n=68 名志愿者的数据，他们在四种不同条件下驾驶同一条高速公路：无干扰、认知干扰、情绪干扰和感觉运动干扰。实验结束时进行了一次特殊的驾驶测试，所有受试者都以意外加速的形式（一半受试者在混合干扰下，另一半在没有干扰的情况下）体验到了惊吓刺激。在实验驾驶过程中，连续记录了关键的响应变量和几个解释变量。响应变量包括速度、加速度、制动力、转向和车道位置信号，而解释变量包括鼻周皮肤电活动（EDA）、手掌 EDA、心率、呼吸率和面部表情信号；还获得了传记和心理测量协变量以及眼动追踪数据。该数据集可用于研究在许多车祸中导致的精心抽象的干扰压力下的驾驶行为。该数据集还可用于生理通道基准测试和多光谱人脸识别。
