Meteier Quentin, Capallera Marine, de Salis Emmanuel, Angelini Leonardo, Carrino Stefano, Widmer Marino, Abou Khaled Omar, Mugellini Elena, Sonderegger Andreas
HumanTech Institute, University of Applied Sciences and Arts of Western Switzerland, HES-SO, Boulevard de Pérolles 80, Fribourg, 1700, Switzerland.
Haute-Ecole Arc Ingénierie, University of Applied Sciences and Arts of Western Switzerland, HES-SO, Rue de la Serre 7, Saint-Imier, 2610, Switzerland.
Data Brief. 2023 Mar 3;47:109027. doi: 10.1016/j.dib.2023.109027. eCollection 2023 Apr.
This dataset contains data of 346 drivers collected during six experiments conducted in a fixed-base driving simulator. Five studies simulated conditionally automated driving (L3-SAE), and the other one simulated manual driving (L0-SAE). The dataset includes physiological data (electrocardiogram (ECG), electrodermal activity (EDA), and respiration (RESP)), driving and behavioral data (reaction time, steering wheel angle, …), performance data of non-driving-related tasks, and questionnaire responses. Among them, measures from standardized questionnaires were collected, either to control the experimental manipulation of the driver's state, or to measure constructs related to human factors and driving safety (drowsiness, mental workload, affective state, situation awareness, situational trust, user experience). In the provided dataset, some raw data have been processed, notably physiological data from which physiological indicators (or features) have been calculated. The latter can be used as input for machine learning models to predict various states (sleep deprivation, high mental workload, ...) that may be critical for driver safety. Subjective self-reported measures can also be used as ground truth to apply regression techniques. Besides that, statistical analyses can be performed using the dataset, in particular to analyze the situational awareness or the takeover quality of drivers, in different states and different driving scenarios. Overall, this dataset contributes to better understanding and consideration of the driver's state and behavior in conditionally automated driving. In addition, this dataset stimulates and inspires research in the fields of physiological/affective computing and human factors in transportation, and allows companies from the automotive industry to better design adapted human-vehicle interfaces for safe use of automated vehicles on the roads.
该数据集包含在固定基座驾驶模拟器中进行的六项实验期间收集的346名驾驶员的数据。五项研究模拟了有条件自动驾驶（L3 - SAE），另一项模拟了手动驾驶（L0 - SAE）。该数据集包括生理数据（心电图（ECG）、皮肤电活动（EDA）和呼吸（RESP））、驾驶和行为数据（反应时间、方向盘角度等）、非驾驶相关任务的性能数据以及问卷调查回复。其中，收集了标准化问卷中的测量数据，用于控制驾驶员状态的实验操作，或测量与人为因素和驾驶安全相关的构念（嗜睡、心理负荷、情感状态、态势感知、情境信任、用户体验）。在提供的数据集中，一些原始数据已经过处理，特别是已经计算出生理指标（或特征）的生理数据。后者可作为机器学习模型的输入，以预测可能对驾驶员安全至关重要的各种状态（睡眠不足、高心理负荷等）。主观自我报告的测量数据也可作为应用回归技术的基本事实。除此之外，可以使用该数据集进行统计分析，特别是分析不同状态和不同驾驶场景下驾驶员的态势感知或接管质量。总体而言，该数据集有助于更好地理解和考虑有条件自动驾驶中驾驶员的状态和行为。此外，该数据集激发并推动了生理/情感计算和交通领域人为因素方面的研究，并使汽车行业的公司能够更好地设计适配的人机界面，以确保自动驾驶车辆在道路上的安全使用。
