A dataset on the physiological state and behavior of drivers in conditionally automated driving

• Published in: Data in brief Vol. 47; p. 109027
• Main Authors: Meteier, Quentin, Capallera, Marine, de Salis, Emmanuel, Angelini, Leonardo, Carrino, Stefano, Widmer, Marino, Abou Khaled, Omar, Mugellini, Elena, Sonderegger, Andreas
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.04.2023; Elsevier
• Subjects: Conditionally automated driving; Data; Driver state; Electrocardiogram (ECG); Electrodermal activity (EDA); Physiology; Respiration; Situation awareness (SA); Takeover quality; Takeover quality; Electrodermal activity (EDA); Conditionally automated driving; Situation awareness (SA); Physiology; Driver state; Electrocardiogram (ECG); Respiration
• ISSN: 2352-3409; 2352-3409
• DOI: 10.1016/j.dib.2023.109027

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.