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

This dataset contains data of 346 drivers collected during six experiments conducted in a fixed-base driving simula-tor. Five studies simulated conditionally automated driving (L3-SAE), and the other one simulated manual driving (L0 -SAE). The dataset includes physiological data (electrocardio-gram (ECG), electrodermal activity (EDA), and respiration (RESP)), driving and behavioral data (reaction time, steer-ing wheel angle, ...), performance data of non-driving-related tasks, and questionnaire responses. Among them, mea-sures from standardized questionnaires were collected, ei-ther 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 indica-tors (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 tech-niques. Besides that, statistical analyses can be performed us-ing the dataset, in particular to analyze the situational aware-ness 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 condition-ally 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.(c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )