AdVitam

A detailed description of the data collected, the experimental design, materials and methods used in each experiment, and the references associated with this work can be found in the manuscript published in the journal Data in Brief, available in Open Access here. Please cite this publication if you use the dataset.

Reference : Quentin Meteier, Marine Capallera, Emmanuel de Salis, Leonardo Angelini, Stefano Carrino, Marino Widmer, Omar Abou Khaled, Elena Mugellini, Andreas Sonderegger. A dataset on the physiological state and behavior of drivers in conditionally automated driving, Data in Brief, 2023, 109027, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2023.109027.

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This is a set of physiological and behavioural data collected in 6 fixed-base driving simulator experiments from 346 drivers. The data was collected as part of the AdVitam project (for Adaptive Driver-Vehicle Interaction to Make future driving safer), a joint research project funded by the Hasler Foundation, led by the Human Tech Institute (HEIA-FR), the He-Arc, the EPFL+ECAL Lab, and the University of Fribourg (Switzerland).

All experiments simulated conditional automation (Level 3 of automation according to the taxonomy released by the Society of Automotive Engineers), except for experiment 1 (Level 0 - manual driving).

Each folder contains raw and preprocessed data collected in each experiment:

Exp1: Experimental manipulation of relaxation before driving and presence of passenger while driving (manual driving) Exp2: Experimental manipulation of cognitive workload at 2 levels using a verbal task (backwards counting) Exp3: Experimental manipulation of cognitive workload at 3 levels using visual and auditory tasks (N-back task) Exp4: Experimental manipulation of fatigue (sleep deprivation) and driving environment (rural vs. urban scenario) ExpTOR: Multiple takeovers requested through different modalities (visual, auditory, haptic), while performing different non-driving related tasks ExpFinal:  Testing a contextual multimodal system for maintaining situation awareness and takeover quality in conditionally automated driving

Physiological data (Electrocardiogram, electrodermal activity and respiration) were collected in all experiments. They were preprocessed in Python with the Neurokit library. It was also used to process the physiological raw signals and calculate a large range of physiological indicators.

Some of these measures were also collected in the various experiments: situation awareness, takeover performance (reaction, time, maximum steering wheel angle, ..), affective state, mental workload, non-driving-related task performance, sleepiness..

More details on each experiment are provided in the ExpX_README.md of each folder.

Verbatim fromZenodo· Licence: cc-by-4.0