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Neurophysiological mental fatigue assessment for developing usercentred Artificial Intelligence as a solution for autonomous driving

Fulltext:


Authors:

Andrea Giorgi , Vincenzo Ronca , Alessia Vozzi , Pietro Aricò , Gianluca Borghini , Rossella Capotorto , Luca Tamborra , Ilaria Simonetti , Simone Sportiello , Marco Petrelli , Carlo Polidori , Rodrigo Varga , Marteyn van Gasteren , Arnab Barua, Mobyen Uddin Ahmed, Fabio Babiloni , Gianluca Di Flumeri

Research group:

Artificial Intelligence and Intelligent Systems

Publication Type:

Journal article

Venue:

Frontiers in Neurorobotics

Abstract

Human factor plays a key role in the automotive field, since most accidents are due to driver’s unsafe behaviors. To deal with this, two main solutions are pursued: on a short term the development of systems aimed at monitoring driver’s psychophysical state, such as inattention and fatigue; and on a medium-long term the full autonomous driving. This second objective is promoted by the recent technological progress in terms of Artificial Intelligence and sensing systems. The assumption is that the more accurate the vehicle will be aware of the “surroundings”, the more reliable will be the autonomous driving. Even with autonomous vehicles, the drivers should be able to take control of the vehicle when needed (i.e., takeover request), especially during this transition phase from lower (SAE < 3) to the highest level (SAE = 5) of autonomous driving.In this scenario, the vehicle has to be aware not only of the “surroundings” but also of the driver’s psychophysical state, i.e. a user-centered Artificial Intelligence. Neurophysiological approach is one the most effective in detecting users' unproper mental states while driving. This is particularly true if considering that the more automatic the driving will be, the less available the vehicular data related to the driver’s driving style will be. Several signals such as those related to brain, ocular and cardiac activities, have been largely adopted in scientific research to characterize episodes of mental fatigue or inattention. The present study aimed at employing a holistic approach, considering simultaneously electroencephalographic, electrooculographic, photopletismographic and electrodermal activity data, on 26 professional drivers engaged in a long-lasting realistic driving task in simulated conditions. The aim was to investigate which neurophysiological parameters can be used together to assess the driver’s mental fatigue in real time and to detect the onset of fatigue, to make this information available for the vehicle AI. Results showed that the most sensitive parameters are those related to brain activity. In a less extent, also those related to ocular parameters are sensitive to the onset of mental fatigue, but with a delayed effect. The other investigated parameters did not significantly change during the experimental session

Bibtex

@article{Giorgi6811,
author = {Andrea Giorgi and Vincenzo Ronca and Alessia Vozzi and Pietro Aric{\`o} and Gianluca Borghini and Rossella Capotorto and Luca Tamborra and Ilaria Simonetti and Simone Sportiello and Marco Petrelli and Carlo Polidori and Rodrigo Varga and Marteyn van Gasteren and Arnab Barua and Mobyen Uddin Ahmed and Fabio Babiloni and Gianluca Di Flumeri},
title = {Neurophysiological mental fatigue assessment for developing usercentred Artificial Intelligence as a solution for autonomous driving },
editor = {Florian R{\"o}hrbein},
volume = {1},
pages = {1--35},
month = {November},
year = {2023},
journal = {Frontiers in Neurorobotics },
url = {http://www.es.mdu.se/publications/6811-}
}