Neurophysiological mental fatigue assessment for developing user-centered Artificial Intelligence as a solution for autonomous driving Article Swipe
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
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fnbot.2023.1240933
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fnbot.2023.1240933
The human factor plays a key role in the automotive field since most accidents are due to drivers' unsafe and risky behaviors. The industry is now pursuing two main solutions to deal with this concern: in the short term, there is the development of systems monitoring drivers' psychophysical states, such as inattention and fatigue, and in the medium-long term, there is the development of fully autonomous driving. This second solution is promoted by recent technological progress in terms of Artificial Intelligence and sensing systems aimed at making vehicles more and more accurately aware of their “surroundings.” However, even with an autonomous vehicle, the driver should be able to take control of the vehicle when needed, especially during the current transition from the lower (SAE < 3) to the highest level (SAE = 5) of autonomous driving. In this scenario, the vehicle has to be aware not only of its “surroundings” but also of the driver's psychophysical state, i.e., a user-centered Artificial Intelligence. The neurophysiological approach is one the most effective in detecting improper mental states. 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. The present study aimed at employing a holistic approach, considering simultaneously several neurophysiological parameters, in particular, electroencephalographic, electrooculographic, photopletismographic, and electrodermal activity data to assess the driver's mental fatigue in real time and to detect the onset of fatigue increasing. This would ideally work as an information/trigger channel for the vehicle AI. In all, 26 professional drivers were engaged in a 45-min-lasting realistic driving task in simulated conditions, during which the previously listed biosignals were recorded. Behavioral (reaction times) and subjective measures were also collected to validate the experimental design and to support the neurophysiological results discussion. Results showed that the most sensitive and timely parameters were those related to brain activity. To a lesser extent, those related to ocular parameters were also sensitive to the onset of mental fatigue, but with a delayed effect. The other investigated parameters did not significantly change during the experimental session.
Related Topics
Concepts
Computer science
Automotive industry
Field (mathematics)
Term (time)
Neurophysiology
Human–computer interaction
Work (physics)
Mental state
Artificial intelligence
Cognitive psychology
Psychology
Engineering
Physics
Pure mathematics
Quantum mechanics
Mechanical engineering
Neuroscience
Mathematics
Aerospace engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fnbot.2023.1240933
- https://www.frontiersin.org/articles/10.3389/fnbot.2023.1240933/pdf?isPublishedV2=False
- OA Status
- gold
- Cited By
- 25
- References
- 84
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389274314
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389274314Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fnbot.2023.1240933Digital Object Identifier
- Title
-
Neurophysiological mental fatigue assessment for developing user-centered Artificial Intelligence as a solution for autonomous drivingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-30Full publication date if available
- 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 FlumeriList of authors in order
- Landing page
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https://doi.org/10.3389/fnbot.2023.1240933Publisher landing page
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https://www.frontiersin.org/articles/10.3389/fnbot.2023.1240933/pdf?isPublishedV2=FalseDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.frontiersin.org/articles/10.3389/fnbot.2023.1240933/pdf?isPublishedV2=FalseDirect OA link when available
- Concepts
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Computer science, Automotive industry, Field (mathematics), Term (time), Neurophysiology, Human–computer interaction, Work (physics), Mental state, Artificial intelligence, Cognitive psychology, Psychology, Engineering, Physics, Pure mathematics, Quantum mechanics, Mechanical engineering, Neuroscience, Mathematics, Aerospace engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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25Total citation count in OpenAlex
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2025: 18, 2024: 7Per-year citation counts (last 5 years)
- References (count)
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84Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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