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Updated: Jul 8, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

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Published on: December 18, 2020

Neurophysiological mental fatigue assessment for developing user-centered Artificial Intelligence as a solution for

Andrea Giorgi^1,2, Vincenzo Ronca^2,3, Alessia Vozzi^1,2

¹Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, Rome, Italy.

Frontiers in Neurorobotics

|December 18, 2023

Summary

Monitoring driver fatigue is crucial for automotive safety. Brain activity (electroencephalography) and eye movements (electrooculography) show promise in real-time detection of mental fatigue during driving.

Keywords:

EEG index mental fatigue multimodal assessment road safety simulated driving

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Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

Area of Science:

Neuroscience
Automotive Engineering
Human-Computer Interaction

Background:

Human error, particularly unsafe driver behavior, is a primary cause of road accidents.
The automotive industry is developing advanced driver-assistance systems (ADAS) and autonomous driving (AD) technologies to mitigate these risks.
During the transition to higher levels of autonomous driving (SAE 3-5), a user-centered AI is needed to monitor the driver's psychophysical state.

Purpose of the Study:

To assess the driver's mental fatigue in real-time using a holistic neurophysiological approach.
To detect the onset and progression of mental fatigue during simulated driving tasks.
To provide an information/trigger channel for vehicle AI to manage driver attention and control transitions.

Main Methods:

Simulated driving task (45 minutes) with 26 professional drivers.
Simultaneous recording of neurophysiological signals: electroencephalography (EEG), electrooculography (EOG), photoplethysmography (PPG), and electrodermal activity (EDA).
Collection of behavioral (reaction times) and subjective fatigue measures for validation.

Main Results:

Brain activity (EEG) parameters were the most sensitive and timely indicators of mental fatigue onset.
Ocular parameters (EOG) also showed sensitivity to fatigue but with a noticeable delay.
Photoplethysmography (PPG) and electrodermal activity (EDA) did not show significant changes related to mental fatigue in this study.

Conclusions:

A multi-modal neurophysiological approach, particularly focusing on brain and ocular activity, can effectively monitor driver mental fatigue in real-time.
Real-time fatigue detection is essential for developing user-centered AI in autonomous vehicles, ensuring safe transitions between human and automated control.
Future systems can leverage these findings to enhance driver safety and the overall performance of autonomous driving technologies.