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Driver Drowsiness Multi-Method Detection for Vehicles with Autonomous Driving Functions.

Horia Beles¹, Tiberiu Vesselenyi¹, Alexandru Rus¹

¹Department of Mechanical Engineering and Automotive, University of Oradea, Universitatii St. 1, 410087 Oradea, Romania.

Sensors (Basel, Switzerland)

|March 13, 2024

Summary

This study presents a fuzzy algorithm using electrooculography (EOG) and eye images to detect driver drowsiness and prevent accidents. The system warns drowsy drivers and can alert advanced driver assistance systems (ADAS) if needed.

Keywords:

ADAS autonomous driving deep learning sensor integration sensors fusion video signal processing

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Area of Science:

Computer Science
Artificial Intelligence
Automotive Safety

Background:

Driver drowsiness is a significant factor in road accidents due to reduced attention and increased reaction times.
Existing driver monitoring systems require improvement in accuracy and reliability for effective drowsiness detection.

Purpose of the Study:

To develop and present a fuzzy decision algorithm for real-time driver drowsiness monitoring and warning.
To integrate electrooculography (EOG) signals and eye state images for robust drowsiness detection.
To enable advanced driver assistance systems (ADAS) intervention when drivers do not respond to drowsiness warnings.

Main Methods:

Utilizing fuzzy logic for decision-making in the drowsiness detection algorithm.
Analyzing electrooculography (EOG) signals to assess physiological indicators of fatigue.
Processing eye state images to identify visual cues associated with drowsiness.
Developing a system with components for learning, analysis, and decision-making regarding driver alertness.

Main Results:

The proposed fuzzy algorithm effectively analyzes EOG signals and eye images to determine driver alertness levels.
The system successfully identifies drowsy states and triggers timely warnings.
Demonstrated potential for reducing accident risks by addressing driver fatigue.

Conclusions:

The developed fuzzy decision algorithm offers a promising approach for driver drowsiness monitoring.
Integration with ADAS enhances safety by providing a fallback mechanism for unresponsive drivers.
This technology contributes to the advancement of intelligent vehicle safety systems.