Citation

Beles H, Vesselenyi T, Rus A, Mitran T, Scurt FB, Tolea BA. Sensors (Basel) 2024; 24(5).

Copyright

(Copyright © 2024, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s24051541

PMID

38475079

PMCID

PMC10934756

Abstract

The article outlines various approaches to developing a fuzzy decision algorithm designed for monitoring and issuing warnings about driver drowsiness. This algorithm is based on analyzing EOG (electrooculography) signals and eye state images with the aim of preventing accidents. The drowsiness warning system comprises key components that learn about, analyze and make decisions regarding the driver's alertness status. The outcomes of this analysis can then trigger warnings if the driver is identified as being in a drowsy state. Driver drowsiness is characterized by a gradual decline in attention to the road and traffic, diminishing driving skills and an increase in reaction time, all contributing to a higher risk of accidents. In cases where the driver does not respond to the warnings, the ADAS (advanced driver assistance systems) system should intervene, assuming control of the vehicle's commands.

Language: en

Keywords

*Automobile Driving; Accidents, Traffic/prevention & control; ADAS; Algorithms; autonomous driving; deep learning; Electrooculography; sensor integration; sensors fusion; video signal processing; Wakefulness