Real-Time Driver Drowsiness Detection System Using Temporal Convolutional Networks and Infrared Vision

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DOI https://doi.org/10.55923/aksara.v4i1.3
Dewi Anggraini 1 , Hendra Setiawan 2 , Novi Maharani 3 , Fajar Lestari 4 , Putri Maharani 5
1, 2, 5Faculty of Engineering, Universitas Gadjah Mada , 3, 4Faculty of Education, Universitas Negeri Malang
1 dewi.anggraini@ugm.ac.id , 2 hendra.setiawan@ugm.ac.id , 3 novi.maharani@um.ac.id , 4 fajar.lestari@um.ac.id , 5 putri.maharani@ugm.ac.id

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Driver fatigue remains a principal catalyst for vehicular highway fatalities. Utilizing an active near-infrared optical camera and temporal convolutional networks analyzing eyelid closure percentages (PERCLOS) and head nodding kinetics, our system identifies fatigue states reliably under complete nocturnal darkness.
Issue Vol. 4 No. 1 (2025)
Section Articles
Pages 225-237
Published 2025-06-30