Accuracy–latency trade-offs and absent embedded validation in deep learning driver drowsiness detection: a PRISMA systematic review

Introduction: Driver drowsiness is a leading cause of road traffic fatalities worldwide, and the convergence of computer vision and deep learning has transformed driver-state monitoring by enabling non-invasive detection within Advanced Driver Assistance Systems (ADAS). Methods: This systematic lite...

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Autores principales:	Christian Wilbert Salas Yupanqui, Cristhian Edy Llanque Tipo, Frank Diego Choquehuanca Huayhua, Ángel Rosendo Condori-Coaquira, David Mamani-Pari, Milton Edward Humpiri Flores, Esteban Tocto-Cano
Formato:	Artículo de revisión
Lenguaje:	inglés
Publicado:	2026
Materias:	driver drowsiness detection deep learning accuracy-latency trade-off driver assistance systems driver state monitoring convolutional neural networks cross-dataset evaluation model selection framework road safety Sleep and Work-Related Fatigue Human-Automation Interaction and Safety Advanced Neural Network Applications
Acceso en línea:	Enlace permanente (DOI o Handle) Ver en el sistema de origen
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