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)
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