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
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Sumario: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 literature review, conducted using the PRISMA 2020 methodology and the Kitchenham protocol, synthesizes findings from 33 peer-reviewed studies published between January 2021 and October 2025 to examine the architectures, biomarkers, performance benchmarks, and deployment barriers of deep-learning-based drowsiness detection. Results: The literature is dominated by convolutional single-pass designs; no reviewed model combines an accuracy above 99% with a latency below 100 ms, and none were evaluated on embedded or automotive-grade hardware. Discussion: This distribution highlights a persistent accuracy-latency trade-off and emphasizes the need for embedded hardware and cross-dataset evaluation. By synthesizing the available evidence, this review characterizes the current state of the art and proposes a prioritized framework for selecting deep learning architectures for embedded ADAS applications.

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