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Accuracy-latency trade-offs and absent embedded validation in deep learning driver drowsiness detection: a PRISMA
Christian Wilbert Salas Yupanqui1, Cristhian Edy Llanque Tipo1, Frank Diego Choquehuanca Huayhua1
1Escuela Profesional de Ingeniería de Sistemas, Universidad Peruana Unión, Juliaca, Perú.
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.
