Accuracy–latency trade-offs and absent embedded validation in deep learning driver drowsiness detection: a PRISMA systematic review
TL;DR
This systematic literature review 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.
Abstract
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.