Lightweight 2D-1D CNN for Real-Time Driver Drowsiness Detection withFederated Learning on Edge Devices - Guettas, Amina; Ayad, Soheyb; Kazar, Okba (Informatica, 2026)
Driver drowsiness is one of the main leading causes of traffic crash accidents. Preventing these mishaps and limiting consequential damage and losses, whether human or material, has been the subject of many research studies these recent years. Fulfilling this task requires a reliable drowsiness detection system installed in the vehicle that can function on limited hardware such as edge devices with real-time execution speed. The present study proposes a computer vision-based driver drowsiness detection system using Convolutional Neural Networks. The traditional approach is to extract some features from the captured driver’s face images such as PERCLOS, blink speed, or blink frequency, and determine the driver state using machine learning methods, predefined thresholds, or rules. However, deep learning techniques allow us to directly identify the driver state from the captured video, yet these techniques are usually computationally intensive and unsuitable for edge computing applications. To overcome these obstacles, we propose a lightweight 2D-1D CNN model for driver drowsiness detection. We deployed a 2D-CNN model for the eyes feature extraction of both the left and right eyes of the driver from every captured frame. In addition, we applied a 1D-CNN model to capture the temporal information of the concatenated features over a defined period. We implemented and evaluated the model’s performance under both centralized learning and federated learning. Experimental results show that our approach achieves 99% accuracy on the DROZY dataset and 98.06% on subjects excluded from the training set in the CL, while in the FL it achieves 95.27% on the same dataset and 77.86% on unseen subjects. Furthermore, the model runs in real time on an NVIDIA Jetson Nano, reaching 25.6 FPS. While FL offers enhanced privacy by enabling local model training without sharing raw user data, it exhibits limited generalization capability. In contrast, CL provides higher accuracy and a more robust model. Comparative analysis reveals a performance trade-off for FL, balanced by improved data privacy. This work highlights the potential of privacy-preserving, real-time drowsiness detection systems using advanced deep learning architectures.