This study presents an optimized, cost-effective radar sensor system for real-time, and contactless driver drowsiness detection. The system consist on a 24 GHz frequency-modulated continuous Wave radar module integrated behind the central rearview mirror for highly sensitive analysis of driver micromovements, including head tilts, and facial dynamics. The core innovation lies in the edge-deployed sensor-specific adaptation, where a custom CNN-LSTM pipeline processes complex radar returns to estimate eye opening and mouth opening metrics without visual data. This pipeline enhances the system's ability to isolate subtle motion characteristics—such as range and velocity—improving the sensing fidelity crucial for fatigue monitoring. Deployed on a resource-constrained Raspberry Pi platform, the model demonstrates robust integration and efficiency. Experimental validation on 20 subjects shows excellent performance, achieving up to 98.1% accuracy for eye blinking, and 97.9% for spontaneous yawning. This radar-based approach advances automotive sensing by providing a privacy-preserving, high-performance solution for safety-critical applications.
The engineering and experimental findings demonstrate that the fusion strategy developed in this work provides a practical and transferable solution for next-generation autonomous vehicle perception in real-world deployments.
Shibai Li, Shunxin Li· The 2026 International Confe...· 0 citations
Drowsiness-related traffic accidents are a major challenge in ensuring road safety in the whole world. One of the biggest challenges is to develop a reliable real-time driver monitoring system that works even under adverse conditions, i.e., low illumination and partial occlusion of the face. A number of existing method...
Karna Vishnu Vardhana Reddy, Kojjarapu Akhila, Yalla V V V Satya Krishna Varaprasad et al.· 2026 International Conferenc...· 0 citations
A comparative evaluation of several state-of-the-art deep learning models for drowsiness detection, including convolutional and transformer-based architectures, across multiple public datasets and a custom-compiled dataset reveals significant performance variability across datasets, indicating limited generalization be...
Zenon Lamprou, Georgia Christodoulou, Konstantinos Avgerinakis· 2026 ITU Kaleidoscope - AI a...· 0 citations
This paper introduces a non-intrusive, real-time Driver Drowsiness Detection and Alert System utilizing computer vision and lightweight deep learning models, providing an affordable, robust solution to enhance smart vehicular safety.
P. Priya, S. S. Santhosh Kumar· International Scientific Jou...· 0 citations
Driver drowsiness is one of the leading causes of road accidents, highlighting the need for accurate and real-time monitoring systems to improve road safety. This paper presents an intelligent driver drowsiness detection framework that combines computer vision and machine learning techniques to classify driver states a...
Priyanka Ghosh, Garima Bishnu, Madhumanti Mallick et al.· International Conference Com...· 0 citations
Driver drowsiness is among the main causes of road accidents worldwide and can lead to dreadful injuries. Early fatigue detection significantly helps to avoid crashes and enhance road safety. Conventional driver monitoring devices are typically designed to monitor behavior, such as eye blinking and yawning, or physiolo...
S. Venkatesh, M. Chithra· 2026 4th International Confe...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
