Real-Time Radar Sensor Performance Enhancement via Edge Machine Learning for Driver Drowsiness Detection

Abstract

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.