Adaptive Neural Embedded Systems for Real-Time Driver State Monitoring in Advanced Driver Assistance Systems

Abstract

As Advanced Driver Assistance Systems continue to evolve rapidly, more sophisticated methods for continuous assessment of driver cognitive and physical states are essential to address safety risks during the transition toward vehicle autonomy. Adaptive neural embedded systems represent a transformative approach to real-time driver monitoring, combining multiple sensor types with edge-deployed artificial intelligence to create personalized safety models. The architecture integrates Convolutional Neural Networks for extracting spatial features and Long Short-Term Memory networks for identifying temporal patterns, processing facial images, physiological signals, and behavioral indicators to detect fatigue and distraction states. Implementation on embedded FPGA-System-on-Chip platforms achieves sub-second inference latency while maintaining high classification accuracy across diverse operational environments. Baseline calibration procedures establish driver-specific detection thresholds that account for individual physiological differences, while ongoing model refinement through incremental learning adapts to changing behavioral patterns over extended operation periods. Direct integration with Electronic Control Units enables graduated intervention strategies ranging from gentle sensory alerts to active vehicle control inputs, including lane-centering assistance and controlled braking. Transfer learning techniques accelerate model development by leveraging pre-trained representations, achieving exceptional precision and recall metrics through fine-tuning on domain-specific drowsiness datasets. The embedded architecture addresses fundamental limitations of cloud-dependent systems, including latency constraints, connectivity dependence, and privacy concerns, delivering deterministic real-time performance essential for safety-critical automotive applications in increasingly autonomous vehicular environments.