Neuro-cognitive AI-driven system for preventing road accidents through driver drowsiness alerts

The increasing integration of smart city technologies has underscored the need for intelligent transportation systems that prioritize road safety. This study presents a neuro-cognitive artificial intelligence (AI)-driven system designed to detect driver drowsiness in real time and proposes a framework for future accident detection. The system combines computer vision and machine learning to monitor driver alertness and issue timely warnings. Facial expressions and eye movements are analyzed using a hybrid architecture that integrates convolutional neural networks (CNNs) for spatial feature extraction and gated recurrent units (GRUs) for temporal modeling. When signs of fatigue are detected—such as sustained low eye aspect ratio (EAR)—the system triggers visual and auditory alerts to re-engage the driver. Operating within a smart city infrastructure, the system is designed to communicate with traffic management platforms and emergency services for enhanced coordination. While the drowsiness detection module has been fully implemented and evaluated, the accident detection component remains a proposed feature for future development. This research contributes to the advancement of proactive road safety solutions and lays the groundwork for scalable, multimodal AI systems in intelligent transportation networks. • Developed a Neuro-Cognitive AI system combining CNNs and GRUs for real-time driver drowsiness detection. • Achieved 93 % F1-score using EAR-based analysis with adaptive threshold calibration across diverse participants. • Integrated multimodal sensor fusion (video, gyroscope, physiological signals) with early and late fusion strategies. • Designed for smart city interoperability via MQTT, Fermata, and API-based communication with emergency services. • Proposed scalable accident detection module using YOLOv5 and Random Forest classifiers for future implementation.