AI-Powered Real-Time Accident Detection Through Integration of Driver Health and Vehicle Dynamics

This research proposes a novel AI-powered system for real-time accident detection through the seamless integration of driver health metrics and vehicle dynamics. Unlike existing models that analyze these factors independently, this system combines physiological data, such as heart rate variability and fatigue indices, with vehicular parameters, including speed, steering angle, and braking intensity, to provide a comprehensive analysis of unsafe driving behaviors. The system processes data in real time using advanced machine learning algorithms, achieving a predictive accuracy of 94.7 pecentage and a response latency of 0.48 seconds. A robust dataset of over 50,000 data points was utilized, encompassing diverse driving scenarios and conditions, ensuring the model’s reliability and robustness. Experimental results demonstrate the system’s ability to identify high-risk behaviors such as drowsiness and aggressive driving with a detection rate of 97.5 percentage, outperforming traditional approaches. This integrated approach not only enhances accident prevention capabilities but also sets the stage for future advancements in intelligent road safety systemst.