Authors: Shreya Chavan, Mayuri Patil, Aarya Pawar, Professor Jayshri Kandekar
Abstract: Road traffic accidents continue to be an major global safety concern due to human error, delayed emergency response, and a lack of predictive monitoring systems. This paper presents an Integrated Intelligent Vehicle Safety System (IIVSS), a hybrid IoT and Artificial Intelligence-based frame-work designed for real-time accident prediction and automated emergency response. The proposed system integrates IMU and GPS sensor fusion with edge-level processing and cloud analytics to detect abnormal driving patterns and predict potential colli-sions. Unlike traditional reactive accident detection systems, the proposed architecture enables predictive safety analysis through anomaly detection algorithms and automated alert generation. The experimental evaluation demonstrates low latency response, reliable communication, and high detection accuracy. The sys-tem provides a scalable, cost-effective and intelligent solution for next-generation smart transportation and connected-vehicle ecosystems.
