Paper Contents
Abstract
The rapid growth of intelligent transportation systems and the increasing rate of vehicular accidents have driven the need for advanced safety solutions that integrate real-time monitoring, intelligent decision-making, and automated response mechanisms. The Integrated Intelligent Vehicle Safety System (IIVSS) is a technology-driven approach that combines Internet of Things (IoT) sensors, cloud computing, Artificial Intelligence (AI), and data analytics to enhance road safety and reduce accident severity. This survey paper explores recent advancements in IoT-enabled vehicle safety systems, highlighting their evolution from traditional accident detection modules to intelligent, predictive frameworks. The study focuses on the integration of IMU (Inertial Measurement Unit) and GPS-based data with predictive algorithms for real-time accident analysis and response. It also reviews existing literature, system architectures, and methodologies implemented for accident prevention, detection, and emergency alert systems. Furthermore, the paper discusses challenges related to connectivity, sensor reliability, and scalability, while identifying future research directions in autonomous safety.
Copyright
Copyright © 2025 Shreya D. Chavan, Mayuri K. Patil, Aarya P. Pawar, Jayshri A. Kandekar. This is an open access article distributed under the Creative Commons Attribution License.
