Authors: Ganesh K. Bharaskar, Jayesh S. Chavan, Yash K. Pawar, Sagar R. Girase, Prof. Mohan T. Patel
Abstract: The imperative for minimizing response time in vehicular accident scenarios necessitates the development of robust, automated detection and notification systems. Conventional methods often rely on manual intervention, introducing critical delays that severely impact victim outcomes. This paper presents the architecture and performance evaluation of an AI-Based Vehicle Crash Detection and Emergency Notification System (AVC-DENS), designed to provide instantaneous, location-aware alerts upon the occurrence of a significant vehicular impact event. The AVC-DENS employs a tightly integrated Internet of Things (IoT) framework centered around the ESP32 microcontroller unit. Crash detection is predicated upon the real-time analysis of data streams derived from integrated vibration and inertial sensors, complemented by GPS modules for precise spatial localization. Upon algorithmic confirmation of a crash event, the system executes a multi-faceted notification protocol: captured video evidence and temporal-spatial coordinates are immediately transmitted to a secure cloud platform, specifically utilizing Firebase for reliable data persistence and retrieval.
DOI: http://doi.org/10.5281/zenodo.18668675
