KAMESH RATHINASAMY@Zing_Sky2vp2

BUS-SAFE X addresses the delay and lack of real-time information during bus accidents. The system monitors simulated impact, damage, deceleration, fire, and smoke sensor inputs and uses a transparent weighted AI detection logic to calculate accident confidence. When the confidence crosses the defined threshold, the system activates the emergency response state and generates a Rescue Intelligence Report containing the bus ID, GPS location, passenger distribution, affected zone, sensor status, accident confidence, and nearest hospital information. The project is implemented as a Python Flask web application with SQLite for persistent state storage. The dashboard provides separate views for passengers, sensors, AI/ML detection, emergency response, and voice interaction. The prototype is designed so that simulated sensor inputs can later be connected to real IoT hardware and a trained machine-learning model.

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