AI-Based Emergency Response System Using Facial Recognition, GPS Navigation and Number Plate Recognition

Abstract: Rapid ambulance response is critical for saving human lives during medical emergencies. However, existing emergency transportation systems often suffer from traffic congestion, delayed route optimization, ambulance siren misuse, and lack of intelligent traffic coordination.

This paper presents an AI-Based Emergency Response System integrating facial recognition, GPS-based shortest path navigation, obstacle detection, and OCR-based number plate recognition to improve ambulance efficiency and emergency healthcare transportation.

The proposed system verifies the presence of a genuine patient using facial recognition before activating the ambulance siren. GPS navigation identifies the fastest route to nearby hospitals using live traffic analysis. A front-mounted camera and ultrasonic sensors continuously monitor vehicles obstructing the ambulance path.

Experimental analysis demonstrated a facial recognition accuracy of 96.2%, OCR detection accuracy of 93.4%, and improved route optimization efficiency under simulated traffic conditions. The integration of Artificial Intelligence, Computer Vision, GPS Navigation, and Smart Transportation technologies pro- vides an efficient and scalable solution for modern emergency healthcare systems.

Keywords: Artificial Intelligence, Emergency Response System, Facial Recognition, GPS Navigation, OCR, Ambulance Automation, OpenCV, Smart Healthcare