Development of an AI-Enabled Smart ambulance system for real-time emergency response and traffic navigation

Delay in ambulance arrival during an emergency remains a major cause of avoidable damage, especially in congested areas and under-resourced regions. This project presents the design and development of an artificial intelligence ambulance detection and alert system using public space CCTV cameras. The system uses a Raspberry Pi as the central processing unit, running a deep learning model built on TensorFlow and trained with MobileNetV2 to identify an ambulance. Upon successful detection, an integrated buzzer is triggered for immediate local alert, while a GSM module sends SMS notifications to the nearby hospital or emergency response unit. The system was mechanically designed using TinkerCAD, with a 3D model that optimally arranges the camera, Raspberry Pi, and peripheral components. Circuit design and simulation were conducted using Cirkit Designer to ensure electrical stability and compatibility. This AI-enabled system demonstrated 96% detection accuracy and high precision, providing a reliable, cost-effective, and scalable alternative to GPS or a manual tracking approach. It offers a vital improvement to emergency medical response workflow, particularly in developing environments where smart healthcare infrastructure is still emerging.