Development of an AI-enabled smart ambulance system for real-time emergency response and traffic navigation
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Date
2026
Journal Title
Journal ISSN
Volume Title
Publisher
KIU Journal of Science, Engineering and Technology
Abstract
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.
Description
Keywords
Ambulance Detection, Public Space Camera, Raspberry Pi, Deep Learning, TinkerCAD
Citation
Saminu, S. Tunmise, O.S. Yahaya, S. A. Muniru, I. O. Salaudeen A.H. Salaudeen, K.O. Kabir, M.A. Sanusi, A. Hauwa, M.H. (2026): Development of an AI-enabled smart ambulance system for real-time emergency response and traffic navigation. KIU Journal of Science, Engineering and Technology, 5(1), 14-23. published by Kampala International University (KIU)