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The rapid movement of emergency vehicles through intersections is very important for efficient response time and saving lives. The current control systems rely mainly on GPS technology for preemption and sirens for manual detection, which might fail because of connectivity problems and lack of autonomous detection systems. The paper proposes an efficient and cost-effective solution for an edge-based solution for emergency vehicle detection with an ESP32-CAM and a microphone module for the analysis of sirens in real time. The solution analyzes the frequency range of sirens (700-1600 Hz) using Fast Fourier Transform (FFT) analysis and then uses a direction estimation module for the source of the siren. The camera module is directed towards the source of the siren. The optional solution for better prediction is a GPS app for drivers. The results show that the solution has an accuracy of 96% for real-time siren detection with a response time of 1.5 seconds from the system.
"AI-Powered Predictive Traffic Signal Control for Emergency Response Optimization", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 6, page no.b50-b54, June-2026, Available :http://www.ijrti.org/papers/IJRTI2606113.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator