Enhancing Road Safety with AI-Powered System for Effective Detection and Localization of Emergency Vehicles by Sound

This work presents the design and implementation of an emergency sound detection and localization system, specifically for sirens and horns, aimed at enhancing road safety in automotive environments. The system integrates specialized hardware and advanced artificial intelligence algorithms to function effectively in complex acoustic conditions, such as urban traffic and environmental noise. It introduces an aerodynamic structure designed to mitigate wind noise and vibrations in microphones, ensuring high-quality audio capture. In terms of analysis through artificial intelligence, the system utilizes transformer-based architecture and convolutional neural networks (such as residual networks and U-NET) to detect, localize, clean, and analyze nearby sounds. Additionally, it operates in real-time through sliding windows, providing the driver with accurate visual information about the direction, proximity, and trajectory of the emergency sound. Experimental results demonstrate high accuracy in both controlled and real-world conditions, with a detection accuracy of 98.86% for simulated data and 97.5% for real-world measurements, and localization with an average error of 5.12° in simulations and 10.30° in real-world measurements. These results highlight the effectiveness of the proposed approach for integration into driver assistance systems and its potential to improve road safety.