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Enhancing Road Safety with AI-Powered System for Effective Detection and Localization of Emergency Vehicles by Sound
Lucas Banchero1, Francisco Vacalebri-Lloret1, Jose M Mossi1
1Institute of Telecommunications and Multimedia Applications, Universitat Politecnica de Valencia, 46022 Valencia, Spain.
Sensors (Basel, Switzerland)
|February 13, 2025
Summary
This study introduces an AI-powered system for detecting and locating emergency vehicle sounds like sirens. The technology enhances road safety by providing drivers with real-time alerts about approaching emergency vehicles.
Area of Science:
- Automotive Engineering
- Artificial Intelligence
- Acoustic Signal Processing
Background:
- Road safety is a critical concern in urban environments, often compromised by ambient noise and the inability to detect emergency vehicles promptly.
- Existing driver assistance systems lack robust capabilities for real-time detection and localization of auditory warnings such as sirens and horns.
Purpose of the Study:
- To design and implement an advanced system for detecting and localizing emergency sounds (sirens, horns) in automotive settings.
- To enhance road safety by providing drivers with accurate, real-time directional and proximity information of emergency vehicles.
- To overcome challenges posed by complex acoustic environments, including urban traffic and wind noise.
Main Methods:
- Integration of specialized hardware with an aerodynamic structure to minimize wind noise and vibrations.
- Application of artificial intelligence, including transformer-based architectures and convolutional neural networks (ResNets, U-NET), for sound analysis.
- Real-time processing using sliding windows for immediate detection, localization, noise cleaning, and trajectory analysis.
Main Results:
- High detection accuracy achieved: 98.86% for simulated data and 97.5% for real-world measurements.
- Accurate sound localization demonstrated with an average error of 5.12° in simulations and 10.30° in real-world tests.
- Effective noise reduction and sound cleaning in challenging acoustic conditions.
Conclusions:
- The developed system effectively detects and localizes emergency sounds, significantly improving upon existing road safety measures.
- The AI-driven approach, utilizing advanced neural networks and real-time processing, shows strong potential for integration into automotive driver assistance systems.
- This technology offers a substantial advancement in mitigating risks associated with emergency vehicle approaches on the road.
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