Review of Emergency Vehicle Detection Techniques by Acoustic Signals

Choudhury, Krishnendu; Nandi, Dalia

doi:10.1007/s41403-023-00424-9

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…The spectrogram and Mfcc serve as inputs for these models. They created a model that demonstrated the potential of the [16] technique with up to 90% accuracy. Models for neural network-based ambient sound detection were reported in papers [5] and [17].…”

Section: Literature Reviewmentioning

confidence: 99%

Smart City Traffic Management: Acoustic-Based Vehicle Detection Using Stacking-Based Ensemble Deep Learning Approach

Shabbir,

Cheema,

Ullah

et al. 2024

IEEE Access

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Acoustic data analysis has emerged as a critical area of exploration for the detection of different events for quick actions in smart traffic management systems, particularly in traffic management and safety for a step toward smart cities. A specific challenge is precisely classifying road noises and emergency vehicles using sound, which is essential for speeding up emergency response times and improving traffic flow management. While existing solutions address this problem, there is opportunity for enhancement in terms of precision and accuracy to enhance the traffic flow in a sustainable smart city through a demanding and innovative technique. In this study, we suggest stacking ensemble deep learning techniques to intelligently classify emergency vehicle sirens from various background noises using a data set of traffic collected on roads via microphone sensors. The ensemble model incorporates Multi-Layer Perceptron (MLP) and Deep Neural Network (DNN) as base-learners, with an LSTM model as a metalearner. This approach not only optimizes model efficiency but also facilitates advanced feature engineering to extract useful features including Mel Frequency Cepstral Coefficients (MFCC), Z-score, root mean square (RMS), spectral centroids, spectral flux, mel spectrogram, chroma, contrast, and Tonnetz. Using these features, our proposed Stacking Ensemble LSTM successfully classified traffic noises and emergency vehicle sirens with the highest efficiency. Upon evaluation of the test set of data for our proposed model, it has gained an accuracy of 99.12% with F1 scores ranging from 98%. This significant improvement highlights the dominance of our proposed model approach over prior research. Our proposed model presents assurance in advancing traffic control and safety statutes, demonstrating potential applicability in daily intelligent transportation systems.INDEX TERMS Data-driven urban planning; smart city technologies; intelligent transportation systems; smart urban infrastructure; sustainable urban development; Smart traffic management; urban mobility solutions; urban sensor networks;

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Section: Literature Reviewmentioning

confidence: 99%

Smart City Traffic Management: Acoustic-Based Vehicle Detection Using Stacking-Based Ensemble Deep Learning Approach

Shabbir,

Cheema,

Ullah

et al. 2024

IEEE Access

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An advanced multimodal driver-assistance prototype for emergency-vehicle detection

Gabrielli,

Migliorelli,

Cantarini

et al. 2024

ICA

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In the automotive industry, intelligent monitoring systems for advanced human-vehicle interaction aimed at enhancing the safety of drivers and passengers represent a rapidly growing area of research. Safe driving behavior relies on the driver’s awareness of the road context, enabling them to make appropriate decisions and act consistently in anomalous circumstances. A potentially dangerous situation can arise when an emergency vehicle rapidly approaches with sirens blaring. In such cases, it is crucial for the driver to perform the correct maneuvers to prioritize the emergency vehicle. For this purpose, an Advanced Driver Assistance System (ADAS) can provide timely alerts to the driver about an approaching emergency vehicle. In this work, we present a driver-assistance prototype that leverages multimodal information from an integrated audio and video monitoring system. In the initial stage, sound analysis technologies based on computational audio processing are employed to recognize the proximity of an emergency vehicle based on the sound of its siren. When such an event occurs, an in-vehicle monitoring system is activated, analyzing the driver’s facial patterns using deep-learning-based algorithms to assess their awareness. This work illustrates the design of such a prototype, presenting the hardware technologies, the software architecture, and the deep-learning algorithms for audio and video data analysis that make the driver-assistance prototype operational in a commercial car. At this initial experimental stage, the algorithms for analyzing the audio and video data have yielded promising results. The area under the precision-recall curve for siren identification stands at 0.92, while the accuracy in evaluating driver gaze orientation reaches 0.97. In conclusion, engaging in research within this field has the potential to significantly improve road safety by increasing driver awareness and facilitating timely and well-informed reactions to crucial situations. This could substantially reduce risks and ultimately protect lives on the road.

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Review of Emergency Vehicle Detection Techniques by Acoustic Signals

Cited by 2 publications

References 26 publications

Smart City Traffic Management: Acoustic-Based Vehicle Detection Using Stacking-Based Ensemble Deep Learning Approach

Smart City Traffic Management: Acoustic-Based Vehicle Detection Using Stacking-Based Ensemble Deep Learning Approach

An advanced multimodal driver-assistance prototype for emergency-vehicle detection

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