Beware the Sirens: Prototyping an Emergency Vehicle Detection System for Smart Cars

Cantarini, Michela; Gabrielli, Leonardo; Migliorelli, Lucia; Mancini, Adriano; Squartini, Stefano

doi:10.1007/978-3-031-24801-6_31

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…A robust and accurate vehicle localization system consisting of a bistatic passive radar is proposed, which exhibits better performance than the existing sparse signal representation-based algorithms, and performs well in the vehicle localization system [18]. Cantarini et al [19] introduces a prototype of an emergency vehicle detection system based on audio and video sensors. The author used a four channel microphone array to capture sound and used a spectral correlation-based DOA algorithm to estimate the direction of the sound source.…”

Section: Introductionmentioning

confidence: 99%

Sparse Non-Uniform Linear Array-Based Propagator Method for Direction of Arrival Estimation

Mo,

Tong,

Wang

et al. 2023

Electronics

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A novel approach that does not require the number of sources as a priori is proposed to estimate the direction of arrival (DOA) based on a sparse non-uniform linear antenna array. To ensure the identifiability of the DOA, a specific configuration scheme of sparse array is designed. Based on this specific sparse array, firstly the fourth-order cumulant (FOC) is adopted to eliminate the impact imposed by Gaussian noise. Secondly, to circumvent eigenvalue decomposition or singular value decomposition, a propagator is constructed by using a Hermitian FOC matrix and a hyperparameter. Finally, a projection onto an irregular Toeplitz set is proposed to further improve estimation accuracy.

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Section: Introductionmentioning

confidence: 99%

Sparse Non-Uniform Linear Array-Based Propagator Method for Direction of Arrival Estimation

Mo,

Tong,

Wang

et al. 2023

Electronics

View full text Add to dashboard Cite

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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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Beware the Sirens: Prototyping an Emergency Vehicle Detection System for Smart Cars

Cited by 2 publications

References 31 publications

Sparse Non-Uniform Linear Array-Based Propagator Method for Direction of Arrival Estimation

Sparse Non-Uniform Linear Array-Based Propagator Method for Direction of Arrival Estimation

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

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