Crashzam: Sound-based Car Crash Detection

Matteo Sammarco, Marcin Detyniecki

2018

Abstract

Connected vehicles, combined with embedded smart computation capabilities, will certainly lead to a new generation of services and opportunities for drivers, car manufacturers, insurance and service companies. One of the main challenges remaining in this field is how to detect key triggering events. One of these crucial moments is a car accident, for which not only smart connected vehicles can improve drivers’ safety as car accidents are still one of the main causes of fatalities worldwide, but also help them during minor, but very stressful moments. In this paper, we present Crashzam which is an innovative way to detect any type car accidents based on sound produced by car impact, while, so far, crash detection is only a prerogative of accelerometer sensor time series analysis, or its proxy: activation of the airbag. We describe the system design, the sound detection model, and the results based on a dataset with in-car cabin sounds of real crashes. We have beforehand built such dataset with real car accident sounds. Classification is built upon features extracted from the time and frequency domain of the audio signal and from its spectrogram image. Results show that the proposed model is able to easily identify crash sounds from other sounds reproduced in-car cabins. Moreover, considering that Crashzam can run on smartphones, it is a low cost and energy solution, contributing to the spreading of such a car safety feature and reducing delays in providing assistance when an accident occurs.

Paper Citation

in Harvard Style

Sammarco M. and Detyniecki M. (2018). Crashzam: Sound-based Car Crash Detection.In Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-293-6, pages 27-35. DOI: 10.5220/0006629200270035

in Bibtex Style

@conference{vehits18,
author={Matteo Sammarco and Marcin Detyniecki},
title={Crashzam: Sound-based Car Crash Detection},
booktitle={Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2018},
pages={27-35},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006629200270035},
isbn={978-989-758-293-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Crashzam: Sound-based Car Crash Detection
SN - 978-989-758-293-6
AU - Sammarco M.
AU - Detyniecki M.
PY - 2018
SP - 27
EP - 35
DO - 10.5220/0006629200270035