Authors:
Matteo Sammarco
1
and
Marcin Detyniecki
2
Affiliations:
1
AXA Data Innovation Lab, France
;
2
AXA Data Innovation Lab, Sorbonne Universités, UPMC Univ Paris 06 and Polish Academy of Sciences, France
Keyword(s):
Real-time Incident Detection, Road Safety, Smart Vehicle, Audio Signal Processing.
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 dat
aset 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.
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