Abstract
Today’s automobiles are equipped with an increasing variety of safety features, yet the use of acoustic methods for automobile crash prevention and detection has been somewhat limited even though the acoustic waves generated during such events can offer valuable information. For example, the high-pitched squealing caused by tire skidding can provide advance warning especially if it is caused by an adjacent car. During car collisions, the elastic waves traveling along the steel car frame are 17 times faster than the speed of sound in air, which can signal a crash more promptly than center-mounted acceleration sensors. To make full use of the high-speed acoustic signals, a wavelet-based algorithm implementable in real-time has been developed to isolate and detect specific pre-crash and crash events such as honking, tire skidding and collision in multi-channel acoustic datasets. The proposed algorithm offers distinct advantages in sudden onset detection, temporal localization accuracy, and computational cost over existing time- and frequency-domain methods. Results demonstrated on a crash scenario are indicative of a substantial enhancement in automobile pre-crash and crash detection performance by acoustic methods.
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