This research aims to provide a timely and accurate accident detection method at intersections, which is very important for the Traffic Management System. This research uses acoustic signals to detect accidents at intersections. A system is constructed that can be operated in two modes: two-class and multi-class. The input to the system is a three-second segment of audio signal. The output of the two-class mode is a label of "crash" or "non-crash". In the multi-class mode of operation, the system identifies crashes as well as several types of non-crash incidents, including normal traffic and construction sounds. The system is composed of three main signal processing stages: feature extraction, feature reduction, and feature classification. Five methods of feature extraction are investigated and compared; these are based on the discrete wavelet transform, fast Fourier transform, discrete cosine transform, real cepstral transform, and mel frequency cepstral transform. Statistical methods are used for feature optimization and classification. Three types of classifiers are investigated and compared: the nearest mean, maximum likelihood, and nearest neighbor methods. This study focuses on the detection algorithm development. Lab testing of the algorithm showed that the selected algorithm can detect intersection accidents with very high accuracy.
Abstract