自動車のタイヤ騒音を用いた路面状況の自動検出

概要

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This paper proposes a new method for automatically detecting the states of the road surface from tire noises of vehicles. The methods are based on a Fast Fourier Transform analysis, an artificial neural network, and the mathematical theory of evidence. The proposed classification is carried out in sets of multiple neural networks using the learning vector quantization networks. The outcomes of the networks are then integrated using the voting decision making scheme. It seems then feasible to detect passively and readily the states of the surface: i.e., as a rule of thumb, dry, wet, snowy and slushy state, automatically. Preliminary classification results for an independent validation set yielded 81.6% correct classification. This was improved to 91% by addition of information about the early state in a final decision.
2009-01-22