Abstract
This paper presents the method of classifying the road surface type using tire-pavement interaction noise (TPIN) signal. TPIN refers to a noise caused by interactions between rolling tires and road surfaces and is measured using the rugged microphone installed in the wheel cover of tire. The road surface information is identified using continuous wavelets transform (CWT). CWT images for the measured TPIN are used as the input of Convolutional neural network (CNN). The CNN extracts the feature for road surface throughout convolution and pooling process and classifies the road surface type in fully connected neural network. Two road surfaces, snow road and asphalt road, are classified using a method that combines the CWT and CNN methods The results indicate an accuracy of over 97 %. Two different tires are used for the experiment. Results from the road classification can be used to control the braking systems of autonomous vehicle in future.
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