Road type classification using deep learning for Tire-Pavement interaction noise data in autonomous driving vehicle

Abstract

This study presents a novel artificial intelligence model (AIM) for the real-time classification of 13 different road types in an autonomous vehicle. The model was developed based on a combination of a continuous wavelet transform (CWT) and convolutional neural network (CNN). Previously, three methods have been used for the classification of road types, depending on the type of sensor. First, a camera sensor has been widely used because it can capture the road type directly. Second, a vibration sensor has been used, since the vibration level measured on the suspension or inside the tire depends on the road type. Finally, an acoustic sensor has been used, especially in measuring tire–pavement interaction noise (TPIN). In a previous feasibility study, an AIM was developed to classify road types using TPIN signals, which vary depending on the road type. It can distinguish between two road types: asphalt and snow. Recently, CNN has been widely used as an AIM for classification, but it is limited as the input size of the CNN should be optimized for real-time processing due to its long calculation times, even for a 2D convolution process. Its input is image data, which can be produced through the CWT of the TPIN signal. This study proposes an AIM that can classify 13 different road surfaces in real-time while driving. In this study, a method to determine the optimal filter band and data length used for CWT is proposed. The method was developed based on the classification accuracy of an AIM. The developed AIM was successfully applied to the real-time classification of road types with an accuracy of 95% on a public road.