# Classification of Road Surfaces Based on CNN Architecture and Tire Acoustical Signals
###### Abstract  
This paper presents a novel work for classification of road surfaces using deep learning method-based convolutional neural network (CNN) architecture. With the development of advanced driver assistance system (ADAS) and autonomous driving technologies, the need for research on vehicle state recognition has increased. However, research on road surface classification has not yet been conducted. If road surface classification and recognition are possible, the control system can make a more robust decision by validating the information from other sensors. Therefore, road surface classification is essential. To achieve this, tire-pavent interaction noise (TPIN) is adopted as a data source for road surface classification. Accelerometers and vision sensors have been used in conventional approaches. The disadvantage of acceleration signals is that they can only represent the surface profile properties and are masked by the resonance characteristics of the car structure. An image signal can be easily contaminated by factors such as illumination, obstacles, and blurring while driving. However, the TPIN signal reflects the surface profile properties of the road and its texture properties. The TPIN signal is also robust compared to those in which the image signal is affected. The measured TPIN signal is converted into a 2-dimensional image through time-frequency analysis. Converted images were used together with a CNN architecture to examine the feasibility of the road surface classification system.  
convolutional neural network; road surface classification; wavelet analysis +
Footnote †: journal: Applied  
image processing [6] and convolutional neural network (CNN) architecture [7]. Despite many approaches and studies, there have been no studies on the classification of snow-covered roads that require precise brake control. Therefore, research identifying road surfaces by considering tire patterns is required to increase the robustness of the control system. In this study, a new road surface classification method that includes snow road is presented based on a tire-pavement interaction noise (TPIN) signal and a deep learning train using a CNN architecture. Two tires made by two different companies and two road surfaces were used for this method. Thus, there were four classes. The aim of this study is to determine if the road has snow on the road to use the results for safety analysis autonomous driving technologies in the future. The effect of tire type is considered for this classification. The TPIN signal is obtained using a rugged microphone installed on the wheel cover, and it is transformed into 2-dimensional (2D) image data using continuous wavelet transform (CWT). The image signal is used as the input to the CNN architecture. A new CNN architecture was constructed for the classification of two road surface types and two tires. The remainder of this paper is organized as follows. Section 2 contains fundamental theories regarding CWT, convolutional neural networks, and TPIN. Section 3 presents the experimental setup, test environment, and result analysis. Section 4 focuses on the preprocessing technique and a comparison of the resulting CWT images. Section 5 addresses the design of the neural network, training setup, and network performance. Finally, Section 6 discusses the overall research process.