Classification of Road Surfaces Based on CNN Architecture and Tire Acoustical Signals

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-pavement 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.

Automated summary

This paper presents a novel approach for road surface classification using deep learning method-based CNN architecture. Traditional methods using accelerometers and vision sensors have some limitations. This study adopts TPIN as a data source and uses a CNN architecture for classification, and the results show that this method is feasible.