Road Type Identification Ahead of the Tire Using D-CNN and Reflected Ultrasonic Signals

Abstract

Every land moving object accelerates or decelerates based on the fictional coefficient of the road surface. It has been known that this coefficient on the road is determined by the type of road surface. In this work, we propose a simplistic, machine-learning based solution to estimate the road type using the reflected ultrasonic signals paired with ultrasonic transmitter and receiver. Since the reflected signal contains the material information of the surface due to the difference in the surface roughness and acoustic impedance, different characteristics can be observed for each frequency of the reflected signal. To exploit such characteristics, the signals are transformed into the frequency domain using short-time Fourier transform. In addition, a deep convolutional neural network is applied as the road identifier due to its well-known representational power. In order to verify the aforementioned ideas, the ample database consisting of eight types of road surfaces are obtained with the ultrasonic sensors. And then, the database is used to train the model, as well as to evaluate the accuracy of the trained model. It can be seen that the proposed method makes it easier and more accurate to identify the type of road surface than the conventional methods.