A Road Condition Classification Algorithm for a Tire Acceleration Sensor using an Artificial Neural Network
Hyeong-Jun Kim, Jun-Young Han, Suk Lee, Jae-Ryon Kwag, Min-Gu Kuk, In-Hyuk Han, Man-Ho Kim
Abstract
The automotive industry is experiencing a period of innovation, represented by the term CASE (connected, autonomous, shared, and electric). Among the innovative new technologies for automobiles, intelligent tire (iTire) collects road surface information through sensors installed inside a tire and informs the driver of the road conditions. iTire can promote safe driving. Various kinds of research on iTire is ongoing, and this paper proposes an algorithm to determine the road surface conditions while driving. Specifically, we have proposed a method for extracting the feature points of a frequency band, by converting acceleration data collected by sensors through fast Fourier transform (FFT) and determining road surface conditions via an artificial neural network. Lastly, the applicability of the algorithm was verified.