A Comparative Study of Estimating Road Surface Condition Using Support Vector Machine and Deep Neural Networ

In this paper, we present a comparative study of two machine learning methods to estimate the road surface condition without directly estimating tire-road friction coefficient. It is well known that using either a vehicle model-based approach or an end-to-end artificial intelligent method is not satisfactory to estimate the tire-road friction coefficient due to sensor noise, parameter uncertainty, and disturbances. To cope with this problem, three feature vectors obtained based on the vehicle dynamics are utilized for support vector machine (SVM) and deep neural network (DNN) with a time-window approach. The effectiveness of the proposed method is verified using experimental data obtained with a test vehicle on proving grounds. From the experimental study, we observed that the road surface condition estimation using DNN is superior to that using SVM.