Citation

Park JH, Kim K. Sensors (Basel) 2017; 17(2): s17020262.

Affiliation

Department of Industrial and Management Engineering, College of Engineering, Incheon National University, Incheon 22012, Korea. khokim@inu.ac.kr.

Copyright

(Copyright © 2017, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s17020262

PMID

28134859

Abstract

Recently, due to the increasing importance of reducing severe vehicle accidents on roads (especially on highways), the automatic identification of road surface conditions, and the provisioning of such information to drivers in advance, have recently been gaining significant momentum as a proactive solution to decrease the number of vehicle accidents. In this paper, we firstly propose an information retrieval approach that aims to identify road surface states by combining conventional machine-learning techniques and moving average methods. Specifically, when signal information is received from a radar system, our approach attempts to estimate the current state of the road surface based on the similar instances observed previously based on utilizing a given similarity function. Next, the estimated state is then calibrated by using the recently estimated states to yield both effective and robust prediction results. To validate the performances of the proposed approach, we established a real-world experimental setting on a section of actual highway in South Korea and conducted a comparison with the conventional approaches in terms of accuracy. The experimental results show that the proposed approach successfully outperforms the previously developed methods.

Language: en

Keywords

exponential moving average; information retrieval; machine learning; ranking and scoring functions; road surface radar; road surface state detection; smart highway