title: An Information Retrieval Approach for Robust Prediction of Road Surface States.
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.
authors.affiliation: ICT Convergence R & D Center, Metabuild Co., Ltd., 5F 1487-6 Seocho-3dong, Seocho-gu, Seoul 06708, Korea. jhpark@metabuild.co.kr.
authors.forename: Jae-Hyung
authors.initials: JH
authors.lastname: Park
authors.affiliation: Department of Industrial and Management Engineering, College of Engineering, Incheon National University, Incheon 22012, Korea. khokim@inu.ac.kr.
authors.forename: Kwanho
authors.initials: K
authors.lastname: Kim
keywords: exponential moving average; information retrieval; machine learning; ranking and scoring functions; road surface radar; road surface state detection; smart highway