Next Article in Journal
Analytical Protein Microarrays: Advancements Towards Clinical Applications
Next Article in Special Issue
Replacement Condition Detection of Railway Point Machines Using an Electric Current Sensor
Previous Article in Journal
THz Pyro-Optical Detector Based on LiNbO3 Whispering Gallery Mode Microdisc Resonator
Previous Article in Special Issue
Trust-Based Cooperative Social System Applied to a Carpooling Platform for Smartphones
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(2), 262; doi:10.3390/s17020262

An Information Retrieval Approach for Robust Prediction of Road Surface States

1
ICT Convergence R & D Center, Metabuild Co., Ltd., 5F 1487-6 Seocho-3dong, Seocho-gu, Seoul 06708, Korea
2
Department of Industrial and Management Engineering, College of Engineering, Incheon National University, Incheon 22012, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Simon X. Yang
Received: 29 November 2016 / Revised: 19 January 2017 / Accepted: 23 January 2017 / Published: 28 January 2017
(This article belongs to the Special Issue Sensors for Transportation)
View Full-Text   |   Download PDF [4403 KB, uploaded 10 February 2017]   |  

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. View Full-Text
Keywords: road surface state detection; road surface radar; smart highway; information retrieval; machine learning; ranking and scoring functions; exponential moving average road surface state detection; road surface radar; smart highway; information retrieval; machine learning; ranking and scoring functions; exponential moving average
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Park, J.-H.; Kim, K. An Information Retrieval Approach for Robust Prediction of Road Surface States. Sensors 2017, 17, 262.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top