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General Information
Prof. Adrian Olaru
University Politehnica of Bucharest, Romania
I'm happy to take on the position of editor in chief of IJMO. It's a journal that shows promise of becoming a recognized journal in the area of modelling and optimization. I'll work together with the editors to help it progress.
IJMO 2018 Vol.8(2): 87-94 ISSN: 2010-3697
DOI: 10.7763/IJMO.2018.V8.630

Apprenticeship Learning of Ship Behavior in Crowded Area by Dimension Compression

Yuxin Liang and Masayoshi Mase
Abstract—There is a strong demand for autonomous ship navigation systems in maritime logistics. Such systems need to be able to forecast behaviors of other ships accurately to avoid collisions. Here, time-series of ship positions, called AIS data, can be used in apprenticeship learning (AL) by defining an object map created from the data as a state and the turning direction of the ship as an action. However, when we analyzed 1 months’ worth of AIS data, none of the generated path data took actions in the same state pattern twice. This paper proposes to use a Co-Moving Frame (CMF), a local segment of the environment on a small timescale. CMF improved the effectiveness of the data usage, and as a result, AL forecast paths of ships with 81.2% accuracy when applying CMF. This result is 29.2% better than that of a state transition model generated from the same dataset without applying CMF.

Index Terms—Apprenticeship Learning, Neural Network, Q-learning, Ship, AIS

Yuxin Liang and Masayoshi Mase are with Hitachi, Ltd, Kokubunji, Tokyo 1858601 Japan (e-mail: yuxin.liang.na@hitachi.com, masayoshi.mase.mh@hitachi.na.com).


Cite: Yuxin Liang and Masayoshi Mase, "Apprenticeship Learning of Ship Behavior in Crowded Area by Dimension Compression," International Journal of Modeling and Optimization vol. 8, no. 2, pp. 87-94, 2018.

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