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2023 Journal Impact Factor - 0.7
2023 CiteScore - 1.4
ISSN 2083-6473
ISSN 2083-6481 (electronic version)
Editor-in-Chief
Associate Editor
Prof. Tomasz Neumann
Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
e-mail transnav@umg.edu.pl
The Machine Learning Method of PIDVCA
ABSTRACT: Building a dynamic collision knowledge base of self-learning is one of the core contents of implementing "personified intelligence" in Personifying Intelligent Decision-making for Vessel Collision Avoidance (short for PIDVCA). In the paper, the machine learning method of PIDVCA combined with offline artificial learning and online machine learning is proposed. The static collision avoidance knowledge is acquired through offline artificial learning, and the isomeric knowledge representation integration method with process knowledge as the carrier is established, and the Dynamic collision avoidance knowledge is acquired through online machine learning guided by inference engine. A large number of simulation results show that the dynamic collision avoidance knowledge base constructed by machine learning can achieve the effect of anthropomorphic intelligent collision avoidance. It is verified by examples that the machine learning method of PIDVCA can realize target perception, target cognition and finally obtain an effective collision avoidance decision-making.
KEYWORDS: Collision Avoidance, Personifying Intelligent Decision-making for Vessel Collision Avoidance (PIDVCA), Learning Methods, Machine Learning Method, Personified Intelligence, Dynamic Collision Avoidance, Static Collision Avoidance
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Citation note:
Li L.N., Wang X.H., Chen G.: The Machine Learning Method of PIDVCA. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 14, No. 3, doi:10.12716/1001.14.03.02, pp. 533-540, 2020
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