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Tomasz Neumann
 

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TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
www http://www.transnav.eu
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Intelligent Autonomous Ship Navigation using Multi-Sensor Modalities
1 GMATEK, Inc., Annapolis, MD, USA
ABSTRACT: This paper explores the use of machine learning and deep learning artificial intelligence (AI) techniques as a means to integrate multiple sensor modalities into a cohesive approach to navigation for autonomous ships. Considered is the case of a fully autonomous ship capable of making decisions and determining actions by itself without active supervision on the part of onboard crew or remote human operators. These techniques, when combined with advanced sensor capabilities, have been touted as a means to overcome existing technical and human limitations as unmanned and autonomous ships become operational presently and in upcoming years. Promises of the extraordinary capabilities of these technologies that may even exceed those of crewmembers for decision making under comparable conditions must be tempered with realistic expectations as to their ultimate technical potential, their use in the maritime domain, vulnerabilities that may preclude their safe operation; and methods for development, integration and test. The results of research performed by the author in specific applications of machine learning and AI to shipping are presented citing key factors that must be achieved for certification of these technologies as being suitable for their intended purpose. Recommendations are made for strategies to surmount present limitations in the development, evaluation and deployment of intelligent maritime systems that may accommodate future technological advances. Lessons learned that may be applied to improve safety of navigation for conventional shipping are also provided.
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Citation note:
Wright R.G.: Intelligent Autonomous Ship Navigation using Multi-Sensor Modalities. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 13, No. 3, doi:10.12716/1001.13.03.03, pp. 503-510, 2019

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