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Intelligent autonomous ship navigation using multi-sensor modalities

Wright R.G.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2019

Vol. 13, no. 3

503--510

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.

Named Entity Disambiguation for Maritime-related Data Retrieved from Heterogenous Sources

Małyszko J., Abramowicz W., Stróżyna M.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2016

Vol. 10, no. 3

465--477

The article concerns integration and disambiguation of data related to the maritime domain. A developed system is described, which collects and merges data about several maritime-related entities (vessels, vessel types, ports, companies etc.) retrieved from different internet sources and feeds the data into a single database. This process is however not trivial. There are few challenges, which need to be faced to successfully conduct it. Firstly, in different sources, entities may be referenced to in different ways, for example, by using different text strings. Additionally, some of these references may be ambiguous, i.e. potentially the reference may point to more than one entity. To enable efficient analysis of data coming from different sources, such ambiguities must be resolved automatically as a preprocessing step, before the data is uploaded to the database and utilized in further computations. The aim of the disambiguation process is to assign artificial, unique identifiers to each entity and then, if possible, automatically assign these identifiers to each data item related to a given entity. In the article, developed methods for resolving such ambiguities are discussed and their evaluation is presented.