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Artificial Intelligence and Its Applications in Intelligent Ship Navigation
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Artificial Intelligence and Its Applications in Intelligent Ship Navigation

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: 5 December 2024 | Viewed by 453

Special Issue Editors

Dr. Chenguang Liu

E-Mail Website
Guest Editor
State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan, China
Interests: ship intelligent navigation; motion planning; motion control; formation control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jialun Liu

E-Mail Website
Guest Editor
State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan, China
Interests: testing of intelligent ships; intelligent navigation; ship motion modelling and control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiumin Chu

E-Mail Website
Guest Editor
State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan, China
Interests: intelligent ship; navigation control; environment prediction
Special Issues, Collections and Topics in MDPI journals
Dr. Xiao Lang

E-Mail Website
Guest Editor
Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, Sweden
Interests: maritime digitalization; sustainable shipping; energy efficiency measures; machine learning

Special Issue Information

Dear Colleagues,

As artificial intelligence (AI) continues to develop, autonomous ships have attracted increased amounts of attention with the intention of downsizing the number of staff, increasing efficiency, etc. The deep learning or reinforcement learning network allows more possibilities to improve the intelligence level of ship navigation, which could realize human-like performance in the process of environment perception, decision-making, collision avoidance, and motion control (including berthing and unberthing). Therefore, AI in intelligent ship navigation can boost more applications to assist and even replace officers on board, which is the trend in future autonomous ships in inland waterways and oceans.

In this Special Issue, we welcome contributions from a broad range of theoretical, modeling, field, and laboratory research focused on processes that affect intelligent ships, including, but not limited to, the following topics:

  • Ship intelligent perception with radar, camera, AIS, etc., alone or in combination for the ship navigation environment;
  • Deep learning network for decision-making of ship navigation;
  • Ship navigation behavior analysis;
  • Situation awareness analysis and prediction;
  • Human–machine cooperative navigation;
  • Intelligent collision avoidance with complex encounter scenarios;
  • Ship motion planning with reinforcement learning or other AI algorithms;
  • Ship motion prediction and control, including sailing and berthing;
  • Shore-based remote control;
  • Testing of intelligent ship navigation.

Dr. Chenguang Liu
Prof. Dr. Jialun Liu
Prof. Dr. Xiumin Chu
Dr. Xiao Lang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ship intelligent perception
  • deep learning network for navigation
  • ship navigation behavior analysis
  • navigation situation awareness
  • human–machine cooperative navigation
  • autonomous collision avoidance
  • reinforcement learning for motion planning
  • ship sailing and berthing control
  • shore-based remote control
  • testing of intelligent navigation

Published Papers (1 paper)

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Research

24 pages, 27895 KiB  
Article
Informer-Based Model for Long-Term Ship Trajectory Prediction
by Caiquan Xiong, Hao Shi, Jiaming Li, Xinyun Wu and Rong Gao
J. Mar. Sci. Eng. 2024, 12(8), 1269; https://doi.org/10.3390/jmse12081269 - 28 Jul 2024
Viewed by 301
Abstract
Ship trajectory prediction is a complex time series forecasting problem that necessitates models capable of accurately capturing both long-term trends and short-term fluctuations in vessel movements. While existing deep learning models excel in short-term predictions, they struggle with long-sequence time series forecasting (LSTF) [...] Read more.
Ship trajectory prediction is a complex time series forecasting problem that necessitates models capable of accurately capturing both long-term trends and short-term fluctuations in vessel movements. While existing deep learning models excel in short-term predictions, they struggle with long-sequence time series forecasting (LSTF) due to difficulties in capturing long-term dependencies, resulting in significant prediction errors. This paper proposes the Informer-TP method, leveraging Automatic Identification System (AIS) data and based on the Informer model, to enhance the ability to capture long-term dependencies, thereby improving the accuracy of long-term ship trajectory predictions. Firstly, AIS data are preprocessed and divided into trajectory segments. Secondly, the time series is separated from the trajectory data in each segment and input into the model. The Informer model is utilized to improve long-term ship trajectory prediction ability, and the output mechanism is adjusted to enable predictions for each segment. Finally, the proposed model’s effectiveness is validated through comparisons with baseline models, and the influence of various sequence lengths Ltoken on the Informer-TP model is explored. Experimental results show that compared with other models, the proposed model exhibits the lowest Mean Squared Error, Mean Absolute Error, and Haversine distance in long-term forecasting, demonstrating that the model can effectively capture long-term dependencies in the trajectories, thereby improving the accuracy of long-term vessel trajectory predictions. This provides an effective and feasible method for ensuring ship navigation safety and advancing intelligent shipping. Full article
(This article belongs to the Special Issue Artificial Intelligence and Its Applications in Intelligent Ship Navigation)
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