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Maritime Artificial Intelligence Convergence Research
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Maritime Artificial Intelligence Convergence Research

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: 1 February 2025 | Viewed by 806

Special Issue Editors

Dr. Hyun Yang

E-Mail Website
Guest Editor
Division of Maritime AI & Cyber Security, Korea Maritime and Ocean University, Busan, Republic of Korea
Interests: maritime artificial intelligence
Dr. Xinqiang Chen

E-Mail Website
Guest Editor
Insitute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
Interests: video data-driven intelligent transportation environment perception and understanding; large-scale transportation data analysis (traffic flow data, AIS, etc.); smart ship/autonomous port
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, the maritime field and industry have undergone a transformative shift with the convergence of artificial intelligence (AI) technologies. This convergence is driving innovations in autonomous navigation, the optimization of route planning and following, predictive maintenance, and enhanced safety protocols, among others. This Special Issue invites researchers, scholars, and practitioners to submit their original research articles, review papers, and case studies on "Maritime Artificial Intelligence Convergence Research" and aims to showcase cutting-edge research and developments in the field of AI applications within the maritime sector. We encourage submissions on a wide range of topics, including but not limited to the following:

- Autonomous ship navigation;

- AI-driven optimization of route planning and following;

- Vessel trajectory prediction based on AI;

- Predictive maintenance and fault diagnosis;

- Machine learning for maritime safety and risk management;

- AI-based environmental monitoring and protection;

- Integration of IoT and AI in maritime operations;

- Case studies on AI implementation in maritime logistics;

- Ethical and regulatory issues in maritime AI.

Dr. Hyun Yang
Dr. Xinqiang Chen
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

  • maritime AI Convergence
  • autonomous navigation
  • AI-based prediction
  • intelligent marine systems
  • maritime big data
  • AI for maritime applications

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Published Papers (2 papers)

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Research

20 pages, 3379 KiB  
Article
A Balanced Path-Following Approach to Course Change and Original Course Convergence for Autonomous Vessels
by Won-Jin Choi and Jeong-Seok Lee
J. Mar. Sci. Eng. 2024, 12(10), 1831; https://doi.org/10.3390/jmse12101831 - 14 Oct 2024
Abstract
This paper proposes a novel path-following method for autonomous ships that optimizes overall performance by balancing course changes and convergence to the original route. The proposed method extends the line-of-sight (LOS) guidance law by dynamically adjusting key parameters based on the ship’s cross-track [...] Read more.
This paper proposes a novel path-following method for autonomous ships that optimizes overall performance by balancing course changes and convergence to the original route. The proposed method extends the line-of-sight (LOS) guidance law by dynamically adjusting key parameters based on the ship’s cross-track error (XTE) and the distance of new course considering maneuvering characteristics. By incorporating these maneuvering characteristics, the method enables more precise adjustments during course changes, improving overall path-following performance. Simulation results showed that the proposed method outperformed three existing methods, including the traditional LOS guidance law, by minimizing overshoot and maintaining reasonable XTE during larger course changes. It achieved the lowest mean absolute cross-track error (MAE) while also significantly reducing the total time required to follow the path, highlighting its superior accuracy and efficiency in path following. These outcomes highlight the method’s potential to enhance significantly the path-following capabilities of autonomous vessels, contributing to greater efficiency and accuracy in pre-determined route navigation. Full article
(This article belongs to the Special Issue Maritime Artificial Intelligence Convergence Research)
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25 pages, 7123 KiB  
Article
Vessel Trajectory Prediction at Inner Harbor Based on Deep Learning Using AIS Data
by Gil-Ho Shin and Hyun Yang
J. Mar. Sci. Eng. 2024, 12(10), 1739; https://doi.org/10.3390/jmse12101739 - 2 Oct 2024
Viewed by 400
Abstract
This study aims to improve vessel trajectory prediction in the inner harbor of Busan Port using Automatic Identification System (AIS) data and deep-learning techniques. The research addresses the challenge of irregular AIS data intervals through linear interpolation and focuses on enhancing the accuracy [...] Read more.
This study aims to improve vessel trajectory prediction in the inner harbor of Busan Port using Automatic Identification System (AIS) data and deep-learning techniques. The research addresses the challenge of irregular AIS data intervals through linear interpolation and focuses on enhancing the accuracy of predictions in complex port environments. Recurrent neural network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional GRU models were developed, with LSTM delivering the highest performance. The primary scientific question of this study is how to reliably predict vessel trajectories under varying conditions in inner harbors. The results demonstrate that the proposed method not only improves the precision of predictions but also identifies critical areas where Vessel Traffic Service Operators (VTSOs) can better manage vessel movements. These findings contribute to safer and more efficient vessel traffic management in ports with high traffic density and complex navigational challenges. Full article
(This article belongs to the Special Issue Maritime Artificial Intelligence Convergence Research)
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