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Applied Sciences
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Advances in Intelligent Maritime Navigation and Ship Safety
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Advances in Intelligent Maritime Navigation and Ship Safety

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: closed (20 February 2025) | Viewed by 4411

Special Issue Editors

Prof. Dr. Ho Namgung

E-Mail Website
Guest Editor
Department of Maritime Industry Convergence, Mokpo National Maritime University, Mokpo 58628, Republic of Korea
Interests: collision avoidance; fuzzy inference system; advanced machine learning; artificial intelligence; maritime autonomous surface ships; local route planning; information exchange
Prof. Dr. Joo-Sung Kim

E-Mail Website
Guest Editor
Division of Navigation Science, Mokpo National Maritime University, Mokpo 58628, Republic of Korea
Interests: machine learning; anomaly detection; artificial intelligence; collision avoidance; path planning
Prof. Dr. Kwang-il Kim

E-Mail Website
Guest Editor
Department of Marine Industry and Maritime Police, Jeju National University, Jeju 63243, Republic of Korea
Interests: ship transportation; deep learning; maritime artificial intelligent; maritime big data
Dr. Dusica Marijan

E-Mail Website
Guest Editor
Department of Validation Intelligence for Autonomous Software Systems, Simula Research Laboratory, 0164 Oslo, Norway
Interests: artificial intelligence; machine learning; autonomous systems; autonomous shipping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing need for water-based navigation has created a strong demand for maritime autonomous surface ships (MASSs). In particular, research in the fields of path planning, autonomous navigation, and collision avoidance has started to become increasingly relevant for maritime transport. The use of artificial intelligence (AI) for the development of MASSs can effectively resolve the increasing need for water-based navigation and safety at sea. An important focus of this Special Issue is the use of autonomous functions and increased intelligence in ships is to improve the safety and efficiency of their operations, and to decrease their environmental footprint.

Prof. Dr. Ho Namgung
Prof. Dr. Joo-Sung Kim
Prof. Dr. Kwang-il Kim
Dr. Dusica Marijan
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • path planning
  • autonomous navigation
  • collision avoidance
  • artificial intelligence
  • maritime transportation

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Published Papers (5 papers)

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Research

29 pages, 6142 KiB  
Article
Collision Avoidance Behavior Mining Model Considering Encounter Scenarios
by Shuzhe Chen, Chong Zhang, Lei Wu, Ziwei Wang, Wentao Wu, Shimeng Li and Haotian Gao
Appl. Sci. 2025, 15(5), 2616; https://doi.org/10.3390/app15052616 - 28 Feb 2025
Viewed by 443
Abstract
With the development of intelligent waterborne transportation, mining collision avoidance patterns based on spatiotemporal and motion data of ships are crucial for the autonomous navigation of intelligent ships, which requires accurate collision avoidance information under various encounter scenarios. Addressing the existing issues of [...] Read more.
With the development of intelligent waterborne transportation, mining collision avoidance patterns based on spatiotemporal and motion data of ships are crucial for the autonomous navigation of intelligent ships, which requires accurate collision avoidance information under various encounter scenarios. Addressing the existing issues of low precision and false detection in data mining algorithms, this paper proposes a collision avoidance behavior mining model considering encounter scenarios. The model is based on the Automatic Identification System (AIS) and the International Regulations for Preventing Collisions at Sea (COLREGs); it firstly identifies ship collision avoidance turning points by analyzing trajectory curvature with turning and recovering factors. Then, by combining AIS data and the specific navigational environment, it matches the ship encounter pairs and determines the encounter scenarios. Comparative experiments show that the model demonstrates superior accuracy in various scenarios compared to traditional algorithm. Finally, the model was applied to AIS data east of the Yangtze River Estuary, recognizing a total of 827 instances of ship collision avoidance behavior under different encounter scenarios. The case study shows that the model can precisely mine collision avoidance information, laying a solid foundation for future research on autonomous collision avoidance decision making for intelligent ships. Full article
(This article belongs to the Special Issue Advances in Intelligent Maritime Navigation and Ship Safety)
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24 pages, 13866 KiB  
Article
Development of a Multidimensional Analysis and Integrated Visualization Method for Maritime Traffic Behaviors Using DBSCAN-Based Dynamic Clustering
by Daehan Lee, Daun Jang and Sanglok Yoo
Appl. Sci. 2025, 15(2), 529; https://doi.org/10.3390/app15020529 - 8 Jan 2025
Viewed by 798
Abstract
Automatic Identification System (AIS) data offer essential insights into maritime traffic patterns; however, effective visualization tools for decision-making remain limited. This study presents an integrated visualization processing method to support ship operators by identifying maritime traffic behavior information, such as traffic density, direction, [...] Read more.
Automatic Identification System (AIS) data offer essential insights into maritime traffic patterns; however, effective visualization tools for decision-making remain limited. This study presents an integrated visualization processing method to support ship operators by identifying maritime traffic behavior information, such as traffic density, direction, and flow in specific sea navigational areas. We analyzed AIS dynamic data from a specific sea area, calculated ship density distributions across a grid lattice, and obtained visualizations of traffic-dense areas as heat maps. Using the density-based spatial clustering of applications with a noise algorithm, we detected traffic direction at each grid point, which was visualized in the form of directional arrows, and clustered ship trajectories to identify representative traffic flows. The visualizations were integrated and overlaid onto an S-57-based electronic nautical map for Mokpo’s entry and exit routes, revealing primary shipping lanes and critical inflection points within the target area. This integrated visualization method simultaneously displays traffic density, flow, and customary routes. It is adapted for the electronic nautical chart (S-101) under the next-generation hydrographic information standard (S-100), which can be used as a tool to support decision-making for ship operators. Full article
(This article belongs to the Special Issue Advances in Intelligent Maritime Navigation and Ship Safety)
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18 pages, 4017 KiB  
Article
Development of Automatic Berthing Support Program for Autonomous Ships
by Byung-Sun Kang, Chang-Hyun Jung, Keewon Kim, Hyunwoo Kim, Jin-Soo Kim and Dae-Hae Kim
Appl. Sci. 2025, 15(1), 228; https://doi.org/10.3390/app15010228 - 30 Dec 2024
Viewed by 635
Abstract
Research on autonomous ships has primarily focused on developing response technologies for navigation from pilot station to pilot station. This study developed an automatic berthing support program that calculates the necessary thruster output values for the bow and stern to achieve the desired [...] Read more.
Research on autonomous ships has primarily focused on developing response technologies for navigation from pilot station to pilot station. This study developed an automatic berthing support program that calculates the necessary thruster output values for the bow and stern to achieve the desired berthing speed under varying external force conditions, requiring only essential ship information as input. The program determines the thruster output by analyzing the forces and moments acting on the hull during the berthing process. An experimental setup equipped with the automatic berthing support program was installed on a ship. The outputs of the bow thruster (Thruster(F)) and stern tug (Tug(A)) were 300–544 hp on average, whereas the values calculated by the automatic berthing program (Program(F), Program(A)) were 105–131 hp. The calculation results of the automatic berthing support program of the ship were approximately 3–5 times greater than the horsepower values of the thruster and tug used during actual berthing, probably because the actual berthing speed was 0.25–1.13 m/s, which is more than five times higher than the set speed of 0.05–0.15 m/s. The results indicate that the automatic berthing support program is promising for future applications in automatic berthing systems for autonomous ships. Full article
(This article belongs to the Special Issue Advances in Intelligent Maritime Navigation and Ship Safety)
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27 pages, 11009 KiB  
Article
Development of HTC-DBSCAN: A Hierarchical Trajectory Clustering Algorithm with Automated Parameter Tuning
by Dae-Han Lee and Joo-Sung Kim
Appl. Sci. 2024, 14(23), 10995; https://doi.org/10.3390/app142310995 - 26 Nov 2024
Viewed by 878
Abstract
Existing route-clustering methods often fail to identify abnormal sections or similarities between routes, mainly when working with large or long datasets. While sub-route clustering can detect regional patterns, it struggles to accurately capture the overall route structure. The present study proposes a new [...] Read more.
Existing route-clustering methods often fail to identify abnormal sections or similarities between routes, mainly when working with large or long datasets. While sub-route clustering can detect regional patterns, it struggles to accurately capture the overall route structure. The present study proposes a new ship route-clustering method that enhances computational efficiency and noise recognition while addressing these limitations. We refined Automatic Identification System data via four data-cleaning processes and applied a statistical distance measurement to assess ship trajectory similarity. Dimensionality reduction was then used to facilitate clustering. The clustering of ship route similarities is non-parametric and can be applied to datasets not separated based on density to find clusters of various densities. Density-Based Spatial Clustering of Applications (DBSCA) applies to many research fields; using the DBSCA with Noise (DBSCAN) algorithm, we propose an improved DBSCAN algorithm that automatically determines the parameters Epsilon and MinPts. In this study, as a core ship route-clustering process, we propose a sub-route clustering process by setting the distance and density of data points to clear standards for re-analysis and completion. The proposed approach demonstrates markedly enhanced clustering performance, offering a more sophisticated and efficient basis for ship route decision-making. Full article
(This article belongs to the Special Issue Advances in Intelligent Maritime Navigation and Ship Safety)
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20 pages, 3693 KiB  
Article
Optimal Prediction of Individual Vessel Trajectories Based on Sparse Gaussian Processes
by Jinwan Park
Appl. Sci. 2024, 14(20), 9359; https://doi.org/10.3390/app14209359 - 14 Oct 2024
Cited by 1 | Viewed by 859
Abstract
Accurate forecasting of ship encounter positions is crucial for preventing collisions at sea. This paper presents a framework for predicting a ship’s trajectory using a sparse Gaussian process. The proposed method effectively addresses the limitations of existing full Gaussian processes, specifically the significant [...] Read more.
Accurate forecasting of ship encounter positions is crucial for preventing collisions at sea. This paper presents a framework for predicting a ship’s trajectory using a sparse Gaussian process. The proposed method effectively addresses the limitations of existing full Gaussian processes, specifically the significant storage requirements and time complexity associated with data training. The model is trained using Automatic Identification System (AIS) data on trajectories, with hyperparameters optimized through a genetic algorithm. Experimental analysis demonstrates that the proposed model reduces average time complexity by 61.3 s and improves average prediction error to 9.2 m compared to full Gaussian-process-based models. Full article
(This article belongs to the Special Issue Advances in Intelligent Maritime Navigation and Ship Safety)
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