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Applications of Sensors and Artificial Intelligence Techniques in Ships
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Applications of Sensors and Artificial Intelligence Techniques in Ships

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 August 2025 | Viewed by 1199

Special Issue Editors

Dr. Jiansen Zhao

E-Mail Website
Guest Editor
Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China
Interests: intelligent ship; intelligent communication; ship trajectory prediction; perception
Dr. Xinjian Wang

E-Mail Website
Guest Editor
Navigation College, Dalian Maritime University, Dalian 116026, China
Interests: ship navigation and maritime technology; marine traffic safety and environment
Special Issues, Collections and Topics in MDPI journals
Dr. Rong Zhen

E-Mail Website
Guest Editor
Navigation College, Jimei University, Xiamen 361021, China
Interests: maritime data mining; ship trajectory analysis; intelligent ship; ship collision avoidance; ship traffic control

Special Issue Information

Dear Colleagues,

The application of new sensing and artificial intelligence (AI) technology in the field of intelligent ships is mainly reflected in improvements to environment enhancement and fusion, remote communication, intelligent decision-making, control, and route and energy efficiency optimization for ships.

This Special Issue will focus on cutting-edge solutions to achieve the safe, green, and intelligent navigation of intelligent ships in the fields of environment perception, situation cognition, maneuver decision-making, and control.

Key areas of focus include advanced ship detection and tracking, maritime data mining, ship trajectory fusion, navigation awareness enhancement, intelligent ship collision avoidance, intelligent aids in ship navigation, maritime traffic organization, navigational risk assessment and control, maritime traffic supervision, and MASS.

Dr. Jiansen Zhao
Dr. Xinjian Wang
Dr. Rong Zhen
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 detection and tracking
  • maritime data mining
  • ship trajectory fusion
  • navigation awareness enhancement
  • intelligent ship collision avoidance
  • intelligent aids in ship navigation
  • maritime traffic organization
  • navigational risk assessment and control
  • maritime traffic supervision
  • MASS
  • ship communication
  • perception

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

29 pages, 4065 KiB  
Article
Towards Hazard Analysis Result Verification for Autonomous Ships: A Formal Verification Method Based on Timed Automata
by Xiang-Yu Zhou, Shiqi Jin, Yang Mei, Xu Sun, Xue Yang, Shengzheng Nie and Wenjun Zhang
J. Mar. Sci. Eng. 2025, 13(6), 1058; https://doi.org/10.3390/jmse13061058 - 27 May 2025
Viewed by 374
Abstract
Enhancing the safety standards of autonomous ships is a shared objective of all stakeholders involved in the maritime industry. Since the existing hazard analysis work for autonomous ships often exhibits a degree of subjectivity, in the absence of data support, the verification of [...] Read more.
Enhancing the safety standards of autonomous ships is a shared objective of all stakeholders involved in the maritime industry. Since the existing hazard analysis work for autonomous ships often exhibits a degree of subjectivity, in the absence of data support, the verification of hazard analysis results has become increasingly challenging. In this study, a formal verification method in a risk-based assessment framework is proposed to verify the hazard analysis results for autonomous ships. To satisfy the characteristics of high time sensitivity, time automata are adopted as a formal language while model checking based on the formal verification tool UPPAAL is used to complete the automatic verification of the liveness of system modeling and correctness of hazard analysis results derived from extended System-Theoretic Process Analysis (STPA) by traversing the finite state space of the system. The effectiveness of the proposed method is demonstrated through a case study involving a remotely controlled ship. The results indicate that the timed automata network model for remotely controlled ships, based on the control structure, has no deadlocks and operates correctly, which demonstrates its practicability and effectiveness. By leveraging the verification of risk analysis results based on model checking, the framework enhances the precision and traceability of these inputs into RBAT. The results disclose the significance of the collaborative work between safety and system engineering in the development of autonomous systems under the definition of human–computer interaction mode transformation. These findings also hold reference value for other intelligent systems with potential hazards. Full article
(This article belongs to the Special Issue Applications of Sensors and Artificial Intelligence Techniques in Ships)
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17 pages, 1857 KiB  
Article
Modeling Navigator Awareness of COLREGs Interpretation Using Probabilistic Curve Fitting
by Deuk-Jin Park, Hong-Tae Kim, Sang-A Park, Tae-Yeon Kim and Jeong-Bin Yim
J. Mar. Sci. Eng. 2025, 13(5), 987; https://doi.org/10.3390/jmse13050987 - 20 May 2025
Viewed by 329
Abstract
Despite the existence of standardized collision regulations such as the International Regulations for Preventing Collisions at Sea (COLREGs), ship collisions continue to occur, indicating persistent gaps in how navigators interpret and apply these rules. The COLREGs are globally adopted rules that govern vessel [...] Read more.
Despite the existence of standardized collision regulations such as the International Regulations for Preventing Collisions at Sea (COLREGs), ship collisions continue to occur, indicating persistent gaps in how navigators interpret and apply these rules. The COLREGs are globally adopted rules that govern vessel conduct to avoid collisions. Borderline encounter situations—such as those between head-on and crossing, or overtaking and crossing—pose particular challenges, often resulting in inconsistent or ambiguous interpretations. This study models navigator awareness as a probabilistic function of encounter angle, aiming to identify interpretive transition zones and cognitive uncertainty in rule application. A structured survey was conducted with 101 licensed navigators, each evaluating simulated ship encounter scenarios with varying relative bearings. Responses were collected using a Likert scale and analyzed in angular sectors known for interpretational ambiguity: 006–012° for head on to crossing (HC) and 100–160° for overtaking to crossing (OC). Gaussian curve fitting was applied to the response distributions, with the awareness center (μ) and standard deviation (σ) serving as indicators of consensus and ambiguity. The results reveal sharp shifts in awareness near 008° and 160°, suggesting cognitively unstable zones. Risk-averse interpretation patterns were also observed, where navigators tended to classify borderline situations more conservatively under uncertainty. These findings suggest that navigator awareness is not deterministic but probabilistically structured and context sensitive. The proposed awareness modeling framework helps bridge the gap between regulatory prescriptions and real world navigator behavior, offering practical implications for MASS algorithm design and COLREGs refinement. Full article
(This article belongs to the Special Issue Applications of Sensors and Artificial Intelligence Techniques in Ships)
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