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Maritime Security and Smart Navigation: Recent Developments and Prospects
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Maritime Security and Smart Navigation: Recent Developments and Prospects

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 August 2026 | Viewed by 1654

Special Issue Editors

Dr. Qing Yu

E-Mail Website
Guest Editor
Maritime College, Jimei University, Xiamen, China
Interests: marine traffic safety; maritime risk assessment; intelligent decision-making for autonomous ships; ship traffic analysis and characteristic mining
Special Issues, Collections and Topics in MDPI journals
Dr. Xinjian Wang

E-Mail Website
Guest Editor
Navigation College, Dalian Maritime University, Dalian, China
Interests: emergency evacuation; maritime accidents; seafarer fatigue; ship fuel oil consumption prediction
Special Issues, Collections and Topics in MDPI journals
Dr. Zhisen Yang

E-Mail Website
Guest Editor
College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen, China
Interests: maritime safety management; maritime risk assessment; port state control inspection; green shipping; port resilience

Special Issue Information

Dear Colleagues,

We are pleased to launch this Special Issue entitled “Maritime Security and Smart Navigation: Recent Developments and Prospects.” Maritime transportation serves as the backbone of the global economy, yet it faces unprecedented challenges ranging from increasing traffic density to the urgent need for decarbonization. As the industry accelerates its transition toward Maritime Autonomous Surface Ships (MASS) and smart shipping, the focus of research is shifting from isolated automation systems to holistic, resilient, and human-centric solutions.

This Special Issue aims to gather original contributions that address the latest developments in intelligent navigation technologies and safety management. We particularly welcome studies leveraging emerging technologies such as digital twins, deep learning, and big data analytics to solve complex problems in mixed-traffic scenarios (manned and unmanned ships). Topics of interest extend to cybersecurity in smart navigation, energy-efficient route planning to meet green shipping goals, and the evolution of human–machine collaboration. By integrating theoretical breakthroughs with practical applications, this Special Issue seeks to define the next generation of safe, efficient, and intelligent maritime systems.

Dr. Qing Yu
Dr. Xinjian Wang
Dr. Zhisen Yang
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 250 words) can be sent to the Editorial Office for assessment.

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 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 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

  • intelligent marine navigation
  • Maritime Autonomous Surface Ships (MASS)
  • maritime traffic safety and risk assessment
  • collision avoidance and path planning
  • maritime big data and deep learning
  • digital twins in shipping
  • human–machine collaboration
  • maritime cybersecurity
  • green shipping and energy efficiency
  • resilience of maritime transportation systems

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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

27 pages, 20749 KB  
Article
A Multi-Factor Constrained Autonomous Decision-Making Method for Ship Maneuvering in Complex Shallow Water Areas
by Ke Zhang, Jie Wen, Xiongfei Geng, Chunxu Li, Xingya Zhao, Kexin Xu and Yucheng Zhou
J. Mar. Sci. Eng. 2026, 14(7), 603; https://doi.org/10.3390/jmse14070603 - 25 Mar 2026
Viewed by 507
Abstract
The navigation of ships in complex shallow water areas is constrained by various factors such as water depth, channel boundaries, and environmental interference. Therefore, it is crucial to improve the adaptability and effectiveness of collision avoidance decisions for ships in complex shallow water [...] Read more.
The navigation of ships in complex shallow water areas is constrained by various factors such as water depth, channel boundaries, and environmental interference. Therefore, it is crucial to improve the adaptability and effectiveness of collision avoidance decisions for ships in complex shallow water scenarios. To address these issues, this paper proposes a multi-factor constrained autonomous decision-making method for complex shallow water vessel maneuvering. Firstly, a digital transportation environment was constructed by combining dynamic and static information, such as water depth, tides, channel boundaries, changes in maneuvering characteristics, and navigation rules, and a navigable water area model that was suitable for shallow water was proposed. Then, considering the constraints of ship maneuverability and the navigation environment, a shallow water ship motion model affected by wind flow was developed. A complex shallow water adaptive maneuvering coupled decision-making method was constructed, considering the influence of ship navigation rules and channel constraints. This method utilizes the Kalman filtering algorithm to correct residuals and predict the maneuvering of the target vessel. Integrated improved heading control and guidance algorithms achieved automatic heading control and future position prediction. Through testing and verification in the complex waters of the Yangtze River estuary, the results show that the autonomous collision avoidance decision-making method proposed in this paper can effectively make collision avoidance decisions in complex multi-ship shallow water areas. This study can provide innovative and practical solutions for the technological development of autonomous ship collision avoidance decision-making. Full article
(This article belongs to the Special Issue Maritime Security and Smart Navigation: Recent Developments and Prospects)
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34 pages, 16050 KB  
Article
A Novel Action-Aware Multi-Agent Soft Actor–Critic Algorithm for Tight Formation Control in USV Swarm
by Yongfeng Suo, Kuoyuan Zhu, Weijun Wang, Shenhua Yang and Lei Cui
J. Mar. Sci. Eng. 2026, 14(5), 450; https://doi.org/10.3390/jmse14050450 - 27 Feb 2026
Viewed by 716
Abstract
Tight-formation control is a key technology for unmanned surface vehicle (USV) swarms in harbor navigation, cooperative berthing, and operations in hazardous environments, yet achieving reliable obstacle avoidance while maintaining formation stability remains highly challenging. Although multi-agent reinforcement learning has shown strong potential in [...] Read more.
Tight-formation control is a key technology for unmanned surface vehicle (USV) swarms in harbor navigation, cooperative berthing, and operations in hazardous environments, yet achieving reliable obstacle avoidance while maintaining formation stability remains highly challenging. Although multi-agent reinforcement learning has shown strong potential in cooperative systems, parallel policy structures in many existing methods still struggle to achieve synchronized coordination in tight formations, leading to behavioral inconsistencies and unstable formation keeping. To address these challenges, an action-aware multi-agent soft actor–critic (AAMASAC) algorithm is proposed that introduces a hierarchical, action-aware decision mechanism. Within each time step, upper-layer actions are propagated as prior signals to lower-layer policies, establishing an ordered, intent-aligned decision flow that mitigates temporal inconsistency and enhances coordination efficiency. The architecture explicitly encodes inter-layer dependencies via a decision priority hierarchy and real-time behavioral information channels, enabling more accurate credit assignment and more stable value estimation and policy optimization. Across three representative validation scenarios, the AAMASAC algorithm significantly outperforms baseline methods in average reward, path-tracking accuracy, formation stability, and obstacle-avoidance performance. These results indicate that introducing a hierarchical model and action awareness effectively improves control accuracy and coordination in a USV swarm. Full article
(This article belongs to the Special Issue Maritime Security and Smart Navigation: Recent Developments and Prospects)
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