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Emerging Topics in Intelligent Technology for Maritime Autonomous Systems
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Emerging Topics in Intelligent Technology for Maritime Autonomous Systems

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: closed (10 November 2025) | Viewed by 1444

Special Issue Editors

Prof. Dr. Jin Liu

E-Mail Website
Guest Editor
College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
Interests: deep learning; autonomous agents; autonomous ships; multimodal learning
Prof. Dr. Jialun Liu

E-Mail Website
Guest Editor
State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan, China
Interests: autonomous ship navigation and testing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhouhua Peng

E-Mail Website
Guest Editor
College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China
Interests: unmanned surface vehicle swarm control; embedded systems; power electronics technology; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The maritime industry is on the cusp of a technological revolution with the advent of autonomous ships. Autonomous systems, equipped with advanced sensors and artificial intelligence technologies, are set to transform maritime transportation, offering increased safety, efficiency, and environmental sustainability.

This Special Issue aims to explore innovative technologies and methodologies that contribute to the autonomy of systems, especially maritime autonomous systems, covering topics such as navigation systems, intelligent decision-making, multimodal agents, next-generation human–computer interactions, and autonomous agents. It provides a platform for researchers and industry experts to share innovative solutions and discuss challenges in the field.

The concept of autonomous ships has progressed from theoretical models to practical implementations, reflecting significant strides in maritime engineering and AI over recent decades.

This Special Issue focus on pioneering studies that address challenges in autonomous navigation, energy-efficient systems, autonomous agents, next-generation human–computer interactions, and decision-making algorithms in complex conditions.

We invite original research, comprehensive reviews, and case studies that offer novel insights or practical solutions to emerging topics in intelligent technology for autonomous systems.

Prof. Dr. Jin Liu
Prof. Dr. Jialun Liu
Prof. Dr. Zhouhua Peng
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

  • maritime autonomous systems
  • autonomous agents
  • autonomous ships
  • intelligent decision-making
  • human–computer interactions
  • energy-efficient 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

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28 pages, 8872 KB  
Article
Development and Application of an Intelligent Recognition System for Polar Environmental Targets Based on the YOLO Algorithm
by Jun Jian, Zhongying Wu, Kai Sun, Jiawei Guo and Ronglin Gao
J. Mar. Sci. Eng. 2025, 13(12), 2313; https://doi.org/10.3390/jmse13122313 - 5 Dec 2025
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Abstract
As global climate warming enhances the navigability of Arctic routes, their navigation value has become prominent, yet ships operating in ice-covered waters face severe threats from sea ice and icebergs. Existing manual observation and radar monitoring remain limited, highlighting an urgent need for [...] Read more.
As global climate warming enhances the navigability of Arctic routes, their navigation value has become prominent, yet ships operating in ice-covered waters face severe threats from sea ice and icebergs. Existing manual observation and radar monitoring remain limited, highlighting an urgent need for efficient target recognition technology. This study focuses on polar environmental target detection by constructing a polar dataset with 1342 JPG images covering four classes, including sea ice, icebergs, ice channels, and ships, obtained via web collection and video frame extraction. The “Grounding DINO pre-annotation + LabelImg manual fine-tuning” strategy is employed to improve annotation efficiency and accuracy, with data augmentation further enhancing dataset diversity. After comparing YOLOv5n, YOLOv8n, and YOLOv11n, YOLOv8n is selected as the baseline model and improved by introducing the CBAM/SE attention mechanism, SCConv/AKConv convolutions, and BiFPN network. Among these models, the improved YOLOv8n + SCConv achieves the best in polar target detection, with a mean average precision (mAP) of 0.844–1.4% higher than the original model. It effectively reduces missed detections of sea ice and icebergs, thereby enhancing adaptability to complex polar environments. The experimental results demonstrate that the improved model exhibits good robustness in images of varying resolutions, scenes with water surface reflections, and AI-generated images. In addition, a visual GUI with image/video detection functions was developed to support real-time monitoring and result visualization. This research provides essential technical support for safe navigation in ice-covered waters, polar resource exploration, and scientific activities. Full article
(This article belongs to the Special Issue Emerging Topics in Intelligent Technology for Maritime Autonomous Systems)
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Review

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15 pages, 1506 KB  
Review
Towards LLM Enhanced Decision: A Survey on Reinforcement Learning Based Ship Collision Avoidance
by Yizhou Wu, Jin Liu, Xingye Li, Junsheng Xiao, Tao Zhang, Haitong Xu and Lei Zhang
J. Mar. Sci. Eng. 2025, 13(12), 2275; https://doi.org/10.3390/jmse13122275 - 28 Nov 2025
Viewed by 402
Abstract
This comprehensive review examines the works of reinforcement learning (RL) in ship collision avoidance (SCA) from 2014 to the present, analyzing the methods designed for both single-agent and multi-agent collaborative paradigms. While prior research has demonstrated RL’s advantages in environmental adaptability, autonomous decision-making, [...] Read more.
This comprehensive review examines the works of reinforcement learning (RL) in ship collision avoidance (SCA) from 2014 to the present, analyzing the methods designed for both single-agent and multi-agent collaborative paradigms. While prior research has demonstrated RL’s advantages in environmental adaptability, autonomous decision-making, and online optimization over traditional control methods, this study systematically addresses the algorithmic improvements, implementation challenges, and functional roles of RL techniques in SCA, such as Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Multi-Agent Reinforcement Learning (MARL). It also highlights how these technologies address critical challenges in SCA, including dynamic obstacle avoidance, compliance with Convention on the International Regulations for Preventing Collisions at Sea (COLREGs), and coordination in dense traffic scenarios, while underscoring persistent limitations such as idealized assumptions, scalability issues, and robustness in uncertain environments. Contributions include a structured analysis of recent technological evolution, and a Large Language Model (LLM) based hierarchical architecture integrating perception, communication, decision-making, and execution layers for future SCA systems, which prioritizes the development of scalable, adaptive frameworks that ensure robust and compliant autonomous navigation in complex, real-world maritime environments. Full article
(This article belongs to the Special Issue Emerging Topics in Intelligent Technology for Maritime Autonomous Systems)
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