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Emerging Computational Methods in Intelligent Marine Vehicles
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Emerging Computational Methods in Intelligent Marine Vehicles

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 (5 March 2026) | Viewed by 2384

Special Issue Editors

Prof. Dr. Ryan Wen Liu

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Guest Editor
School of Navigation, Wuhan University of Technology, Wuhan 430063, China
Interests: intelligent transportation; autonomous driving; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Maohan Liang

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, National University of Singapore, Singapore 119260, Singapore
Interests: intelligent shipping; machine learning; data mining
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kezhong Liu

E-Mail Website
Guest Editor
School of Navigation, Wuhan University of Technology, Wuhan, China
Interests: intelligent shipping; wireless sensor networks; maritime safety; artificial intelligence

Special Issue Information

Dear Colleagues,

Intelligent marine vehicles, such as unmanned/autonomous surface vehicles (USV/ASV) and unmanned/autonomous underwater vehicles (UUV/AUV), have become increasingly popular recently because of their flexibility, versatility and high performance–price ratio in several applications, e.g., ocean exploration, oceanography, military security, and search and rescue missions. Performance in these applications depends highly upon the data sensed from various sensors, such as visible/infrared cameras, LiDAR, radar, global navigation satellite systems (GNSSs), and automatic identification systems (AISs) for USV/ASV, and visible cameras, sonar, inertial navigation systems (INSs), and Doppler velocity logs (DVLs) for UUV/AUV. However, the collected sensed data inevitably suffer from noise and missing data during signal encoding, transmission, and decoding. To guarantee high-quality sensed data, it is necessary to develop advanced computational methods to handle raw data under complex environments. Developing emerging technologies, e.g., data fusion, large language models (LLMs), embodied artificial intelligence (EAI), generative artificial intelligence (GAI), and artificial general intelligence (AGI), have been redefining and expanding traditional application scenarios of marine vehicles. To further improve the capabilities, efficiency, reliability, and safety of USV/ASV and UUV/AUV, emerging computational methods should be considered to handle the intractable problems arising from vehicle perception, decision making, planning, and control.

Potential topics for this Special Issue include, but are not limited to, the following:

  • Visual perception enhancement for vision-aided navigation systems;
  • Marine object detection, recognition and tracking under complex environments;
  • Multi-sensor data fusion for maritime situational awareness;
  • Simultaneous localization and mapping (SLAM) for intelligent marine vehicles;
  • Behavior and trajectory prediction for intelligent marine vehicles;
  • Semantic segmentation for autonomous navigation;
  • Unsupervised learning for (multi-task) autonomous navigation;
  • Large language models (LLMs) for end-to-end autonomous navigation;
  • Embodied intelligence-based perception, decision making, and control for intelligent marine vehicles;
  • Generative artificial intelligence (GAI) for testing autonomous navigation systems;
  • Artificial general intelligence (AGI) for intelligent marine vehicles;
  • Cooperative navigation and control for surface–underwater vehicles.

Prof. Dr. Ryan Wen Liu
Dr. Maohan Liang
Prof. Dr. Kezhong Liu
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 vehicles
  • intelligent transportation
  • autonomous navigation
  • maritime traffic safety
  • artificial intelligence
  • computer vision
  • multi-sensor data fusion

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Published Papers (3 papers)

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Research

28 pages, 5258 KB  
Article
Dual-View Entropy-Driven AIS–Sonar Fusion for Surface and Underwater Target Discrimination
by Xiaoshuang Zhang, Jiayi Che, Xiaodan Xiong, Yucheng Zhang, Xinbo He, Mengsha Deng and Dezhi Wang
J. Mar. Sci. Eng. 2026, 14(7), 675; https://doi.org/10.3390/jmse14070675 - 4 Apr 2026
Viewed by 424
Abstract
Distinguishing surfaces from underwater targets in complex marine environments is challenging when relying solely on physical sonar features. To address the high uncertainty inherent in single-modal features and the conflicts arising from heterogeneous data, we propose a Dual-View Entropy-Driven Negation Dempster–Shafer (DVE-NDS) fusion [...] Read more.
Distinguishing surfaces from underwater targets in complex marine environments is challenging when relying solely on physical sonar features. To address the high uncertainty inherent in single-modal features and the conflicts arising from heterogeneous data, we propose a Dual-View Entropy-Driven Negation Dempster–Shafer (DVE-NDS) fusion method that integrates AIS kinematic priors with passive sonar signals. First, a heterogeneous recognition framework is constructed. LOFAR and DEMON features are extracted via convolutional neural networks (CNNs), while a Negation Basic Probability Assignment (Negation BPA) strategy is introduced to transform AIS spatiotemporal mismatches into effective "negation support" for non-cooperative underwater targets. Instead of relying on a single conflict coefficient, the proposed method jointly considers evidence self-information and inter-source consistency. Evidence quality is quantified using improved Deng entropy and negation belief entropy, while mutual trust is evaluated via the Jousselme distance. Heterogeneous evidence is weighted and corrected by generated coupling weights, effectively suppressing low-quality evidence and sharpening decision boundaries. Simulation results confirm that DVE-NDS improves macro-F1 over classical fusion, indicating the framework’s potential for handling conflicting evidence, though the current validation remains simulation-based and should be regarded as a methodological proof-of-concept. Full article
(This article belongs to the Special Issue Emerging Computational Methods in Intelligent Marine Vehicles)
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26 pages, 12124 KB  
Article
MF-GCN: Multimodal Information Fusion Using Incremental Graph Convolutional Network for Ship Behavior Anomaly Detection
by Ruixin Ma, Jinhao Zhang, Weizhi Nie, Naiming Ge, Hao Wen and Aoxiang Liu
J. Mar. Sci. Eng. 2026, 14(1), 87; https://doi.org/10.3390/jmse14010087 - 1 Jan 2026
Viewed by 686
Abstract
Ship behavior anomaly detection is critical for intelligent perception and early warning in complex inland waterways, where single-source sensing (e.g., AIS-only or vision-only) is often fragile under occlusion, illumination variation, and signal noise. This study proposes MF-GCN, a multimodal (heterogeneous) information fusion framework [...] Read more.
Ship behavior anomaly detection is critical for intelligent perception and early warning in complex inland waterways, where single-source sensing (e.g., AIS-only or vision-only) is often fragile under occlusion, illumination variation, and signal noise. This study proposes MF-GCN, a multimodal (heterogeneous) information fusion framework based on an Incremental Graph Convolutional Network (IGCN) to detect and warn anomalous ship behaviors by jointly modeling AIS, video imagery, LiDAR point clouds, and water level signals. We first extract modality-specific features and enforce temporal–spatial consistency via timestamp and geo-referencing alignment, then construct an evolving graph in which nodes represent multimodal features and edges encode temporal dependency and semantic similarity. MF-GCN integrates a Semantic Clustering-based GCN (S-GCN) to inject historical semantic context and an Attentive Fusion-based GCN (A-GCN) to learn dynamic cross-modal correlations using multi-head attention. Experiments on our constructed real-world datasets demonstrate that MF-GCN achieves accuracies of 93.8%, 93.8%, and 93.3% with F1-scores of 93.6%, 93.6%, and 93.3% for ship deviation warning, bridge-crossing warning, and inter-ship collision warning, respectively, consistently outperforming representative baselines. These results verify the effectiveness of the proposed method for robust multimodal anomaly detection and early warning in inland-waterway scenarios. Full article
(This article belongs to the Special Issue Emerging Computational Methods in Intelligent Marine Vehicles)
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20 pages, 39007 KB  
Article
Hybrid Regularized Variational Minimization Method to Promote Visual Perception for Intelligent Surface Vehicles Under Hazy Weather Condition
by Peizheng Li, Dayong Qiao, Caofei Luo, Desong Wan and Guilian Li
J. Mar. Sci. Eng. 2025, 13(10), 1991; https://doi.org/10.3390/jmse13101991 - 17 Oct 2025
Cited by 2 | Viewed by 562
Abstract
Intelligent surface vehicles, including unmanned surface vehicles (USVs) and autonomous surface vehicles (ASVs), have gained significant attention from both academic and industrial communities. However, shipboard maritime images captured under hazy weather conditions inevitably suffer from a blurred, distorted appearance. Low-quality maritime images can [...] Read more.
Intelligent surface vehicles, including unmanned surface vehicles (USVs) and autonomous surface vehicles (ASVs), have gained significant attention from both academic and industrial communities. However, shipboard maritime images captured under hazy weather conditions inevitably suffer from a blurred, distorted appearance. Low-quality maritime images can lead to negative effects on high-level computer vision tasks, such as object detection, recognition and tracking, etc. To avoid the negative influence of low-quality maritime images, it is necessary to develop a visual perception enhancement method for intelligent surface vehicles. To generate satisfactory haze-free maritime images, we propose development of a novel transmission map estimation and refinement framework. In this work, the coarse transmission map is obtained by the weighted fusion of transmission maps generated by dark channel prior (DCP)- and luminance-based estimation methods. To refine the transmission map, we take the segmented smooth feature of the transmission map into account. A joint variational framework with total generalized variation (TGV) and relative total variation (RTV) regularizers is accordingly proposed. The joint variational framework is effectively solved by an alternating-direction numerical algorithm, which decomposes the original nonconvex nonsmooth optimization problem into several subproblems. Each subproblem could be efficiently and easily handled using the existing optimization algorithm. Finally, comprehensive experiments are conducted on synthetic and realistic maritime images. The imaging results have illustrated that our method can outperform or achieve comparable results with other competing dehazing methods. The promoted visual perception is beneficial to improve navigation safety for intelligent surface vehicles under hazy weather conditions. Full article
(This article belongs to the Special Issue Emerging Computational Methods in Intelligent Marine Vehicles)
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