Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships—2nd Edition

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: 10 May 2024 | Viewed by 5220

Special Issue Editors

National Engineering Research Center Water Transport Safety, Wuhan University of Technology, Wuhan 430063, China
Interests: nautical traffic safety and simulation; artificial intelligence and its applications in maritime; maritime autonomous surface ships
Special Issues, Collections and Topics in MDPI journals
German Aerospace Center e.V., University of Oldenburg, 6121 Oldenburg, Germany
Interests: simulation; maritime systems; transportation; navigation
Special Issues, Collections and Topics in MDPI journals
Research Group on Safe and Efficient Marine Systems, Marine Technology, Department of Mechanical Engineering, Aalto University, 02150 Espoo, Finland
Interests: safety and systems engineering; risk analysis; maritime safety; winter navigation; autonomous ships
Special Issues, Collections and Topics in MDPI journals
National Engineering Research Center Water Transport Safety, Wuhan University of Technology, Wuhan 430063, China
Interests: human–machine cooperation; autonomous ships; ship collision avoidance; maritime traffic management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Understanding, modeling, and predicting ship behaviors are fundamental and essential issues for planning, controlling, and operating different levels of Maritime Autonomous Surface Ships (MASS). The maritime traffic data (e.g., radar data, AIS data, CCTV data, etc.) provide designers, officers on watch, and traffic operators with extensive information about the states of ships at present and in history, which are the treasure for behavior analysis. Additionally, navigation rules and regulations (i.e., knowledge) offer valuable prior knowledge about ship manners at sea. Combining multi-source heterogeneous big data and artificial intelligence techniques inspire innovative and important means for the development of MASS, leading to smart, safe, green, and efficient shipping.

With the successful Special Issue ”Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships” in 2021, this special issue is continuing to provide an excellent medium to present the latest developments on methods and tools suitable for relevant issues, including but not limited to:

  • Data-driven behavior modeling and simulation
  • Knowledge-driven behavior modeling and reasoning
  • Multi-source heterogeneous traffic data fusion
  • Semantic analysis of ship behaviors
  • Quantifying COLREGs and seamanship for machine
  • Inference engine and ontology reasoning for rule-compliant MASS
  • Intention Inference based on behavior observations
  • Maritime traffic situational awareness
  • Multi-agent simulation
  • Risk analysis and management of MASS
  • Safety and Cyber Security of MASS

Prof. Dr. Yuanqiao Wen
Prof. Dr. Axel Hahn
Dr. Osiris Valdez Banda
Prof. Dr. Yamin Huang
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

  • maritime autonomous surface ships (MASS)
  • data-driven modeling
  • knowledge-driven modeling
  • behavior modeling
  • knowledge graph
  • navigation simulation
  • multi-source heterogeneous data analysis
  • nautical safety
  • quantifying rules and regulations

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

26 pages, 10537 KiB  
Article
Fishing Behavior Detection and Analysis of Squid Fishing Vessel Based on Multiscale Trajectory Characteristics
by Fan Zhang, Baoxin Yuan, Liang Huang, Yuanqiao Wen, Xue Yang, Rongxin Song and Pieter van Gelder
J. Mar. Sci. Eng. 2023, 11(6), 1245; https://doi.org/10.3390/jmse11061245 - 18 Jun 2023
Viewed by 1242
Abstract
Accurate fishing activity detection from the trajectories of fishing vessels can not only achieve high-precision fishery management but also ensure the reasonable and sustainable development of marine fishery resources. This paper proposes a new method to detect fishing vessels’ fishing activities based on [...] Read more.
Accurate fishing activity detection from the trajectories of fishing vessels can not only achieve high-precision fishery management but also ensure the reasonable and sustainable development of marine fishery resources. This paper proposes a new method to detect fishing vessels’ fishing activities based on the defined local dynamic parameters and global statistical characteristics of vessel trajectories. On a local scale, the stop points and points of interest (POIs) in the vessel trajectory are extracted. Voyage extraction can then be conducted on this basis. After that, multiple characteristics based on motion and morphology on a global scale are defined to construct a logistic regression model for fishing behavior detection. To verify the effectiveness and feasibility of the method, vessel trajectory data, and fishing log data collected from Chinese ocean squid fishing vessels in Argentine waters in 2020 are integrated for fishing operation detection. Multiple evaluation metrics show that the proposed method can provide robust and accurate recognition results. Moreover, further analysis of the temporal and spatial distribution and seasonal changes in squid fishing activities in Argentine waters has been performed. A more refined assessment of the fishing activities of individual fishing vessels can also be provided quantitatively. All the results above can benefit the regulation of fishing activities. Full article
Show Figures

Figure 1

12 pages, 3736 KiB  
Article
An Offshore Self-Stabilized System Based on Motion Prediction and Compensation Control
by Yanhua Liu, Haiwen Yuan, Zeyu Xiao and Changshi Xiao
J. Mar. Sci. Eng. 2023, 11(4), 745; https://doi.org/10.3390/jmse11040745 - 29 Mar 2023
Cited by 1 | Viewed by 964
Abstract
The swaying motion of ships can always be generated due to the influence of complex sea conditions. A novel offshore Self-Stabilized system based on motion prediction and compensation control was studied. Firstly, an autoregressive model of ship motion exposed to various sea conditions [...] Read more.
The swaying motion of ships can always be generated due to the influence of complex sea conditions. A novel offshore Self-Stabilized system based on motion prediction and compensation control was studied. Firstly, an autoregressive model of ship motion exposed to various sea conditions was established, and the parameters of the model were initialized and updated by offline and online learning historical data. Using the autoregressive model with the acquired parameters, the prediction of the ship’s motion was achieved. Then, a Self-Stabilized system platform composed of six electric cylinders in parallel was designed, and the corresponding inverse kinematics were established. The corresponding controller using the result of motion prediction as the input was also proposed to counteract the extra motion variables of the ship. Various experiments, by simulating different sea conditions, can be carried out. The results show that the average error of the motion prediction was less than 1%. The maximum error of the self-stabilizing control was 1.6°, and the average error was stable within 0.7°. The Self-Stabilized system was able to effectively compensate for the rocking motion of ships affected by waves, which was of great significance for improving the maritime safety guarantee and the intelligent level of shipborne equipment. Full article
Show Figures

Figure 1

21 pages, 3703 KiB  
Article
Spatiotemporal Companion Pattern (STCP) Mining of Ships Based on Trajectory Features
by Chunhui Zhou, Guangya Liu, Liang Huang and Yuanqiao Wen
J. Mar. Sci. Eng. 2023, 11(3), 528; https://doi.org/10.3390/jmse11030528 - 28 Feb 2023
Cited by 1 | Viewed by 1326
Abstract
Spatiotemporal companion pattern (STCP) mining is one of the means to identify and detect group behavioral activities. To detect the spatiotemporal traveling pattern of ships from massive spatiotemporal trajectory data and to understand the movement law of group ships, this article proposes a [...] Read more.
Spatiotemporal companion pattern (STCP) mining is one of the means to identify and detect group behavioral activities. To detect the spatiotemporal traveling pattern of ships from massive spatiotemporal trajectory data and to understand the movement law of group ships, this article proposes a feature-driven approach for STCP mining that consists of (1) generating the grid index via the rasterizing of geospace and characterizing trajectory points via the spatiotemporal trajectory grid sequences (STTGSs) of ships; (2) designing filtering rules with the constraints of range, time and distance to construct a candidate set for ship STCP mining; and (3) measuring the STTGS similarity of the associated ships and setting the confidence threshold to realize spatiotemporal companion mining. The effectiveness of the proposed method is practically validated on a real trajectory dataset which is collected from the Taiwan Strait waters. The experimental results are as follows: 825 pairs of associated ships and 225 pairs of accompanying ships are mined when the grid size is 0.05° and the confidence is 0.5. Larger grid sizes can increase the inclusiveness of the associated ship trajectory similarity measurement, which can result in an increase in confidence of pattern. A large number of pseudo-accompaniment ships are extracted to the result set, resulting in a more dispersed distribution of pattern confidence. By verifying the proposed method, accompanying behavioral activities such as ship cooperative operation, companion navigation method, and so on, can be detected. These results can provide a reference for the research of ship group behavior identification and have an important application value for water transportation management. Full article
Show Figures

Figure 1

24 pages, 28800 KiB  
Article
Research on Black-Box Modeling Prediction of USV Maneuvering Based on SSA-WLS-SVM
by Lifei Song, Le Hao, Hao Tao, Chuanyi Xu, Rong Guo, Yi Li and Jianxi Yao
J. Mar. Sci. Eng. 2023, 11(2), 324; https://doi.org/10.3390/jmse11020324 - 02 Feb 2023
Cited by 3 | Viewed by 1065
Abstract
Unmanned surface vessels (USVs) are required to perform motion prediction during a task. This is essential for USVs, especially when conducting motion control, and this work has been proven to be complicated. In this paper, an off-line black box modeling method for USV [...] Read more.
Unmanned surface vessels (USVs) are required to perform motion prediction during a task. This is essential for USVs, especially when conducting motion control, and this work has been proven to be complicated. In this paper, an off-line black box modeling method for USV maneuvering, the Sparrow search algorithm-based weighted-least-squares support vector machine (SSA-WLS-SVM) was proposed to recognize the motion model of a USV. Firstly, the construction of the USV test platform and the processing process of the experimental data were introduced, the correctness of the MMG model was verified using a comparison of the test data and the simulation results, and then the MMG model was used to produce sample data later. To improve the stability and robustness of LS-SVM, weighted least squares and SSA were introduced to perform the optimization of the parameters of the algorithm and its kernel function. Then, the random maneuvering dataset was obtained using simulation on the MMG model, which was then preprocessed and used for training the black-box model. To verify the generalization ability of the identified model, the black-box model was used for comparison analysis between motion prediction with the proposed model and maneuvering test on the USV platform in a scenario different from the training data. Full article
Show Figures

Figure 1

Back to TopTop