Nash Bargaining Strategy in Autonomous Decision Making for Multi‐Ship Collision Avoidance Based on Route Exchange DOI Creative Commons
Yang Wang,

Qiangsheng Ye,

Hoong Chuin Lau

и другие.

IET Intelligent Transport Systems, Год журнала: 2025, Номер 19(1)

Опубликована: Янв. 1, 2025

ABSTRACT A novel scheme is proposed for the distributed multi‐ship collision avoidance (CA) problem with consideration of autonomous, dynamic nature real circumstance. All ships in envisioned scenarios can share their decisions or intentions through route exchange, allowing them to make subsequent based on planning each iteration. By leveraging CA involves iterations negotiation, and regarded as a staged cooperative game under conditions complete information. The concept closest spatio‐temporal distance (CSTD) introduced more accurately assess risk between ships. coordinated mechanism established when identified, which further incorporates considerations including stand‐on/give‐way relationships, negotiation rounds, re‐planning calculation, well cost factor evaluation. Nash bargaining solution (NBS) elaborated achieve Pareto‐optimal routes scenarios. In model, while individual interest ship are maximized, economic fairness global optimization overall system also maintained. Simulation results indicate that NBS shows good flexibility adaptability, all comply solution, bring out normal solutions within limited number iterations.

Язык: Английский

Sustainable Maritime Transport: A Review of Intelligent Shipping Technology and Green Port Construction Applications DOI Creative Commons
Guangnian Xiao,

Yiqun Wang,

Ruijing Wu

и другие.

Journal of Marine Science and Engineering, Год журнала: 2024, Номер 12(10), С. 1728 - 1728

Опубликована: Окт. 1, 2024

With the global economy’s relentless growth and heightened environmental consciousness, sustainable maritime transport emerges as a pivotal development trajectory for shipping sector. This study systematically analyzes 478 publications searched in Web of Science Core Collection, from 2000 to 2023, utilizing bibliometric methods investigate application areas within industry. begins with an analysis annual publication trends, which reveals substantial expansion research endeavors this discipline over recent years. Subsequently, comprehensive statistical evaluation scholarly journals collaborative network assessment are conducted pinpoint foremost productive journals, nations, organizations, individual researchers. Furthermore, keyword co-occurrence methodology is applied delineate core themes emerging focal points domain, thereby outlining potential directions future research. In addition, drawing on analysis, advancements intelligent technologies green port construction applications discussed. Finally, review discusses existing challenges opportunities theoretical practical perspective. The shows that, terms technology, data security multi-source focus that people need pay attention future; prediction different climates ship types also area ports, Cold Ironing (CI) one key strategy, how drive stakeholders build ports efficiently economically developmental direction. serves enhance researchers’ comprehension current landscape progression technologies, fostering continued advancement exploration vital domain.

Язык: Английский

Процитировано

45
The Application of Artificial Intelligence Technology in Shipping: A Bibliometric Review DOI Creative Commons
Guangnian Xiao,

Daoqi Yang,

Lang Xu

и другие.

Journal of Marine Science and Engineering, Год журнала: 2024, Номер 12(4), С. 624 - 624

Опубликована: Апрель 7, 2024

Artificial intelligence (AI) technologies are increasingly being applied to the shipping industry advance its development. In this study, 476 articles published in Science Citation Index Expanded (SCI-EXPANDED) and Social Sciences (SSCI) of Web Core Collection from 2001 2022 were collected, bibliometric methods conduct a systematic literature field AI technology applications industry. The review commences with an annual publication trend analysis, which shows that research has been growing rapidly recent years. This is followed by statistical analysis journals collaborative network identify most productive journals, countries, institutions, authors. keyword “co-occurrence analysis” then utilized major clusters, as well hot directions field, providing for future field. Finally, based on results co-occurrence content papers years, gaps AIS data applications, ship trajectory, anomaly detection, possible directions, discussed. findings indicate direction mainly reflected behavior repair. Ship trajectory deep learning-based method discussion classification. Anomaly detection application learning improving efficiency detection. These insights offer guidance researchers’ investigations area. addition, we discuss implications both theoretical practical perspectives. Overall, can help researchers understand status development shipping, correctly grasp methodology, promote further

Язык: Английский

Процитировано

38
Optimizing anti-collision strategy for MASS: A safe reinforcement learning approach to improve maritime traffic safety DOI
Chengbo Wang, Xinyu Zhang, Hongbo Gao

и другие.

Ocean & Coastal Management, Год журнала: 2024, Номер 253, С. 107161 - 107161

Опубликована: Апрель 29, 2024

Язык: Английский

Процитировано

22
Deep bi-directional information-empowered ship trajectory prediction for maritime autonomous surface ships DOI Creative Commons
Huanhuan Li, Wenbin Xing,

Hang Jiao

и другие.

Transportation Research Part E Logistics and Transportation Review, Год журнала: 2023, Номер 181, С. 103367 - 103367

Опубликована: Дек. 6, 2023

It is critical to have accurate ship trajectory prediction for collision avoidance and intelligent traffic management of manned ships emerging Maritime Autonomous Surface Ships (MASS). Deep learning methods based on AIS data emerged as a contemporary maritime transportation research focus. However, concerns about its accuracy computational efficiency widely exist across both academic industrial sectors, necessitating the discovery new solutions. This paper aims develop approach called Bi-Directional Information-Empowered (DBDIE) by utilising integrated multiple networks an attention mechanism address above issues. The DBDIE model extracts valuable features fusing Bi-directional Long Short-Term Memory (Bi-LSTM) Gated Recurrent Unit (Bi-GRU) neural networks. Additionally, weights two bi-directional units are optimised using mechanism, final results obtained through weight self-adjustment mechanism. effectiveness proposed verified comprehensive comparisons with state-of-the-art deep methods, including Neural Network (RNN), (LSTM), (GRU), Bi-LSTM, Bi-GRU, Sequence (Seq2Seq), Transformer experimental demonstrate that achieves most satisfactory outcomes than all other classical providing solution improving predicting trajectories, which becomes increasingly important in era safe navigation mixed MASS. As result, findings can aid development implementation proactive preventive measures avoid collisions, enhance efficiency, ensure safety.

Язык: Английский

Процитировано

34
Adaptive collision avoidance decisions in autonomous ship encounter scenarios through rule-guided vision supervised learning DOI
Kangjie Zheng, Xinyu Zhang, Chengbo Wang

и другие.

Ocean Engineering, Год журнала: 2024, Номер 297, С. 117096 - 117096

Опубликована: Фев. 17, 2024

Язык: Английский

Процитировано

16
A data mining-then-predict method for proactive maritime traffic management by machine learning DOI Creative Commons
Zhao Liu, Wanli Chen, Cong Liu

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 135, С. 108696 - 108696

Опубликована: Июнь 7, 2024

Язык: Английский

Процитировано

12
Incorporation of adaptive compression into a GPU parallel computing framework for analyzing large-scale vessel trajectories DOI Creative Commons
Yan Li, Huanhuan Li, Chao Zhang

и другие.

Transportation Research Part C Emerging Technologies, Год журнала: 2024, Номер 163, С. 104648 - 104648

Опубликована: Май 9, 2024

Automatic Identification System (AIS) offers a wealth of vessel navigation data, which underpins research in maritime data mining, situational awareness, and knowledge discovery within the realm intelligent transportation systems. The flourishing marine industry has prompted AIS satellites base stations to generate massive amounts trajectory escalating both storage calculation costs. conventional Douglas-Peucker (DP) algorithm used for compression sets uniform threshold, hampers effective compression. Additionally, compressing accelerating computation large datasets poses significant challenge real-world applications. To address these limitations, this paper aims develop new Graphics Processing Unit (GPU) parallel computing framework that enables acceleration optimal threshold each automatically big mining. It achieves by incorporating Adaptive DP with Speed Course (ADPSC) algorithm, utilizes dynamic characteristics different vessels. can effectively solve associated computational time cost concern when using ADPSC compress vast real world. proposes novel evaluation metric assessing efficacy based on Dynamic Time Warping (DTW) method. Comprehensive experiments encompass from three representative areas: Tianjin Port, Chengshan Jiao Promontory, Caofeidian Port. experimental results demonstrate 1) newly developed method outperforms terms compression, 2) designed GPU significantly shorten extensive datasets. GPU-accelerated methodology not only minimizes transmission costs manned unmanned vessels but also enhances processing speed, supporting real-time decision-making. From theoretical perspective, it provides key puzzle realizing anti-collision ships, particularly complex waters. hence makes contributions safety autonomous shipping era.

Язык: Английский

Процитировано

10
A hybrid deep learning method for the prediction of ship time headway using automatic identification system data DOI Creative Commons

Quandang Ma,

Xu Du, Cong Liu

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 133, С. 108172 - 108172

Опубликована: Март 8, 2024

Ship Time Headway (STH) is used in maritime navigation to describe the time interval between arrivals of two consecutive ships same water area. This measurement may offer a straightforward way gauge frequency ship traffic and likelihood congestion particular STH an important factor understanding managing dynamics movements busy waterways. paper introduces hybrid deep learning method for predicting domain. The integrates Seasonal-Trend Decomposition using Loess (STL), Multi-head Self-Attention (MSA) mechanism into Long Short-Term Memory (LSTM) neural network. dataset was extracted from Automatic Identification System (AIS) through trajectory spatial motion, seasonal, trend residual components decomposition were then determined STL algorithms. MSA-LSTM adopted comprehensively capture evolving patterns sequence. Comparison studies with existing methods demonstrate accuracy robustness predictions provided by this method, indicating that proposed outperforms other models terms prediction performance capabilities. By STH, offers potential assist managers navigators assessing flow, thereby enabling them make informed decisions on safety efficiency.

Язык: Английский

Процитировано

9
Time-evolving graph-based approach for multi-ship encounter analysis: Insights into ship behavior across different scenario complexity levels DOI
Yuerong Yu, Kezhong Liu, Wei Kong

и другие.

Transportation Research Part A Policy and Practice, Год журнала: 2025, Номер 194, С. 104427 - 104427

Опубликована: Фев. 27, 2025

Язык: Английский

Процитировано

2
Application of switching-input LSTM network for vessel trajectory prediction DOI
Weihong Wang, Yi Zuo, Licheng Zhao

и другие.

Applied Intelligence, Год журнала: 2025, Номер 55(4)

Опубликована: Янв. 9, 2025

Язык: Английский

Процитировано

1