Multi-ship encounter situation graph structure learning for ship collision avoidance based on AIS big data with spatio-temporal edge and node attention graph convolutional networks-Reference-Cited by-同舟云学术

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Multi-ship encounter situation graph structure learning for ship collision avoidance based on AIS big data with spatio-temporal edge and node attention graph convolutional networks

Published:2024-06 Issue: Volume:301 Page:117605
ISSN:0029-8018
Container-title:Ocean Engineering
language:en
Short-container-title:Ocean Engineering

Author:

Gao Miao^ORCID,Liang Maohan^ORCID,Zhang Anmin,Hu Yingjun,Zhu Jixiang

Publisher

Elsevier BV

Reference46 articles.

1. Collision avoidance method for multiple autonomous mobile agents by implicit cooperation;Abe,2001

2. Formation of MASS collision avoidance and path following based on artificial potential field in con-strained environment;Chen;J. Mar. Sci. Eng.,2022

3. Personnel trajectory extraction from port-like videos under varied rainy interferences;Chen;IEEE Trans. Intell. Transport. Syst.,2024

4. Ship imaging trajectory extraction via an aggregated you only look once (YOLO) model;Chen;Eng. Appl. Artif. Intell.,2024

5. ANNUAL OVERVIEW OF MARINE CASUALTIES AND INCIDENTS,2022

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A CNNGRU-MHA method for ship trajectory prediction based on marine fusion data;Ocean Engineering;2024-10

2. A dynamic topology analysis method for multi-ship encounters based on multi time-space network trees;Ocean Engineering;2024-09

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