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 in: Ocean engineering 2024-06, Vol.301, p.117605, Article 117605
• Main Authors: Gao, Miao, Liang, Maohan, Zhang, Anmin, Hu, Yingjun, Zhu, Jixiang
• Format: Article
• Language: English
• Subjects: AIS big data; Collision avoidance; Decision-making; Multi-ship encounters; ST-ENAGCN
• ISSN: 0029-8018; 1873-5258
• DOI: 10.1016/j.oceaneng.2024.117605

With the increasing number of ships on the sea, the frequency multi-ship encounters situation was becoming more common than two-ship encounter. The complexity and risk of the navigation will exponentially increase with the more ships involved. Relying solely on the International Regulations for Preventing Collisions at Sea (COLREGS) (objective knowledge) was insufficient to handle the multi-ship intelligent collision avoidance problem, also needed the ship officer's good seamanship (subjective knowledge). In this study, we propose a methodology that combines subjective insights from AIS big data with objective analysis through multi-ship encounters recognition with graph convolutional networks (GCN). (1) The ship encounter 8-azimuths map was utilized to identify the two-ship encounter situation (25 types) from the AIS data. (2) Identify the multi-ship encounters trajectory data by cross-matching the two-ship encounter data. (3) To handle the intricate relationship information between the multiple ships which were transformed into a graph structure using graph theory. (4) Finally, a spatial-temporal edge and node attention graph convolutional network (ST-ENAGCN) was proposed with the graph convolutional unit and long short-term memory (LSTM) unit. The 2022 Ningbo Sea area AIS big data was utilized to achieve graph-structured learning regarding human experiences during the multi-ship encounters situation. The results indicate that the ST-ENAGCN model can understand the complex marine traffic situation to make the own ship collision avoidance decisions. This study contributes significantly to the increased efficiency and safety of sea operations in complex marine traffic conditions and support autonomous navigation of swarm of MASSs under the human-machine hybrid (HMH) conditions to better understand the collision avoidance intention of the ship officer in the multi-ship encounters situation. •The ship encounter 8-azimuths map was utilized to extract the data of 25 types of two-ships encounter modes from the AIS big data. Identify the multi-ships encounter trajectory data by cross-matching the two-ships encounter data.•The intricate relationships information between the multi-ship were transformed into a graph structure using graph theory.•A spatial temporal edge and node attention graph convolutional networks was proposed with the edge and node attention graph convolutional unit and long short-term memory unit, that was utilized to achieve graph structured l