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Multi-ship encounter situation analysis with the integration of elliptical ship domains and velocity obstacles

Cheng Z., Chen P., Mou J., Chen L.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2023

Vol. 17 No. 4

895--902

With economic globalization, ships tend to be larger and faster, and the volume of maritime traffic is increasing. Ships sailing in waters with dense traffic flow are easy to fall into complicated multi-ship encounter situations and have a high risk of collision. Thus, it is crucial to conduct risk analysis in such situations. In this paper, a modified collision analysis method for detecting dangerous multi-ship encounters in ports and waterways is proposed. The velocity obstacle algorithm is utilized to detect encounters. The model of the elliptic ship domain was integrated into the algorithm as the criteria. The Boolean operation was also used in the multi-ship encounter. A case study is conducted to illustrate the efficacy of the improved model, and a comparison between the existing method and the formal model is also performed. The results indicate that with the integration of the ship domain, the proposed method can effectively detect the encounters of multiple ships which are dangerous to collide.

Integration of elliptical ship domains and velocity obstacles for ship collision candidate detection

Chen P. F., Van Gelder P., Mou J. M.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2019

Vol. 13. no. 4

751--758

The maritime shipping industry has been making significant contributions to the development of the regional and global economy. However, maritime accidents and their severe consequences have been posing an incrementing risk to the individuals and societies. It is therefore important to conduct risk analysis on such accidents to support maritime safety management. In this paper, a modified ship collision candidate detection method is proposed as a tool for collision risk analysis in ports and waterways. Time-Discrete Velocity Obstacle algorithm (TD-NLVO) is utilized to detect collision candidates based on the encounter process extracted from AIS data. Ship domain model was further integrated into the algorithm as the criteria for determination. A case study is conducted to illustrate the efficacy of the improved model, and a comparison between the existing method and actual ship trajectories are also performed. The results indicate that with the integration of ship domain, the new method can effectively detect the encounters with significant collision avoidance behaviours. The choice of criteria can have a significant influence on the results of collision candidate detection.