Analyzing appropriate autonomous vessel for South-East Asian route: from the view of seafarers

Author:

Rahman BornaliORCID

Abstract

AbstractThe development of autonomous vessel has achieved tremendous interest across the world for the safe navigation and economic benefits. Numerous alternatives are constructed in the autonomous vessel development projects, the alternatives of MUNIN and NYK project are combined for this study; these are - Manned autonomous vessel, Remotely controlled vessel, Autonomous and Partially remote-controlled vessel, and Full autonomous vessel. As the statistics of UNCTAD shows that South-East Asia is a highly dense region and has the busiest international maritime connectivity, this research tried to select the appropriate autonomous vessels from the four alternatives to ensure safe navigation in this traffic congested maritime route. For this study, 311 accident reports are investigated to find out the most frequent casualty and its cause. The data are collected from the global integrated shipping information system of the international maritime organization's website. The decision tree of R-studio demonstrates that the most frequent accidents are- Collision, Grounding, Fire, and listing. Afterwards a survey was made on 65 experienced seafarers to determine which autonomous vessel criteria would be compatible to avoid the casualty. This research adopts AHP (analytical hierarchy process) to conduct the analysis. AHP is a multi-criteria decision analysis method for solving any decision problem. The research shows that ‘Manned autonomous vessel’ and ‘Autonomous and Partially remote-controlled vessel’ are the appropriate alternatives for safe navigation in the South-East Asian region. This study will help the researcher who is working in autonomous vessel development, mainly working for Asian water.

Publisher

Springer Science and Business Media LLC

Subject

General Medicine

Cited by 1 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. Adversarial Maritime Trajectory Prediction with Real-time Spatial-Temporal Mutual Influence;2023 IEEE International Conference on Data Mining Workshops (ICDMW);2023-12-04

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