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Trajectory risk cognition of ship collision accident based on fusion of multi-model spatial data

Published online by Cambridge University Press:  07 February 2022

Tao Liu ,
Shuo Wang ,
Zhengling Lei ,
Jinfeng Zhang  and
Xiaocai Zhang
Tao Liu
Affiliation:
College of Transport & Communications, Shanghai Maritime University, Shanghai, China. Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan, China. State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China.
Shuo Wang
Affiliation:
College of Transport & Communications, Shanghai Maritime University, Shanghai, China.
Zhengling Lei*
Affiliation:
College of Engineering Science and Technology, Shanghai Ocean University, Shanghai, China.
Jinfeng Zhang
Affiliation:
Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan, China.
Xiaocai Zhang
Affiliation:
Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
*
*Corresponding author. E-mail: zllei@shou.edu.cn
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Abstract

When conducting accident analysis, the assessment of risk is one of the important links. Moreover, with regards to crew training, risk cognition is also an important training subject. However, most of the existing researches only rely on a single or a few data sources. It is necessary to fuse the collected multi-source data to obtain a more comprehensive risk evaluation model. There are few studies on the three-dimensional (3D) multi-modal data-fusion-based trajectory risk cognition. In this paper, a fuzzy logic-based trajectory risk cognition method is proposed based on multi-model spatial data fusion and accident data mining. First, the necessity of multi-model spatial data fusion is analysed and a data-fusion-based scene map is constructed. Second, a risk cognition model fused by multiple factors, multi-dimensional spatial calculations as well as data mining results is proposed, including a novel ship boundary calculation approach and newly constructed factors. Finally, a radar chart is used to illustrate the risk, and a risk cognition system is developed. Experiment results confirm the effectiveness of the method. It can be applied to train human operators of unmanned ship systems.

Keywords

ECDISmarine trafficnavigationoceantrajectory
Type
Research Article
Information
The Journal of Navigation , Volume 75 , Issue 2 , March 2022 , pp. 299 - 318
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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