A data-driven approach for ship-bridge collision candidate detection in bridge waterway

Journal article (2022)

Authors

Liang Zhang Wuhan University of Technology, Hubei Key Laboratory of Inland Shipping Technology

Pengfei Chen Wuhan University of Technology, Hubei Key Laboratory of Inland Shipping Technology

M. Li Wuhan University of Technology, Hubei Key Laboratory of Inland Shipping Technology,

Linying Chen Wuhan University of Technology, Hubei Key Laboratory of Inland Shipping Technology

Junmin Mou Wuhan University of Technology, Hubei Key Laboratory of Inland Shipping Technology

DOI: https://doi.org/10.1016/j.oceaneng.2022.113137

Data-driven Kernel density estimation Velocity obstacle Collision candidate detection Ship-bridge collision avoidance

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Published Date

2022

Language

English

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Abstract

The consequences caused by bridge failures owing to the ship-bridge collision are always severe in terms of loss of life, economy, and environmental consequences to individuals and societies. The previous studies focused on the ship-bridge collision mainly concentrated on passive anti-collision, such as strengthening the bridge structure or setting anti-collision facilities. Compared with the previous research, the contribution of this work is to facilitate the reduction of collision risk of ship-bridge collision from the perspective of active anti-collision. A data-driven approach for ship-bridge collision candidate detection method in inland bridge waterways is proposed in this research. The approach is mainly divided into two steps: 1) The features (channel boundary, pier domain, and ship domain) of bridge waterways are identified using Kernel Density Estimation (KDE) method based on the historical AIS data; 2) Collision candidate detection with Velocity Obstacle (VO) method considering the identified features. This work can provide beneficial support for the ship-bridge active collision avoidance system.

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