A data-driven approach for ship-bridge collision candidate detection in bridge waterway. (15th December 2022)

• Record Type: Journal Article
• Authors: Zhang, Liang; Chen, Pengfei; Li, Mengxia; Chen, Linying; Mou, Junmin
• Is Part Of: Ocean engineering. Volume 266(2022)Part 5
• Publication Date: 2022-12-15
• Subjects: Collision candidate detection -- Ship-bridge collision avoidance -- Data-driven -- Kernel density estimation -- Velocity obstacle; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2022.113137
• Languages: English
• ISSNs: 0029-8018

Abstract

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. Highlights: Navigation features of bridge waterways are recognized by the KDE method. Linear-Nonlinear integrated velocity obstacle is applied to collision candidate detection in bridge waterways. Collision candidates between ships and ship-bridge are identified and analyzed.