A collision risk identification method for vessels in marine operational waters based on the Especial Cautious Navigation Angle
With the rapid growth of marine engineering activities in navigable waters, collision risk identification has become increasingly complex due to the coexistence of operational vessels and passing traffic. To address this challenge, this paper proposes a collision risk identification method based on the Especial Cautious Navigation Angle (ECNA) model for waters with ongoing marine operations. Operational scenarios are classified according to the maneuvering characteristics of operational vessels, and corresponding safety boundaries are defined. Based on these characteristics, an extended ECNA model is developed to describe the collision risk relationship between navigating vessels and operational areas. Furthermore, a collision risk scoring algorithm is introduced to quantify collision risk levels and analyse their temporal evolution. To validate the effectiveness of the proposed approach, multiple simulation experiments are conducted under different encounter conditions. The results demonstrate that the proposed ECNA model provides improved visualisation capability in complex navigational environments. The proposed method enables intuitive collision risk interpretation, supports multi-target prioritisation, and offers a practical framework for real-time collision risk assessment in waters with ongoing marine operations.
Authors
- Wei Pan
- Xin-Lian Xie
- Meng Li
- Ping He
- Yu-Kuan Wang
- Xiao-wei Xu
Institutions
- Shanghai Jiao Tong University (CN)
- Anhui University of Finance and Economics (CN)
- Wuhan University of Technology (CN)
- Liaoning University of International Business and Economics (CN)
- Dalian Maritime University (CN)
Publication Details
- Journal
- Journal of Marine Engineering & Technology
- Published
- 2026-08-14
- DOI
- https://doi.org/10.1080/20464177.2026.2718639
- Primary Topic
- Maritime Navigation and Safety
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Natural Science Foundation of Liaoning Province