Ship Collision Risk Assessment Based on D-S Evidence Theory

DOI

10.2991/978-94-6463-514-0_11How to use a DOI?

Keywords

Ship collision risk; Risk evaluation indicators; Membership function; D-S Evidence Theory

Abstract

Accurately and effectively assessing the collision risk between ships is of great significance for the formulation of ship collision avoidance decisions. However, in different encounter situations, different evaluation indicators may play different roles, making it difficult to accurately and effectively calculate the risk of such ship collisions. Therefore, this article proposes a ship collision risk assessment model based on the D-S evidence theory. The model selects five factors: nearest encounter distance, nearest encounter time, relative distance between ships, relative orientation, and ship speed ratio to establish an evaluation index membership function. On this basis, the joint basic probability allocation method is used to evaluate the risk of ship collision. Three different ship collision scenarios were set up to validate the collision risk model of the ship, and the experimental results verified the effectiveness of the model.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Liujiang Zheng
AU  - Longhui Gang
AU  - Dongqin Liu
AU  - Xusheng Wang
PY  - 2024
DA  - 2024/09/28
TI  - Ship Collision Risk Assessment Based on D-S Evidence Theory
BT  - Proceedings of the 2024 7th International Symposium on Traffic Transportation and Civil Architecture (ISTTCA 2024)
PB  - Atlantis Press
SP  - 83
EP  - 90
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-514-0_11
DO  - 10.2991/978-94-6463-514-0_11
ID  - Zheng2024
ER  -