A model for vessel trajectory prediction based on long short-term memory neural network
Each vessel has its own way of sailing in the port region. Any autonomous vessel navigating such a scene should be able to predict the trajectories of surrounding ships and adjust its behaviour to avoid a collision. In this paper, combined with the sequence prediction method, a Long Short-Term Memory (LSTM) model is proposed to predict the trajectories of the vessels. The ground-truth Automatic Identification System (AIS) data in the port of Tianjin, China are used to train and test the proposed model. The experimental results prove that this model can predict ship trajectories accurately, and it is applicable to the autonomous navigation system.
- Record URL:
-
Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/20464177
-
Supplemental Notes:
- © 2019 Institute of Marine Engineering, Science & Technology. Abstract reprinted with permission of Taylor & Francis.
-
Authors:
- Tang, Huang
- Yin, Yong
-
0000-0002-2947-5275
- Shen, Helong
-
0000-0002-3799-3582
- Publication Date: 2022-5
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 136-145
-
Serial:
- Journal of Marine Engineering & Technology
- Volume: 21
- Issue Number: 3
- Publisher: Taylor & Francis
- ISSN: 2046-4177
- Serial URL: http://www.tandfonline.com/tmar20
Subject/Index Terms
- TRT Terms: Automatic vehicle identification; Autonomous vehicles; Neural networks; Predictive models; Ships; Vehicle trajectories
- Identifier Terms: Tianjin Port (China)
- Geographic Terms: Tianjin (China)
- Subject Areas: Data and Information Technology; Marine Transportation; Vehicles and Equipment;
Filing Info
- Accession Number: 01849616
- Record Type: Publication
- Files: TRIS
- Created Date: Jun 24 2022 5:07PM