Ship Trajectory Prediction in Complex Waterways Based on Transformer and Social Variational Autoencoder (SocialVAE) Article Swipe
Pengyue Wang
,
Mingyang Pan
,
Zongying Liu
,
Shaoxi Li
,
Yuanlong Chen
,
Wei Yang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/jmse12122233
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/jmse12122233
Ship trajectory prediction plays a key role in the early warning and safety of maritime traffic. It is a necessary assistant tool that can forecast a ship’s trajectory in a certain period to prevent ship collision. However, highly precise prediction of long-term ship trajectories is still a challenge. This study proposes a ship trajectory prediction model called ShipTrack-TVAE, which is based on a Variational Autoencoder (SocialVAE) and Transformer architecture. It aims to address ship trajectory prediction tasks in complex waterways. To enable the model to avoid potential collision risks, this study designs a collision avoidance mechanism, which comprehensively incorporates safety constraints related to the distance between ships into the loss function. The experimental results show that on the Qiongzhou Strait ship AIS dataset, the Average Displacement Error (ADE) of ShipTrack-TVAE improved by 21.85% compared to the current state-of-the-art trajectory prediction model, SocialVAE, while the Final Displacement Error (FDE) improved by 17.83%. The experimental results demonstrate that the ShipTrack-TVAE model can effectively improve the prediction accuracy of short-term, medium-term, and long-term ship trajectories. It has excellent performance and provides a certain reference value for advancing unmanned ship collision avoidance.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/jmse12122233
- https://www.mdpi.com/2077-1312/12/12/2233/pdf?version=1733393653
- OA Status
- gold
- Cited By
- 5
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405077015
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405077015Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/jmse12122233Digital Object Identifier
- Title
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Ship Trajectory Prediction in Complex Waterways Based on Transformer and Social Variational Autoencoder (SocialVAE)Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-05Full publication date if available
- Authors
-
Pengyue Wang, Mingyang Pan, Zongying Liu, Shaoxi Li, Yuanlong Chen, Wei YangList of authors in order
- Landing page
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https://doi.org/10.3390/jmse12122233Publisher landing page
- PDF URL
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https://www.mdpi.com/2077-1312/12/12/2233/pdf?version=1733393653Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2077-1312/12/12/2233/pdf?version=1733393653Direct OA link when available
- Concepts
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Trajectory, Collision, Autoencoder, Computer science, Mean squared prediction error, Transformer, Simulation, Artificial neural network, Artificial intelligence, Engineering, Algorithm, Computer security, Electrical engineering, Physics, Astronomy, VoltageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5Per-year citation counts (last 5 years)
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32Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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