Multi-modal graph convolutional network for vessel trajectory prediction based on cooperative intention enhance using conditional variational autoencoder
Advances in maritime instrumentation and measurement (I&M), particularly through the widespread adoption of automatic identification system (AIS), have significantly accelerated the development of intelligent situational awareness systems (ISAS). As a critical component of ISAS, cooperative navi...
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| Published in: | Reliability engineering & system safety Vol. 267; p. 111885 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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01.03.2026
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| ISSN: | 0951-8320 |
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| Abstract | Advances in maritime instrumentation and measurement (I&M), particularly through the widespread adoption of automatic identification system (AIS), have significantly accelerated the development of intelligent situational awareness systems (ISAS). As a critical component of ISAS, cooperative navigation demands greater accuracy and reliability in vessel trajectory prediction. Nevertheless, challenges arising from complex inter-vessel interactions and implicit intention inference expose limitations in modeling explicit and implicit relationships and ensuring the robustness of trajectory prediction. To address these challenges, we propose a cooperative intention enhance multi-modal graph convolutional network (CIE-MGCN) to learn and predict the future vessel trajectories. The CIE-MGCN is composed of three primary components: Interaction Extractor, Intention Constructor, and Trajectory Generator. In Interaction Extractor, we designed the social-community extractor (SCE) to construct diverse interaction graphs that capture both cooperative and adversarial relationships among vessel trajectories, and the multi-modal transformer (MMT) to fuse explicit interaction features across various modalities. In Intention Constructor, we introduce a conditional variational autoencoder (CVAE)-based approach to infer implicit relationships and capture potential future behavioral variations and multi-modal probability distributions of future trajectories are produced by Trajectory Generator. Extensive experiments on real-world navigation data show that CIE-MGCN outperforms state-of-the-art models in accuracy and robustness, owing to its strong reasoning and learning capabilities. These reliable predictions further support cooperative navigation within ISAS by enhancing coordination and decision-making among multi-vessel.
•This study proposes a model (CIE-MGCN) to enhance maritime situational awareness.•The CIE-MGCN models the dynamic relationships among vessel trajectories.•The CIE-MGCN models the intention of future navigational trajectories.•The CIE-MGCN was evaluated on different datasets and produced reliable results. |
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| AbstractList | Advances in maritime instrumentation and measurement (I&M), particularly through the widespread adoption of automatic identification system (AIS), have significantly accelerated the development of intelligent situational awareness systems (ISAS). As a critical component of ISAS, cooperative navigation demands greater accuracy and reliability in vessel trajectory prediction. Nevertheless, challenges arising from complex inter-vessel interactions and implicit intention inference expose limitations in modeling explicit and implicit relationships and ensuring the robustness of trajectory prediction. To address these challenges, we propose a cooperative intention enhance multi-modal graph convolutional network (CIE-MGCN) to learn and predict the future vessel trajectories. The CIE-MGCN is composed of three primary components: Interaction Extractor, Intention Constructor, and Trajectory Generator. In Interaction Extractor, we designed the social-community extractor (SCE) to construct diverse interaction graphs that capture both cooperative and adversarial relationships among vessel trajectories, and the multi-modal transformer (MMT) to fuse explicit interaction features across various modalities. In Intention Constructor, we introduce a conditional variational autoencoder (CVAE)-based approach to infer implicit relationships and capture potential future behavioral variations and multi-modal probability distributions of future trajectories are produced by Trajectory Generator. Extensive experiments on real-world navigation data show that CIE-MGCN outperforms state-of-the-art models in accuracy and robustness, owing to its strong reasoning and learning capabilities. These reliable predictions further support cooperative navigation within ISAS by enhancing coordination and decision-making among multi-vessel.
•This study proposes a model (CIE-MGCN) to enhance maritime situational awareness.•The CIE-MGCN models the dynamic relationships among vessel trajectories.•The CIE-MGCN models the intention of future navigational trajectories.•The CIE-MGCN was evaluated on different datasets and produced reliable results. Advances in maritime instrumentation and measurement (I&M), particularly through the widespread adoption of automatic identification system (AIS), have significantly accelerated the development of intelligent situational awareness systems (ISAS). As a critical component of ISAS, cooperative navigation demands greater accuracy and reliability in vessel trajectory prediction. Nevertheless, challenges arising from complex inter-vessel interactions and implicit intention inference expose limitations in modeling explicit and implicit relationships and ensuring the robustness of trajectory prediction. To address these challenges, we propose a cooperative intention enhance multi-modal graph convolutional network (CIE-MGCN) to learn and predict the future vessel trajectories. The CIE-MGCN is composed of three primary components: Interaction Extractor, Intention Constructor, and Trajectory Generator. In Interaction Extractor, we designed the social-community extractor (SCE) to construct diverse interaction graphs that capture both cooperative and adversarial relationships among vessel trajectories, and the multi-modal transformer (MMT) to fuse explicit interaction features across various modalities. In Intention Constructor, we introduce a conditional variational autoencoder (CVAE)-based approach to infer implicit relationships and capture potential future behavioral variations and multi-modal probability distributions of future trajectories are produced by Trajectory Generator. Extensive experiments on real-world navigation data show that CIE-MGCN outperforms state-of-the-art models in accuracy and robustness, owing to its strong reasoning and learning capabilities. These reliable predictions further support cooperative navigation within ISAS by enhancing coordination and decision-making among multi-vessel. |
| ArticleNumber | 111885 |
| Author | Zuo, Yi Li, Zhiyuan Jiang, Junhao |
| Author_xml | – sequence: 1 givenname: Junhao orcidid: 0000-0003-3425-7966 surname: Jiang fullname: Jiang, Junhao email: jiangjunhao@dlmu.edu.cn organization: Navigation College, Dalian Maritime University, Dalian, 116026, Liaoning, China – sequence: 2 givenname: Yi orcidid: 0000-0002-4580-6855 surname: Zuo fullname: Zuo, Yi email: zuo@dlmu.edu.cn organization: Navigation College, Dalian Maritime University, Dalian, 116026, Liaoning, China – sequence: 3 givenname: Zhiyuan orcidid: 0000-0001-6478-1027 surname: Li fullname: Li, Zhiyuan email: zhiyuan.li@chalmers.se organization: Department of Mechanics and Maritime Sciences, Chalmers University of Technology, SE-41296, Gothenburg, Sweden |
| BackLink | https://research.chalmers.se/publication/549377$$DView record from Swedish Publication Index (Chalmers tekniska högskola) |
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| Keywords | Conditional variational autoencoder Trajectory prediction Graph convolutional network Intelligent situational awareness systems Cooperative intention constructor Multi-modal interaction extractor |
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| SubjectTerms | Conditional variational autoencoder Cooperative intention constructor Graph convolutional network Intelligent situational awareness systems Multi-modal interaction extractor Trajectory prediction |
| Title | Multi-modal graph convolutional network for vessel trajectory prediction based on cooperative intention enhance using conditional variational autoencoder |
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