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Multi-modal graph convolutional network for vessel trajectory prediction based on cooperative intention enhance using conditional variational autoencoder

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Title: Multi-modal graph convolutional network for vessel trajectory prediction based on cooperative intention enhance using conditional variational autoencoder
Authors: Jiang, Junhao, Zuo, Yi, Li, Zhiyuan, 1974
Source: Reliability Engineering and System Safety. 267
Subject Terms: Graph convolutional network, Trajectory prediction, Cooperative intention constructor, Intelligent situational awareness systems, Conditional variational autoencoder, Multi-modal interaction extractor
Description: 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.
Access URL: https://research.chalmers.se/publication/549377
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Multi-modal graph convolutional network for vessel trajectory prediction based on cooperative intention enhance using conditional variational autoencoder
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Jiang%2C+Junhao%22">Jiang, Junhao</searchLink><br /><searchLink fieldCode="AR" term="%22Zuo%2C+Yi%22">Zuo, Yi</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Zhiyuan%22">Li, Zhiyuan</searchLink>, 1974
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  Data: <i>Reliability Engineering and System Safety</i>. 267
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  Data: <searchLink fieldCode="DE" term="%22Graph+convolutional+network%22">Graph convolutional network</searchLink><br /><searchLink fieldCode="DE" term="%22Trajectory+prediction%22">Trajectory prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Cooperative+intention+constructor%22">Cooperative intention constructor</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+situational+awareness+systems%22">Intelligent situational awareness systems</searchLink><br /><searchLink fieldCode="DE" term="%22Conditional+variational+autoencoder%22">Conditional variational autoencoder</searchLink><br /><searchLink fieldCode="DE" term="%22Multi-modal+interaction+extractor%22">Multi-modal interaction extractor</searchLink>
– Name: Abstract
  Label: Description
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  Data: 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.
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      – Type: doi
        Value: 10.1016/j.ress.2025.111885
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: Graph convolutional network
        Type: general
      – SubjectFull: Trajectory prediction
        Type: general
      – SubjectFull: Cooperative intention constructor
        Type: general
      – SubjectFull: Intelligent situational awareness systems
        Type: general
      – SubjectFull: Conditional variational autoencoder
        Type: general
      – SubjectFull: Multi-modal interaction extractor
        Type: general
    Titles:
      – TitleFull: Multi-modal graph convolutional network for vessel trajectory prediction based on cooperative intention enhance using conditional variational autoencoder
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            NameFull: Jiang, Junhao
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            NameFull: Li, Zhiyuan
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2026
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              Value: 09518320
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            – Type: volume
              Value: 267
          Titles:
            – TitleFull: Reliability Engineering and System Safety
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