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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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Název: Multi-modal graph convolutional network for vessel trajectory prediction based on cooperative intention enhance using conditional variational autoencoder
Autoři: Jiang, Junhao, Zuo, Yi, Li, Zhiyuan, 1974
Zdroj: Reliability Engineering and System Safety. 267
Témata: Graph convolutional network, Trajectory prediction, Cooperative intention constructor, Intelligent situational awareness systems, Conditional variational autoencoder, Multi-modal interaction extractor
Popis: 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.
Přístupová URL adresa: https://research.chalmers.se/publication/549377
Databáze: SwePub
Popis
Abstrakt: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.
ISSN:09518320
DOI:10.1016/j.ress.2025.111885