Prediction-enabled path planning for multi-ship encounters in Oslofjord

Zhu, Mingda; Tian, Weiwei; Skulstad, Robert; Zhang, Houxiang; Li, Guoyuan

doi:10.1016/j.oceaneng.2024.116747

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2024

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Cited by 3 publications

References 29 publications

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Global path planning for Unmanned Surface Vehicles in complex maritime environments considering environmental interference

Mu,

Li,

Han

et al. 2024

Ocean Engineering

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Global path planning for Unmanned Surface Vehicles in complex maritime environments considering environmental interference

Mu,

Li,

Han

et al. 2024

Ocean Engineering

View full text Add to dashboard Cite

A Multi-AttentionLSTM model for multi-ship encounter classification

Tian,

Han,

Zhu

et al. 2024

2024 IEEE International Conference on Real-Time Computing and Robotics (RCAR)

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A Deep Generative Model for Multi-Ship Trajectory Forecasting With Interaction Modeling

Zhu,

Han,

Tian

et al. 2024

Journal of Offshore Mechanics and Arctic Engineering

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Multi-agent modeling is a challenging issue in intelligent systems, which is further compounded by heavy and complex traffic in maritime contexts. Trajectory forecasting can enhance operation safety. Nonetheless, effectively modeling interactions among vessels poses a significant difficulty. Towards this end, we propose a conditional variational autoencoder approach to ship trajectory prediction in a dynamic and multi-modal encounter situation. Leveraging a shared Recurrent Neural Network architecture and attention mechanism, our method aggregates vessel trajectory data, enabling the model to learn and encapsulate meaningful encounter information across active vessels. We utilize Automatic Identification System data from the Oslofjord region to validate our approach. Through comprehensive experiments conducted on a four-ship encounter dataset, our proposed model demonstrates promising performance, by outperforming the benchmark models. Furthermore, we analyze the prediction model in a wide array of dimensions, showcasing its proficiency in complex ship behaviours learning, modeling ship interaction and approximating actual trajectories.

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Prediction-enabled path planning for multi-ship encounters in Oslofjord

Cited by 3 publications

References 29 publications

Global path planning for Unmanned Surface Vehicles in complex maritime environments considering environmental interference

Global path planning for Unmanned Surface Vehicles in complex maritime environments considering environmental interference

A Multi-AttentionLSTM model for multi-ship encounter classification

A Deep Generative Model for Multi-Ship Trajectory Forecasting With Interaction Modeling

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