dc.contributor.author | Han, Peihua | |
dc.contributor.author | Zhu, Mingda | |
dc.contributor.author | Zhang, Houxiang | |
dc.date.accessioned | 2023-12-15T13:02:57Z | |
dc.date.available | 2023-12-15T13:02:57Z | |
dc.date.created | 2023-08-23T12:42:49Z | |
dc.date.issued | 2023 | |
dc.identifier.issn | 1551-3203 | |
dc.identifier.uri | https://hdl.handle.net/11250/3107809 | |
dc.description.abstract | Navigation safety is of paramount importance in areas with heavy and complex maritime traffic. Any ship navigating such a scenario should be able to foresee the future positions of other ships and adjust its path accordingly to avoid collisions. However, predicting future trajectories is a very challenging problem due to many possible future trajectories from the inherent uncertainty and the complex interaction dynamics between different ships. In this article, we propose a deep generative model based on the conditional variational autoencoder framework to learn marine vessel movement and predict future trajectories. The model is able to produce a multimodal probability distribution over future trajectories and model the complex interactions between vessels. Experiments are performed in two-vessel encounter scenarios from real-world automatic identification system data. The proposed model outperforms the baseline methods, including both kinematics-based and data-driven methods. The trajectories predicted by the proposed model are also analyzed to demonstrate the effectiveness of the model. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Interaction-Aware Short-Term Marine Vessel Trajectory Prediction With Deep Generative Models | en_US |
dc.title.alternative | Interaction-Aware Short-Term Marine Vessel Trajectory Prediction With Deep Generative Models | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.journal | IEEE Transactions on Industrial Informatics | en_US |
dc.identifier.doi | 10.1109/TII.2023.3302304 | |
dc.identifier.cristin | 2169000 | |
cristin.ispublished | true | |
cristin.fulltext | preprint | |
cristin.qualitycode | 2 | |