Vessel Trajectory Prediction Using Vessel Influence Long Short-Term Memory with Uncertainty Estimation
Vessel trajectory prediction plays a crucial role in ensuring the safety and efficiency of maritime transportation. This study proposes an innovative sequence-to-sequence model, called the Vessel Influence Long Short-Term Memory (VI-LSTM), which introduces a novel Vessel Influence Map (VIM) to quant...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/2/353 |
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| author | Zhiyuan Guo Huimin Qiang Xiaodong Peng |
| author_facet | Zhiyuan Guo Huimin Qiang Xiaodong Peng |
| author_sort | Zhiyuan Guo |
| collection | DOAJ |
| description | Vessel trajectory prediction plays a crucial role in ensuring the safety and efficiency of maritime transportation. This study proposes an innovative sequence-to-sequence model, called the Vessel Influence Long Short-Term Memory (VI-LSTM), which introduces a novel Vessel Influence Map (VIM) to quantitatively model the dynamic effects of surrounding vessels. To enhance reliability, VI-LSTM incorporates Gaussian distribution predictions combined with Monte Carlo dropout techniques to estimate prediction uncertainty. Additionally, a temporally weighted hybrid loss function is designed to balance prediction accuracy and uncertainty. Furthermore, this study systematically categorizes and models factors influencing vessel trajectory prediction. Experimental results demonstrate that VI-LSTM achieves a mean distance error of 330.66 m on the standard test set and 480.30 m on an unseen subject test set, outperforming other comparative models, particularly in complex navigation scenarios and high-density maritime environments. These innovations significantly improve the accuracy and generalizability of vessel trajectory predictions, leading to enhanced safety, increased efficiency, and more effective collision avoidance in maritime navigation. |
| format | Article |
| id | doaj-art-5efdfd0361054d5ca253ca6e74d562d8 |
| institution | DOAJ |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-5efdfd0361054d5ca253ca6e74d562d82025-08-20T02:44:56ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-02-0113235310.3390/jmse13020353Vessel Trajectory Prediction Using Vessel Influence Long Short-Term Memory with Uncertainty EstimationZhiyuan Guo0Huimin Qiang1Xiaodong Peng2University of Chinese Academy of Sciences, Beijing 100049, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaNational Space Science Center, Chinese Academy of Sciences, Beijing 100190, ChinaVessel trajectory prediction plays a crucial role in ensuring the safety and efficiency of maritime transportation. This study proposes an innovative sequence-to-sequence model, called the Vessel Influence Long Short-Term Memory (VI-LSTM), which introduces a novel Vessel Influence Map (VIM) to quantitatively model the dynamic effects of surrounding vessels. To enhance reliability, VI-LSTM incorporates Gaussian distribution predictions combined with Monte Carlo dropout techniques to estimate prediction uncertainty. Additionally, a temporally weighted hybrid loss function is designed to balance prediction accuracy and uncertainty. Furthermore, this study systematically categorizes and models factors influencing vessel trajectory prediction. Experimental results demonstrate that VI-LSTM achieves a mean distance error of 330.66 m on the standard test set and 480.30 m on an unseen subject test set, outperforming other comparative models, particularly in complex navigation scenarios and high-density maritime environments. These innovations significantly improve the accuracy and generalizability of vessel trajectory predictions, leading to enhanced safety, increased efficiency, and more effective collision avoidance in maritime navigation.https://www.mdpi.com/2077-1312/13/2/353trajectory predictionvessel influence mapuncertainty estimationmultifactor predictiontemporally weighted hybrid loss function |
| spellingShingle | Zhiyuan Guo Huimin Qiang Xiaodong Peng Vessel Trajectory Prediction Using Vessel Influence Long Short-Term Memory with Uncertainty Estimation Journal of Marine Science and Engineering trajectory prediction vessel influence map uncertainty estimation multifactor prediction temporally weighted hybrid loss function |
| title | Vessel Trajectory Prediction Using Vessel Influence Long Short-Term Memory with Uncertainty Estimation |
| title_full | Vessel Trajectory Prediction Using Vessel Influence Long Short-Term Memory with Uncertainty Estimation |
| title_fullStr | Vessel Trajectory Prediction Using Vessel Influence Long Short-Term Memory with Uncertainty Estimation |
| title_full_unstemmed | Vessel Trajectory Prediction Using Vessel Influence Long Short-Term Memory with Uncertainty Estimation |
| title_short | Vessel Trajectory Prediction Using Vessel Influence Long Short-Term Memory with Uncertainty Estimation |
| title_sort | vessel trajectory prediction using vessel influence long short term memory with uncertainty estimation |
| topic | trajectory prediction vessel influence map uncertainty estimation multifactor prediction temporally weighted hybrid loss function |
| url | https://www.mdpi.com/2077-1312/13/2/353 |
| work_keys_str_mv | AT zhiyuanguo vesseltrajectorypredictionusingvesselinfluencelongshorttermmemorywithuncertaintyestimation AT huiminqiang vesseltrajectorypredictionusingvesselinfluencelongshorttermmemorywithuncertaintyestimation AT xiaodongpeng vesseltrajectorypredictionusingvesselinfluencelongshorttermmemorywithuncertaintyestimation |