VesNet: a Vessel Network for Jointly Learning Route Pattern and Future Trajectory

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Fenyu Jiang, Huandong Wang, Yong Li
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引用次数: 0

Abstract

Vessel trajectory prediction is the key to maritime applications such as traffic surveillance, collision avoidance, anomaly detection, etc. Making predictions more precisely requires a better understanding of the moving trend for a particular vessel since the movement is affected by multiple factors like marine environment, vessel type, and vessel behavior. In this paper, we propose a model named VesNet, based on the attentional seq2seq framework, to predict vessel future movement sequence by observing the current trajectory. Firstly, we extract the route patterns from the raw AIS data during preprocessing. Then, we design a multi-task learning structure to learn how to implement route pattern classification and vessel trajectory prediction simultaneously. By comparing with representative baseline models, we find that our VesNet has the best performance in terms of long-term prediction precision. Additionally, VesNet can recognize the route pattern by capturing the implicit moving characteristics. The experimental results prove that the proposed multi-task learning assists the vessel trajectory prediction mission.

VesNet:联合学习路线模式和未来轨迹的容器网络
船舶轨迹预测是交通监控、避免碰撞、异常检测等海事应用的关键。要更精确地进行预测,就必须更好地了解特定船只的运动趋势,因为运动受海洋环境、船只类型和船只行为等多种因素的影响。在本文中,我们基于注意力 seq2seq 框架提出了一个名为 VesNet 的模型,通过观察当前轨迹来预测船舶未来的移动序列。首先,我们在预处理过程中从原始 AIS 数据中提取航线模式。然后,我们设计了一种多任务学习结构,学习如何同时实现航线模式分类和船舶轨迹预测。通过与具有代表性的基线模型进行比较,我们发现 VesNet 在长期预测精度方面表现最佳。此外,VesNet 还能通过捕捉隐含的移动特征来识别航线模式。实验结果证明,所提出的多任务学习方法有助于完成船舶轨迹预测任务。
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来源期刊
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.30
自引率
2.00%
发文量
131
期刊介绍: ACM Transactions on Intelligent Systems and Technology is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. ACM TIST is published quarterly (six issues a year). Each issue has 8-11 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers.
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