Abstract Advances in artificial intelligence are driving the development of intelligent transportation systems, with the purpose of enhancing the safety and efficiency of such systems. One of the most important aspects of maritime safety is effective collision avoidance. In this study, a novel dual linear autoencoder approach is suggested to predict the future trajectory of a selected vessel. Such predictions can serve as a decision support tool to evaluate the future risk of ship collisions. Inspired by generative models, the method suggests to predict the future trajectory of a vessel based on historical AIS data. Using unsupervised learning to facilitate trajectory clustering and classification, the method utilizes a cluster of historical AIS trajectories to predict the trajectory of a selected vessel. Similar methods predict future states iteratively, where states are dependent upon the prior predictions. The method in this study, however, suggests predicting an entire trajectory, where all states are predicted jointly. Further, the method estimates a latent distribution of the possible future trajectories of the selected vessel. By sampling from this distribution, multiple trajectories are predicted. The uncertainties of the predicted vessel positions are also quantified in this study.

中文翻译：

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使用历史 AIS 数据进行船舶轨迹预测的双线性自动编码器方法

摘要 人工智能的进步正在推动智能交通系统的发展，旨在提高此类系统的安全性和效率。海上安全最重要的方面之一是有效避免碰撞。在这项研究中，建议采用一种新颖的双线性自动编码器方法来预测选定船只的未来轨迹。这种预测可以作为决策支持工具来评估未来船舶碰撞的风险。受生成模型的启发，该方法建议根据历史 AIS 数据预测船舶的未来轨迹。使用无监督学习来促进轨迹聚类和分类，该方法利用历史 AIS 轨迹的集群来预测选定船只的轨迹。类似的方法迭代地预测未来状态，其中状态取决于先前的预测。然而，本研究中的方法建议预测整个轨迹，其中所有状态都是联合预测的。此外，该方法估计所选船只的可能未来轨迹的潜在分布。通过从该分布中采样，可以预测多个轨迹。本研究还量化了预测船只位置的不确定性。