QSD-LSTM: Vessel trajectory prediction using long short-term memory with quaternion ship domain

Ryan Wen Liu, Kunlin Hu, Maohan Liang, Yan Li, Xin Liu, Dong Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)

Abstract

Vessel trajectory prediction is a critical aspect of ensuring maritime traffic safety and avoiding collisions. The long short-term memory (LSTM) network and its extensions have represented powerful ability of vessel trajectory prediction. However, the previous studies often did not take dynamic interactions between neighboring vessels into account. Additionally, in complex traffic conditions, trajectory prediction will acquire uncertainty, and these potential negative factors can limit the prediction of future trajectory. To enhance the prediction performance, we propose an interactive vessel trajectory prediction framework (i.e., QSD-LSTM) based on LSTM, which is embedded with the quaternion ship domain (QSD). The QSD is beneficial for avoiding unwanted collision between neighboring vessels. In addition, the operation of trajectory clustering is further incorporated into our trajectory prediction framework, potentially leading to more robust prediction results. Numerous experiments have been implemented on realistic automatic identification system (AIS)-based vessel trajectories to compare our QSD-LSTM with several state-of-the-art prediction methods. The prediction results have demonstrated the superior performance of our method in terms of both quantitative and qualitative evaluations.

Original languageEnglish
Article number103592
JournalApplied Ocean Research
Volume136
DOIs
Publication statusPublished - Jul 2023

Keywords

  • Automatic identification system (AIS)
  • Collision avoidance
  • Long short-term memory (LSTM)
  • Quaternion ship domain (QSD)
  • Vessel trajectory prediction

ASJC Scopus subject areas

  • Ocean Engineering

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