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

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.