An AIS Data-Driven Hybrid Approach to Ship Trajectory Prediction

The safety of navigation is critical in areas with heavy and complex traffic. Accurate prediction of the future trajectory of ships is crucial, especially in encounter situations where conventional navigational devices are prone to high uncertainty and risk due to their limitations. Unfortunately, kinematic models and data-driven methods suffer from numerous issues, such as poor performance, intricate architecture, and reduced interpretability. In response, this article introduces a novel approach, driven by automatic identification system (AIS) data, to enhance the efficacy of ship encounter trajectory prediction. It revolves around encounter classification, wherein the invaluable insights of seamanship play a pivotal role in identifying and categorizing legitimate encounters into three distinct types. This categorization forms the foundation for developing a probability-based classification model, in conjunction with a hybrid predictor that amalgamates a kinematics-based model and a neural network-based model. Historical AIS data collected in Oslofjord, Norway, are utilized in this study, and experiments have been conducted to assess the performance of the proposed method through real cases. The results substantiate the promise of the classification model and underscore the exceptional predictive capabilities of the hybrid approach. This superiority is attributed to the synergistic effects arising from the integration of its constituent models.