Continual Learning for Handling Maritime Data Shifts in Vessel Trajectory Prediction

The highly dynamic characteristics of the maritime environment present significant challenges for vessel trajectory prediction. Traditional statistical and machine learning models often struggle to adapt to changing conditions and new data streams, leading to performance degradation. To address these well known issues, we propose the use of Continual Learning that enables the system to learn incrementally from sequential data streams. Our proposal avoids catastrophic forgetting of previously acquired knowledge through a replay-based approach. This strategy ensures that the prediction model can track and adapt to shifting environmental factors and variations in vessel behavior. We test the Continual Learning-based model using high-frequency trajectory data recorded by a cruise vessel Voyage Data Recorder. Experimental results indicate that our approach achieves a lower error compared to conventional static learning models. It mitigates catastrophic forgetting, ensuring the retention of critical information from past vessel movements, and demonstrates a strong capacity to adapt to data shifts inherent in real-world maritime operations. These findings highlight the potential of Continual Learning to enhance the reliability and robustness of vessel trajectory prediction systems in an ever-changing maritime landscape.