An Interactive Multiple-Model Approach for Accurate and Interpretable Trajectory Prediction in Autonomous Docking

Autonomous vessel docking presents significant challenges due to complex vessel dynamics, confined waterways, and critical safety requirements, demanding robust trajectory prediction models. This study proposes an interactive multiple-model (IMM) approach that seamlessly integrates physics-based and data-driven approaches to enhance predictive accuracy, interpretability, and reliability in real-world docking operations. The proposed approach adapts dynamically to changing operational conditions throughout the docking process, ensuring robust performance. The IMM approach incorporates a temporal belief decay mechanism to manage prediction uncertainties, assigning higher confidence to near-term estimates while accommodating long-term variability. This approach is complemented by a locally linear weighting mechanism, which provides interpretable insights into the influence of each model component, thereby enhancing transparency and trust in autonomous docking systems. The approach is validated in 28.9 m-length research vessel Gunnerus’ real-world docking tests, demonstrating superior trajectory prediction accuracy, confidence-aware outputs, and explainable decision-making compared to conventional single-model approaches. Results highlight that the hybrid methodology improves prediction precision and reliability, effectively bridging the gap between physics-based knowledge and data-driven adaptability. These findings suggest that the proposed IMM approach can enhance the safety, reliability, and operational efficiency of autonomous maritime systems.