The deployment of autonomous navigation systems on ships necessitates accurate motion prediction models tailored to individual vessels. Traditional physics-based models, while grounded in hydrodynamic principles, often fail to account for ship-specific behaviors under real-world conditions. Conversely, purely data-driven models offer specificity but lack interpretability and robustness in edge cases. This study proposes a data-driven physics-based model that integrates physics-based equations with data-driven parameter optimization, leveraging the strengths of both approaches to ensure interpretability and adaptability. The model incorporates physics-based components such as 3-DoF dynamics, rudder, and propeller forces, while parameters such as resistance curve and rudder coefficients are optimized using synthetic data. By embedding domain knowledge into the parameter optimization process, the fitted model maintains physical consistency. Validation of the approach is realized with two container ships by comparing, both qualitatively and quantitatively, predictions against ground-truth trajectories. The results demonstrate significant improvements, in predictive accuracy and reliability, of the data-driven physics-based models over baseline physics-based models tuned with traditional marine engineering practices. The fitted models capture ship-specific behaviors in diverse conditions with their predictions being, 51.6% (ship A) and 57.8% (ship B) more accurate, 72.36% (ship A) and 89.67% (ship B) more consistent.

翻译：船舶自主导航系统的部署需要针对个体船舶的精确运动预测模型。传统的基于物理的模型虽然基于水动力学原理，但往往无法反映真实条件下船舶特有的行为。相反，纯数据驱动模型虽具有针对性，但在边缘案例中缺乏可解释性和鲁棒性。本研究提出一种数据驱动的基于物理的模型，该模型将基于物理的方程与数据驱动的参数优化相结合，融合两种方法的优势以确保可解释性和适应性。模型包含基于物理的组件，如三自由度动力学、舵力和螺旋桨力，同时利用合成数据优化阻力曲线和舵系数等参数。通过将领域知识嵌入参数优化过程，拟合后的模型保持了物理一致性。通过将两艘集装箱船的预测轨迹与真实轨迹进行定性和定量比较，验证了该方法的有效性。结果表明，相较于采用传统船舶工程实践调优的基线物理模型，数据驱动的基于物理模型在预测精度和可靠性方面均有显著提升。拟合模型能够捕捉不同条件下船舶特有的行为，其预测精度分别提高了51.6%（船舶A）和57.8%（船舶B），一致性分别提高了72.36%（船舶A）和89.67%（船舶B）。