The final **MarVelocity** prediction is: \begin{equation} V_{\text{MarV}} = V_{\text{HM}} + \hat{\Delta V}(\mathbf{x}). \end{equation}
\bigskip \noindent\textbf{Keywords:} maritime speed prediction, AIS data, hydrodynamic resistance, machine learning, fuel efficiency, autonomous vessels marvelocity pdf
\subsection{Learning the Residual} Define the residual speed: \begin{equation} \Delta V = V_{\text{SOG}} - V_{\text{HM}}, \end{equation} where $V_{\text{SOG}}$ is the measured speed over ground from AIS. We train a Gradient‑Boosted Regression Tree (XGBoost \cite{Chen2016}) to predict $\Delta V$ from the feature vector $\mathbf{x}$: \[ \mathbf{x} = \bigl[\,\underbrace{L, B, D, C_B}_{\text{design}};\, \underbrace{V_{\text{HM}}}_{\text{baseline}};\, \underbrace{U_{10}, \theta_{\text{wind}}}_{\text{wind}};\, \underbrace{H_s, \theta_{\text{wave}}}_{\text{wave}};\, \underbrace{U_c, \theta_{\text{current}}}_{\text{current}}\,\bigr]. \] \] Recent work has shown that **data‑driven** techniques
Recent work has shown that **data‑driven** techniques can capture residual dynamics missed by physics‑based formulas \cite{Bai2021, Chen2022}. However, many studies either (i) treat speed prediction as a black‑box regression problem without incorporating physical insight, or (ii) lack rigorous validation on out‑of‑sample vessels. Our contribution is two‑fold: \begin{enumerate}[label=\alph*)] \item We define **MarVelocity**, a hybrid metric that augments a baseline hydrodynamic resistance model with a learned correction term. \item We provide a large‑scale, ship‑agnostic evaluation pipeline, demonstrating superior accuracy and tangible fuel savings. \end{enumerate} evaluation scripts). \end{enumerate}
\subsection{Future Work} \begin{enumerate} \item Extension to **fuel‑consumption** prediction via a joint multi‑task network. \item Incorporation of **ship‑maneuvering** dynamics for autonomous docking. \item Open‑source **benchmark suite** for maritime speed prediction (datasets, evaluation scripts). \end{enumerate}