A classification framework for digital twins, shadows, and models in autonomous ship navigation

In recent years, the maritime industry is undergoing a transformative shift toward autonomy, driven by advancements in sensor technology, simulation tools, and real-time data processing. Central to this evolution is the concept of the Ship Digital Twin (SDT), a virtual replica of a physical vessel that enables real-time monitoring, simulation, and autonomous decision-making. Despite its increasing significance in academic and industrial discourse, the term “digital twin” is often misapplied to simpler models lacking bidirectional data exchange or standalone functioning ability. This paper addresses the ambiguity by systematically defining and distinguishing between three foundational concepts in maritime digitalization: digital models, digital shadows, and digital twins, each with two progressive levels of development. Through a detailed exploration of kinematic modeling, sensor fusion, control architectures, and AI/ML integration, the study introduces a six-degree framework for evaluating digitalization in ship navigation. The framework highlights the essential role of high-fidelity models and real-time interaction in the path to full ship autonomy. By reviewing recent literature through this perspective, the paper identifies common misclassifications and emphasizes the technological requirements for ship digital twin systems. This work offers a classification system that highlights precise communication between the physical and the virtual ship enabling structured development of autonomous maritime technologies.