  Autonomous surface vessels (ASVs) play an increasingly important role in the
safety and sustainability of open sea operations. Since most maritime accidents
are related to human failure, intelligent algorithms for autonomous collision
avoidance and path following can drastically reduce the risk in the maritime
sector. A DT is a virtual representative of a real physical system and can
enhance the situational awareness (SITAW) of such an ASV to generate optimal
decisions. This work builds on an existing DT framework for ASVs and
demonstrates foundations for enabling predictive, prescriptive, and autonomous
capabilities. In this context, sophisticated target tracking approaches are
crucial for estimating and predicting the position and motion of other dynamic
objects. The applied tracking method is enabled by real-time automatic
identification system (AIS) data and synthetic light detection and ranging
(Lidar) measurements. To guarantee safety during autonomous operations, we
applied a predictive safety filter, based on the concept of nonlinear model
predictive control (NMPC). The approaches are implemented into a DT built with
the Unity game engine. As a result, this work demonstrates the potential of a
DT capable of making predictions, playing through various what-if scenarios,
and providing optimal control decisions according to its enhanced SITAW.
