  Trajectory planning in autonomous driving is highly dependent on predicting
the emergent behavior of other road users. Learning-based methods are currently
showing impressive results in simulation-based challenges, with
transformer-based architectures technologically leading the way. Ultimately,
however, predictions are needed in the real world. In addition to the shifts
from simulation to the real world, many vehicle- and country-specific shifts,
i.e. differences in sensor systems, fusion and perception algorithms as well as
traffic rules and laws, are on the agenda. Since models that can cover all
system setups and design domains at once are not yet foreseeable, model
adaptation plays a central role. Therefore, a simulation-based study on
transfer learning techniques is conducted on basis of a transformer-based
model. Furthermore, the study aims to provide insights into possible trade-offs
between computational time and performance to support effective transfers into
the real world.
