  Predicting the future motion of observed vehicles is a crucial enabler for
safe autonomous driving. The field of motion prediction has seen large progress
recently with state-of-the-Art (sotA) models achieving impressive results on
large-scale public benchmarks. However, recent work revealed that
learning-based methods are prone to predict off-road trajectories in
challenging scenarios. These can be created by perturbing existing scenarios
with additional turns in front of the target vehicle while the motion history
is left unchanged. We argue that this indicates that SotA models do not
consider the map information sufficiently and demonstrate how this can be
solved by representing model inputs and outputs in a Frenet frame defined by
lane centreline sequences. To this end, we present a general wrapper that
leverages a Frenet representation of the scene, and that can be applied to SotA
models without changing their architecture. We demonstrate the effectiveness of
this approach in a comprehensive benchmark using two SotA motion prediction
models. Our experiments show that this reduces the off-road rate on challenging
scenarios by more than 90% without sacrificing average performance.
