  Robustness against Out-of-Distribution (OoD) samples is a key performance
indicator of a trajectory prediction model. However, the development and
ranking of state-of-the-art (SotA) models are driven by their In-Distribution
(ID) performance on individual competition datasets. We present an OoD testing
protocol that homogenizes datasets and prediction tasks across two large-scale
motion datasets. We introduce a novel prediction algorithm based on polynomial
representations for agent trajectory and road geometry on both the input and
output sides of the model. With a much smaller model size, training effort, and
inference time, we reach near SotA performance for ID testing and significantly
improve robustness in OoD testing. Within our OoD testing protocol, we further
study two augmentation strategies of SotA models and their effects on model
generalization. Highlighting the contrast between ID and OoD performance, we
suggest adding OoD testing to the evaluation criteria of trajectory prediction
models.
