  Automated vehicles (AVs) are tested in diverse scenarios, typically specified
by parameters such as velocities, distances, or curve radii. To describe
scenarios uniformly independent of such parameters, this paper proposes a
vectorized scenario description defined by the road geometry and vehicles'
trajectories. Data of this form are generated for three scenarios, merged, and
used to train the motion prediction model VectorNet, allowing to predict an
AV's trajectory for unseen scenarios. Predicting scenario evaluation metrics,
VectorNet partially achieves lower errors than regression models that
separately process the three scenarios' data. However, for comprehensive
generalization, sufficient variance in the training data must be ensured. Thus,
contrary to existing methods, our proposed method can merge diverse scenarios'
data and exploit spatial and temporal nuances in the vectorized scenario
description. As a result, data from specified test scenarios and real-world
scenarios can be compared and combined for (predictive) analyses and scenario
selection.
