  Advances in learning-based trajectory prediction are enabled by large-scale
datasets. However, in-depth analysis of such datasets is limited. Moreover, the
evaluation of prediction models is limited to metrics averaged over all samples
in the dataset. We propose an automated methodology that allows to extract
maneuvers (e.g., left turn, lane change) from agent trajectories in such
datasets. The methodology considers information about the agent dynamics and
information about the lane segments the agent traveled along. Although it is
possible to use the resulting maneuvers for training classification networks,
we exemplary use them for extensive trajectory dataset analysis and
maneuver-specific evaluation of multiple state-of-the-art trajectory prediction
models. Additionally, an analysis of the datasets and an evaluation of the
prediction models based on the agent dynamics is provided.
