  Methods to quantify the complexity of trajectory datasets are still a missing
piece in benchmarking human trajectory prediction models. In order to gain a
better understanding of the complexity of trajectory prediction tasks and
following the intuition, that more complex datasets contain more information,
an approach for quantifying the amount of information contained in a dataset
from a prototype-based dataset representation is proposed. The dataset
representation is obtained by first employing a non-trivial spatial sequence
alignment, which enables a subsequent learning vector quantization (LVQ) stage.
A large-scale complexity analysis is conducted on several human trajectory
prediction benchmarking datasets, followed by a brief discussion on indications
for human trajectory prediction and benchmarking.
