  Human motion trajectory prediction, an essential task for autonomous systems
in many domains, has been on the rise in recent years. With a multitude of new
methods proposed by different communities, the lack of standardized benchmarks
and objective comparisons is increasingly becoming a major limitation to assess
progress and guide further research. Existing benchmarks are limited in their
scope and flexibility to conduct relevant experiments and to account for
contextual cues of agents and environments. In this paper we present Atlas, a
benchmark to systematically evaluate human motion trajectory prediction
algorithms in a unified framework. Atlas offers data preprocessing functions,
hyperparameter optimization, comes with popular datasets and has the
flexibility to setup and conduct underexplored yet relevant experiments to
analyze a method's accuracy and robustness. In an example application of Atlas,
we compare five popular model- and learning-based predictors and find that,
when properly applied, early physics-based approaches are still remarkably
competitive. Such results confirm the necessity of benchmarks like Atlas.
