  Since the past few decades, human trajectory forecasting has been a field of
active research owing to its numerous real-world applications: evacuation
situation analysis, deployment of intelligent transport systems, traffic
operations, to name a few. Early works handcrafted this representation based on
domain knowledge. However, social interactions in crowded environments are not
only diverse but often subtle. Recently, deep learning methods have
outperformed their handcrafted counterparts, as they learned about human-human
interactions in a more generic data-driven fashion. In this work, we present an
in-depth analysis of existing deep learning-based methods for modelling social
interactions. We propose two knowledge-based data-driven methods to effectively
capture these social interactions. To objectively compare the performance of
these interaction-based forecasting models, we develop a large scale
interaction-centric benchmark TrajNet++, a significant yet missing component in
the field of human trajectory forecasting. We propose novel performance metrics
that evaluate the ability of a model to output socially acceptable
trajectories. Experiments on TrajNet++ validate the need for our proposed
metrics, and our method outperforms competitive baselines on both real-world
and synthetic datasets.
