  As robots across domains start collaborating with humans in shared
environments, algorithms that enable them to reason over human intent are
important to achieve safe interplay. In our work, we study human intent through
the problem of predicting trajectories in dynamic environments. We explore
domains where navigation guidelines are relatively strictly defined but not
clearly marked in their physical environments. We hypothesize that within these
domains, agents tend to exhibit short-term motion patterns that reveal context
information related to the agent's general direction, intermediate goals and
rules of motion, e.g., social behavior. From this intuition, we propose
Social-PatteRNN, an algorithm for recurrent, multi-modal trajectory prediction
that exploits motion patterns to encode the aforesaid contexts. Our approach
guides long-term trajectory prediction by learning to predict short-term motion
patterns. It then extracts sub-goal information from the patterns and
aggregates it as social context. We assess our approach across three domains:
humans crowds, humans in sports and manned aircraft in terminal airspace,
achieving state-of-the-art performance.
