  Reasoning about human motion is an important prerequisite to safe and
socially-aware robotic navigation. As a result, multi-agent behavior prediction
has become a core component of modern human-robot interactive systems, such as
self-driving cars. While there exist many methods for trajectory forecasting,
most do not enforce dynamic constraints and do not account for environmental
information (e.g., maps). Towards this end, we present Trajectron++, a modular,
graph-structured recurrent model that forecasts the trajectories of a general
number of diverse agents while incorporating agent dynamics and heterogeneous
data (e.g., semantic maps). Trajectron++ is designed to be tightly integrated
with robotic planning and control frameworks; for example, it can produce
predictions that are optionally conditioned on ego-agent motion plans. We
demonstrate its performance on several challenging real-world trajectory
forecasting datasets, outperforming a wide array of state-of-the-art
deterministic and generative methods.
