  Reasoning about human motion is a core component of modern human-robot
interactive systems. In particular, one of the main uses of behavior prediction
in autonomous systems is to inform robot motion planning and control. However,
a majority of planning and control algorithms reason about system dynamics
rather than the predicted agent tracklets (i.e., ordered sets of waypoints)
that are commonly output by trajectory forecasting methods, which can hinder
their integration. Towards this end, we propose Mixtures of Affine Time-varying
Systems (MATS) as an output representation for trajectory forecasting that is
more amenable to downstream planning and control use. Our approach leverages
successful ideas from probabilistic trajectory forecasting works to learn
dynamical system representations that are well-studied in the planning and
control literature. We integrate our predictions with a proposed multimodal
planning methodology and demonstrate significant computational efficiency
improvements on a large-scale autonomous driving dataset.
