  Predicting agents' future trajectories plays a crucial role in modern AI
systems, yet it is challenging due to intricate interactions exhibited in
multi-agent systems, especially when it comes to collision avoidance. To
address this challenge, we propose to learn congestion patterns as contextual
cues explicitly and devise a novel "Sense--Learn--Reason--Predict" framework by
exploiting advantages of three different doctrines of thought, which yields the
following desirable benefits: (i) Representing congestion as contextual cues
via latent factors subsumes the concept of social force commonly used in
physics-based approaches and implicitly encodes the distance as a cost, similar
to the way a planning-based method models the environment. (ii) By decomposing
the learning phases into two stages, a "student" can learn contextual cues from
a "teacher" while generating collision-free trajectories. To make the framework
computationally tractable, we formulate it as an optimization problem and
derive an upper bound by leveraging the variational parametrization. In
experiments, we demonstrate that the proposed model is able to generate
collision-free trajectory predictions in a synthetic dataset designed for
collision avoidance evaluation and remains competitive on the commonly used
NGSIM US-101 highway dataset.
