  Predicting future motions of road participants is an important task for
driving autonomously in urban scenes. Existing models excel at predicting
marginal trajectories for single agents, yet it remains an open question to
jointly predict scene compliant trajectories over multiple agents. The
challenge is due to exponentially increasing prediction space as a function of
the number of agents. In this work, we exploit the underlying relations between
interacting agents and decouple the joint prediction problem into marginal
prediction problems. Our proposed approach M2I first classifies interacting
agents as pairs of influencers and reactors, and then leverages a marginal
prediction model and a conditional prediction model to predict trajectories for
the influencers and reactors, respectively. The predictions from interacting
agents are combined and selected according to their joint likelihoods.
Experiments show that our simple but effective approach achieves
state-of-the-art performance on the Waymo Open Motion Dataset interactive
prediction benchmark.
