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Trajectory prediction is a critical functionality of autonomous systems thatshare environments with uncontrolled agents, one prominent example beingself-driving vehicles. Currently, most prediction methods do not enforce sceneconsistency, i.e., there are a substantial amount of self-collisions betweenpredicted trajectories of different agents in the scene. Moreover, manyapproaches generate individual trajectory predictions per agent instead ofjoint trajectory predictions of the whole scene, which makes downstreamplanning difficult. In this work, we present ScePT, a policy planning-basedtrajectory prediction model that generates accurate, scene-consistenttrajectory predictions suitable for autonomous system motion planning. Itexplicitly enforces scene consistency and learns an agent interaction policythat can be used for conditional prediction. Experiments on multiple real-worldpedestrians and autonomous vehicle datasets show that ScePT} matches currentstate-of-the-art prediction accuracy with significantly improved sceneconsistency. We also demonstrate ScePT's ability to work with a downstreamcontingency planner.

ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planning