ScePT: 計画のためのシーン整合性を持つポリシーベース軌道予測
ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planning
エージェント間の衝突を防ぐシーン整合性を明示的に強制し、シーン全体の軌道を予測するポリシーベースの軌道予測モデルScePTを提案。歩行者・自動運転データセットで精度と整合性を両立し、下流の計画への応用も示した。
著者: Yuxiao Chen, Boris Ivanovic, Marco Pavone
分類: cs.AI, cs.CV, cs.LG, cs.RO
原文アブストラクト
Trajectory prediction is a critical functionality of autonomous systems that share environments with uncontrolled agents, one prominent example being self-driving vehicles. Currently, most prediction methods do not enforce scene consistency, i.e., there are a substantial amount of self-collisions between predicted trajectories of different agents in the scene. Moreover, many approaches generate individual trajectory predictions per agent instead of joint trajectory predictions of the whole scene, which makes downstream planning difficult. In this work, we present ScePT, a policy planning-based trajectory prediction model that generates accurate, scene-consistent trajectory predictions suitable for autonomous system motion planning. It explicitly enforces scene consistency and learns an agent interaction policy that can be used for conditional prediction. Experiments on multiple real-world pedestrians and autonomous vehicle datasets show that ScePT} matches current state-of-the-art prediction accuracy with significantly improved scene consistency. We also demonstrate ScePT's ability to work with a downstream contingency planner.
