M2I: 周辺軌道予測からインタラクティブ予測へ
M2I: From Factored Marginal Trajectory Prediction to Interactive Prediction
相互作用するエージェントを影響側と反応側のペアに分け、周辺予測と条件付き予測を組み合わせてマルチエージェントの軌道を統合的に予測する手法を提案した。
著者: Qiao Sun, Xin Huang, Junru Gu, Brian C. Williams, Hang Zhao
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
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.
