マルチエージェント環境におけるインスタンス認識型予測ナビゲーション
Instance-Aware Predictive Navigation in Multi-Agent Environments
エージェント間の相互作用や将来のシーン構造を予測するマルチインスタンスイベント予測モジュールを提案し、安全な行動系列をサンプリングして動的環境での運転方策を効率的に学習する手法。
著者: Jinkun Cao, Xin Wang, Trevor Darrell, Fisher Yu
分類: cs.RO, cs.CV
原文アブストラクト
In this work, we aim to achieve efficient end-to-end learning of driving policies in dynamic multi-agent environments. Predicting and anticipating future events at the object level are critical for making informed driving decisions. We propose an Instance-Aware Predictive Control (IPC) approach, which forecasts interactions between agents as well as future scene structures. We adopt a novel multi-instance event prediction module to estimate the possible interaction among agents in the ego-centric view, conditioned on the selected action sequence of the ego-vehicle. To decide the action at each step, we seek the action sequence that can lead to safe future states based on the prediction module outputs by repeatedly sampling likely action sequences. We design a sequential action sampling strategy to better leverage predicted states on both scene-level and instance-level. Our method establishes a new state of the art in the challenging CARLA multi-agent driving simulation environments without expert demonstration, giving better explainability and sample efficiency.
