MATS: 計画・制御に適した解釈可能な軌道予測表現
MATS: An Interpretable Trajectory Forecasting Representation for Planning and Control
軌道予測の出力を、計画・制御で扱いやすいアフィン時変システムの混合表現として学習し、大規模自動運転データで計算効率の向上を示した論文。
著者: Boris Ivanovic, Amine Elhafsi, Guy Rosman, Adrien Gaidon, Marco Pavone
分類: cs.RO, cs.HC, cs.LG, cs.SY, eess.SY
原文アブストラクト
Reasoning about human motion is a core component of modern human-robot interactive systems. In particular, one of the main uses of behavior prediction in autonomous systems is to inform robot motion planning and control. However, a majority of planning and control algorithms reason about system dynamics rather than the predicted agent tracklets (i.e., ordered sets of waypoints) that are commonly output by trajectory forecasting methods, which can hinder their integration. Towards this end, we propose Mixtures of Affine Time-varying Systems (MATS) as an output representation for trajectory forecasting that is more amenable to downstream planning and control use. Our approach leverages successful ideas from probabilistic trajectory forecasting works to learn dynamical system representations that are well-studied in the planning and control literature. We integrate our predictions with a proposed multimodal planning methodology and demonstrate significant computational efficiency improvements on a large-scale autonomous driving dataset.
