Related papers: MATS: An Interpretable Trajectory Forecasting Representation for Planning and Control

MATS: An Interpretable Trajectory Forecasting Representation for Planning and Control

URL: http://arxiv.org/abs/2009.07517v2
Date: Thu, 14 Jan 2021 09:46:46 GMT
Title: MATS: An Interpretable Trajectory Forecasting Representation for Planning and Control
Authors: Boris Ivanovic, Amine Elhafsi, Guy Rosman, Adrien Gaidon, Marco Pavone
Abstract summary: Reasoning about human motion is a core component of modern human-robot interactive systems. One of the main uses of behavior prediction in autonomous systems is to inform robot motion planning and control. We propose a new output representation for trajectory forecasting that is more amenable to downstream planning and control use.
Score: 46.86174832000696
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.