Trajectron：動的時空間グラフによる確率的マルチエージェント軌道モデリング
The Trajectron: Probabilistic Multi-Agent Trajectory Modeling With Dynamic Spatiotemporal Graphs
動的に変化する時空間グラフ構造と変分深層生成モデルを組み合わせ、複数エージェントの将来軌道分布を同時に予測するモデルTrajectronを提案した論文。
著者: Boris Ivanovic, Marco Pavone
分類: cs.RO, cs.HC, cs.LG
原文アブストラクト
Developing safe human-robot interaction systems is a necessary step towards the widespread integration of autonomous agents in society. A key component of such systems is the ability to reason about the many potential futures (e.g. trajectories) of other agents in the scene. Towards this end, we present the Trajectron, a graph-structured model that predicts many potential future trajectories of multiple agents simultaneously in both highly dynamic and multimodal scenarios (i.e. where the number of agents in the scene is time-varying and there are many possible highly-distinct futures for each agent). It combines tools from recurrent sequence modeling and variational deep generative modeling to produce a distribution of future trajectories for each agent in a scene. We demonstrate the performance of our model on several datasets, obtaining state-of-the-art results on standard trajectory prediction metrics as well as introducing a new metric for comparing models that output distributions.
