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 ego-robot motion planning and control. However, a majority of planning and control algorithms reason about system dynamics rather than the predicted agent tracklets 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.

中文翻译：

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MATS：用于规划和控制的可解释轨迹预测表示

对人体运动的推理是现代人机交互系统的核心组成部分。特别是，自主系统中行为预测的主要用途之一是为自我机器人运动规划和控制提供信息。然而，大多数规划和控制算法的原因是系统动力学而不是轨迹预测方法通常输出的预测代理轨迹，这可能会阻碍它们的集成。为此，我们提出仿射时变系统（MATS）的混合物作为轨迹预测的输出表示，更适合下游规划和控制使用。我们的方法利用来自概率轨迹预测工作的成功想法来学习在规划和控制文献中得到充分研究的动态系统表示。