论文标题
MATS：计划和控制的可解释的轨迹预测表示
MATS: An Interpretable Trajectory Forecasting Representation for Planning and Control
论文作者
论文摘要
关于人类运动的推理是现代人类机器人互动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机器人运动计划和控制提供信息。但是，大多数计划和控制算法的原因是关于系统动力学的原因，而不是通常通过轨迹预测方法输出的预测代理轨迹（即，订购的路点集），这可能会阻碍其集成。为此，我们提出了仿射时变系统（MATS）的混合物作为轨迹预测的输出表示形式，这更适合下游计划和控制使用。我们的方法利用了概率轨迹预测工作的成功思想，以学习在计划和控制文献中经过充分研究的动态系统表示。我们将预测与提出的多模式计划方法相结合，并在大规模自动驾驶数据集上显示出显着的计算效率提高。
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.
