论文标题
小睡：行人轨迹的非自动回归预测
Take a NAP: Non-Autoregressive Prediction for Pedestrian Trajectories
论文作者
论文摘要
行人轨迹预测是一项具有挑战性的任务，因为人类运动行为的三种特性需要解决其他行人的社会影响，场景约束以及预测的多模式（多路线）性质。尽管现有方法探索了这些关键属性，但这些方法的预测过程是自回归的。这意味着他们只能顺序预测未来的位置。在本文中，我们提出了NAP，这是一种非自动回忆的轨迹预测方法。我们的方法包括专门设计的功能编码器和一个潜在变量生成器来处理上述三个属性。它还具有时间不足的上下文生成器和用于非自动性预测的时间特定上下文生成器。通过将NAP与最近几种方法进行比较的广泛实验，我们表明NAP具有最新的轨迹预测性能。
Pedestrian trajectory prediction is a challenging task as there are three properties of human movement behaviors which need to be addressed, namely, the social influence from other pedestrians, the scene constraints, and the multimodal (multiroute) nature of predictions. Although existing methods have explored these key properties, the prediction process of these methods is autoregressive. This means they can only predict future locations sequentially. In this paper, we present NAP, a non-autoregressive method for trajectory prediction. Our method comprises specifically designed feature encoders and a latent variable generator to handle the three properties above. It also has a time-agnostic context generator and a time-specific context generator for non-autoregressive prediction. Through extensive experiments that compare NAP against several recent methods, we show that NAP has state-of-the-art trajectory prediction performance.
