As more and more robots are envisioned to cooperate with humans sharing the same space, it is desired for robots to be able to predict others’ trajectories to navigate in a safe and self-explanatory way. In this letter, we propose a Convolutional Neural Network-based approach to learn, detect, and extract patterns in sequential trajectory data, known here as Social Pattern Extraction Convolution (Social-PEC). A set of experiments carried out on the human trajectory prediction problem shows that our model performs comparably to the state of the art and outperforms in some cases. More importantly, the proposed approach unveils the obscurity in the previous use of a pooling layer, presenting a way to intuitively explain the decision-making process.

中文翻译：

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注意运动模式：具有用于人体轨迹预测的新型卷积算子的时间 CNN

随着越来越多的机器人被设想与共享同一空间的人类合作，希望机器人能够预测他人的轨迹，以安全且不言自明的方式导航。在这封信中，我们提出了一种基于卷积神经网络的方法来学习、检测和提取顺序轨迹数据中的模式，这里称为社会模式提取卷积 (Social-PEC)。对人体轨迹预测问题进行的一组实验表明，我们的模型与现有技术相当，并且在某些情况下表现更好。更重要的是，所提出的方法揭示了先前使用池化层的模糊性，提供了一种直观解释决策过程的方法。