The movement of pedestrians involves temporal continuity, spatial interactivity, and random diversity. As a result, pedestrian trajectory prediction is rather challenging. Most existing trajectory prediction methods tend to focus on just one aspect of these challenges, ignoring the temporal information of the trajectory and making too many assumptions. In this paper, we propose a recurrent attention and interaction ( RAI ) model to predict pedestrian trajectories. The RAI model consists of a temporal attention module, spatial pooling module, and randomness modeling module. The temporal attention module is proposed to assign different weights to the input sequence of a target, and reduce the speed deviation of different pedestrians. The spatial pooling module is proposed to model not only the social information of neighbors in historical frames, but also the intention of neighbors in the current time. The randomness modeling module is proposed to model the uncertainty and diversity of trajectories by introducing random noise. We conduct extensive experiments on several public datasets. The results demonstrate that our method outperforms many that are state-of-the-art.

中文翻译：

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行人轨迹预测的循环注意力和交互模型

行人的活动涉及时间连续性，空间互动性和随机多样性。结果，行人轨迹预测相当具有挑战性。大多数现有的轨迹预测方法往往只关注这些挑战的一方面，而忽略了轨迹的时间信息并做出了太多的假设。在本文中，我们提出了一种经常性的注意力和交互作用（RAI）模型来预测行人的轨迹。RAI模型由时间注意模块，空间合并模块和随机性建模模块组成。提出了时间注意模块，将不同的权重分配给目标的输入序列，并减少不同行人的速度偏差。提出了空间池化模块，以不仅对历史框架中邻居的社会信息进行建模，而且还有当前邻居的意图。提出了随机性建模模块，通过引入随机噪声对轨迹的不确定性和多样性进行建模。我们对几个公共数据集进行了广泛的实验。结果表明，我们的方法优于许多最新技术。