Accurate trajectory prediction plays a key role in robot navigation. It is beneficial for planning a collision-free and appropriate path for the autonomous robots, especially in crowded scenes. However, it is a particularly challenging task because there are complex and subtle interactions among pedestrians. There have been many studies focusing on how to model this spatial interactions but most of them neglected the temporal characteristic. Towards this end, we propose a novel Global Social Spatial-Temporal Attentive Neural Network for trajectory-to-trajectory prediction (Tra2Tra). In this model, we first extract features of spatial interactions with decentralization operation and attention mechanism, and then iteratively extract its temporal dependency through the Long Short-Term Memory network for obtaining the global spatial-temporal feature representation. We further aggregate this global spatial-temporal feature representation and velocity features into our encoder-decoder module for prediction. In order to make multi-modality predictions, we introduce a random noise perturbation while decoding, which enhances the robustness and the generalization ability of our model. Experimental results demonstrate that our Tra2Tra model can achieve better performance than the state-of-the-art methods not only on two pedestrian-walking datasets, i.e. ETH and UCY, but also on three other complex trajectory datasets, i.e. Collisions, NGsim and Charges.

中文翻译：

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Tra2Tra：使用全球社交时空专心神经网络进行轨迹到轨迹的预测

准确的轨迹预测在机器人导航中起着关键作用。为自主机器人规划无冲突且合适的路径非常有益，尤其是在拥挤的场景中。但是，这是一项特别具有挑战性的任务，因为行人之间存在复杂而微妙的交互。已经有许多研究集中于如何对这种空间相互作用进行建模，但是它们中的大多数都忽略了时间特性。为此，我们提出了一种新颖的全球社会时空专心神经网络，用于轨迹到轨迹的预测（Tra2Tra）。在此模型中，我们首先提取具有分散操作和注意力机制的空间互动特征，然后通过长短期记忆网络迭代提取其时间相关性，以获得全局时空特征表示。我们进一步将此全局时空特征表示和速度特征汇总到我们的编码器-解码器模块中进行预测。为了进行多模态预测，我们在解码时引入了随机噪声扰动，这增强了模型的鲁棒性和泛化能力。实验结果表明，不仅在两个行人步行数据集（即ETH和UCY）上，而且在其他三个复杂轨迹数据集（即Collisions，NGsim和Charges）上，我们的Tra2Tra模型都可以比最新方法获得更好的性能。我们进一步将此全局时空特征表示和速度特征汇总到我们的编码器-解码器模块中进行预测。为了进行多模态预测，我们在解码时引入了随机噪声扰动，从而增强了模型的鲁棒性和泛化能力。实验结果表明，我们的Tra2Tra模型不仅在两个行人步行数据集（即ETH和UCY）上，而且在其他三个复杂轨迹数据集（即Collisions，NGsim和Charges）上都比最新方法具有更好的性能。我们进一步将此全局时空特征表示和速度特征汇总到我们的编码器-解码器模块中进行预测。为了进行多模态预测，我们在解码时引入了随机噪声扰动，这增强了模型的鲁棒性和泛化能力。实验结果表明，我们的Tra2Tra模型不仅在两个行人步行数据集（即ETH和UCY）上，而且在其他三个复杂轨迹数据集（即Collisions，NGsim和Charges）上都比最新方法具有更好的性能。