论文标题
具有空间连续性的空间周期性细胞网络用于轨迹预测
A Spatial-Temporal Attentive Network with Spatial Continuity for Trajectory Prediction
论文作者
论文摘要
由于多个相互作用，包括代理相互作用和代理相互作用的场景，自动预测多代理轨迹仍然具有挑战性。尽管最近的方法已经达到了有希望的表现，但其中大多数只是考虑了相互作用的空间影响，而忽略了时间影响总是伴随空间影响的事实。此外，这些基于场景信息的方法总是需要额外的分段场景图像来生成多个社会可接受的轨迹。为了解决这些局限性，我们提出了一种具有空间连续性（Stan-SC）的新型模型，称为时空的专注网络。首先，提出了时空注意机制，以探讨最有用和最重要的信息。其次，我们根据序列和即时状态信息进行联合特征序列，以使生成轨迹保持空间连续性。实验是在两个广泛使用的ETH-UCY数据集上进行的，并证明所提出的模型可实现最先进的预测准确性并处理更复杂的方案。
It remains challenging to automatically predict the multi-agent trajectory due to multiple interactions including agent to agent interaction and scene to agent interaction. Although recent methods have achieved promising performance, most of them just consider spatial influence of the interactions and ignore the fact that temporal influence always accompanies spatial influence. Moreover, those methods based on scene information always require extra segmented scene images to generate multiple socially acceptable trajectories. To solve these limitations, we propose a novel model named spatial-temporal attentive network with spatial continuity (STAN-SC). First, spatial-temporal attention mechanism is presented to explore the most useful and important information. Second, we conduct a joint feature sequence based on the sequence and instant state information to make the generative trajectories keep spatial continuity. Experiments are performed on the two widely used ETH-UCY datasets and demonstrate that the proposed model achieves state-of-the-art prediction accuracy and handles more complex scenarios.
