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.
翻译:由于多种相互作用,包括代理代理相互作用的代理人和代理相互作用的场景,自动预测多试剂轨迹仍然具有挑战性。虽然最近的方法取得了有希望的性能,但大多数方法只是考虑到相互作用的空间影响,忽略了时间影响总是伴随空间影响这一事实。此外,根据现场信息采用的方法总是需要额外的片段场景图像来产生多种社会可接受的轨迹。为解决这些局限性,我们提出了一个名为空间连续性的空间时钟关注网络的新模式(STAN-SC)。首先,提出了空间时钟关注机制,以探索最有用和最重要的信息。第二,我们根据序列和即时状态信息进行联合地貌序列,以使基因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,并表明拟议的模型达到了最先进的预测准确性,并处理更为复杂的情景。