Predicting motion of surrounding agents is critical to real-world applications of tactical path planning for autonomous driving. Due to the complex temporal dependencies and social interactions of agents, on-line trajectory prediction is a challenging task. With the development of attention mechanism in recent years, transformer model has been applied in natural language sequence processing first and then image processing. In this paper, we present a Spatial-Channel Transformer Network for trajectory prediction with attention functions. Instead of RNN models, we employ transformer model to capture the spatial-temporal features of agents. A channel-wise module is inserted to measure the social interaction between agents. We find that the Spatial-Channel Transformer Network achieves promising results on real-world trajectory prediction datasets on the traffic scenes.

翻译：周围物剂的预测运动对于在现实世界应用自主驾驶战术路径规划至关重要。 由于各种物剂的复杂时间依赖性和社会互动关系,在线轨迹预测是一项艰巨的任务。随着近年来注意力机制的发展,变压器模型首先应用于自然语言序列处理,然后用于图像处理。在本论文中,我们提出了一个空间-气道变压器网络,用于轨迹预测和关注功能。我们使用变压器模型来捕捉各种物剂的空间-时空特征。我们插入了一个频道智能模块来衡量各种物剂之间的社会互动。我们发现,空间-气道变压器网络在现实世界交通场的轨迹预测数据集上取得了可喜的成果。