Trajectory prediction in autonomous driving relies on accurate representation
of all relevant contexts of the driving scene including traffic participants,
road topology, traffic signs as well as their semantic relations to each other.
Despite increased attention to this issue, most approaches in trajectory
prediction do not consider all of these factors sufficiently. This paper
describes a method SemanticFormer to predict multimodal trajectories by
reasoning over a semantic traffic scene graph using a hybrid approach. We
extract high-level information in the form of semantic meta-paths from a
knowledge graph which is then processed by a novel pipeline based on multiple
attention mechanisms to predict accurate trajectories. The proposed
architecture comprises a hierarchical heterogeneous graph encoder, which can
capture spatio-temporal and relational information across agents and between
agents and road elements, and a predictor that fuses the different encodings
and decodes trajectories with probabilities. Finally, a refinement module
evaluates permitted meta-paths of trajectories and speed profiles to obtain
final predicted trajectories. Evaluation of the nuScenes benchmark demonstrates
improved performance compared to the state-of-the-art methods.

本文介绍了一种使用混合方法通过对语义交通场景图进行推理来预测多模态轨迹的方法 SemanticFormer。该方法从知识图谱中提取语义元路径的高级信息，并通过多个注意机制的新型流水线对其进行处理，以预测准确的轨迹。所提出的架构包括一个分层异构图编码器，可以捕捉代理之间及代理与道路元素之间的时空和关系信息，以及一个使用概率融合不同编码并解码轨迹的预测器。最后，一个改进模块评估轨迹和速度配置文件的准许元路径，以获得最终的预测轨迹。与现有方法相比，对 nuScenes 基准测试的评估显示出改进的性能。