Heterogeneous trajectory forecasting is critical for intelligent transportation systems, but it is challenging because of the difficulty of modeling the complex interaction relations among the heterogeneous road agents as well as their agent-environment constraints. In this work, we propose a risk and scene graph learning method for trajectory forecasting of heterogeneous road agents, which consists of a Heterogeneous Risk Graph (HRG) and a Hierarchical Scene Graph (HSG) from the aspects of agent category and their movable semantic regions. HRG groups each kind of road agent and calculates their interaction adjacency matrix based on an effective collision risk metric. HSG of the driving scene is modeled by inferring the relationship between road agents and road semantic layout aligned by the road scene grammar. Based on this formulation, we can obtain effective trajectory forecasting in driving situations, and superior performance to other state-of-the-art approaches is demonstrated by exhaustive experiments on the nuScenes, ApolloScape, and Argoverse datasets.

翻译：异构轨迹预测对智能交通系统至关重要，但由于难以建模异构道路主体间复杂的交互关系及其与环境约束的关联，该任务极具挑战性。本文提出一种基于风险和场景图学习的异构道路主体轨迹预测方法，该方法从主体类别及其可移动语义区域维度，分别构建异构风险图（HRG）与层级场景图（HSG）。其中，HRG对各道路主体类别进行分组，并基于有效碰撞风险度量计算其交互邻接矩阵；HSG通过推理道路主体与基于道路场景语法约束的语义布局间关系，对驾驶场景进行建模。基于该框架，我们可在驾驶场景中实现有效的轨迹预测，并在nuScenes、ApolloScape和Argoverse数据集上的大量实验证明，该方法性能优于现有最先进方法。