论文标题
通过风险和场景图学习的异质轨迹预测
Heterogeneous Trajectory Forecasting via Risk and Scene Graph Learning
论文作者
论文摘要
异质轨迹预测对于智能运输系统至关重要，但是由于难以建模异质道路代理之间的复杂相互作用关系以及其代理 - 环境的约束，这是一项挑战。在这项工作中，我们提出了一种风险和场景图表学习方法，用于对异质道路代理的轨迹预测，该方法由异构风险图（HRG）和层次结构场景图（HSG）组成。 HRG将每种道路代理分组，并根据有效的碰撞风险度量计算其相互作用邻接矩阵。驾驶现场的HSG是通过推断道路代理与路面语法一致的道路语义布局之间的关系进行建模的。基于此公式，我们可以在驾驶情况下获得有效的轨迹预测，并且在Nuscenes，Apolloscape和Argoverse数据集上进行了详尽的实验证明了与其他最先进的方法的卓越性能。
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.
