Trajectory prediction in traffic scenes involves accurately forecasting the behaviour of surrounding vehicles. To achieve this objective it is crucial to consider contextual information, including the driving path of vehicles, road topology, lane dividers, and traffic rules. Although studies demonstrated the potential of leveraging heterogeneous context for improving trajectory prediction, state-of-the-art deep learning approaches still rely on a limited subset of this information. This is mainly due to the limited availability of comprehensive representations. This paper presents an approach that utilizes knowledge graphs to model the diverse entities and their semantic connections within traffic scenes. Further, we present nuScenes Knowledge Graph (nSKG), a knowledge graph for the nuScenes dataset, that models explicitly all scene participants and road elements, as well as their semantic and spatial relationships. To facilitate the usage of the nSKG via graph neural networks for trajectory prediction, we provide the data in a format, ready-to-use by the PyG library. All artefacts can be found here: https://github.com/boschresearch/nuScenes_Knowledge_Graph

翻译：交通场景中的轨迹预测涉及准确预测周围车辆的行为。为实现这一目标，必须考虑上下文信息，包括车辆行驶路径、道路拓扑、车道分隔线及交通规则。尽管已有研究表明利用异构上下文信息有助于改进轨迹预测，但当前最先进的深度学习方法仍仅依赖其中有限子集的信息，这主要归因于缺乏全面的表示形式。本文提出了一种基于知识图谱的方法，用于建模交通场景中的多样化实体及其语义关联。此外，我们构建了nuScenes知识图谱（nSKG），该图谱针对nuScenes数据集，显式建模了所有场景参与者与道路元素，以及它们之间的语义和空间关系。为便于通过图神经网络利用nSKG进行轨迹预测，我们提供了可直接用于PyG库的数据格式。所有相关资源可访问：https://github.com/boschresearch/nuScenes_Knowledge_Graph