Motion prediction for automated vehicles in complex environments is a difficult task that is to be mastered when automated vehicles are to be used in arbitrary situations. Many factors influence the future motion of traffic participants starting with traffic rules and reaching from the interaction between each other to personal habits of human drivers. Therefore we present a novel approach for a graph-based prediction based on a heterogeneous holistic graph representation that combines temporal information, properties and relations between traffic participants as well as relations with static elements like the road network. The information are encoded through different types of nodes and edges that both are enriched with arbitrary features. We evaluated the approach on the INTERACTION and the Argoverse dataset and conducted an informative ablation study to demonstrate the benefit of different types of information for the motion prediction quality.

翻译：自动驾驶车辆在复杂环境中的运动预测是一项艰巨任务，要实现自动驾驶车辆在任意场景中的运用，就必须攻克这一难题。交通参与者的未来运动受多种因素影响，包括交通规则、参与者间的相互作用，乃至人类驾驶员的个人习惯。为此，我们提出一种基于异质全息图表示的新型图基预测方法，该方法融合了时间信息、交通参与者属性及其相互关系，以及与道路网络等静态元素的关系。这些信息通过不同类型的节点和边进行编码，两者均具备任意特征增强。我们在INTERACTION和Argoverse数据集上对该方法进行了评估，并开展了一项信息丰富的消融研究，以论证不同类型信息对运动预测质量的改进效果。