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，唯有掌握此技术，自动驾驶车辆才能在任意场景中实际应用。交通参与者的未来运动受多重因素影响，从交通规则到参与者间的交互行为，乃至人类驾驶员个人习惯等不一而足。为此，我们提出了一种基于异构全局图表示的新型图预测方法。该方法融合了时间信息、交通参与者属性及相互关系，以及与道路网络等静态元素的关联。通过不同节点与边（均可附加任意特征）编码信息。我们在INTERACTION与Argoverse数据集上评估了该方法，并进行了信息充分的消融研究，以证明不同类型信息对运动预测质量的提升效果。