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:在复杂环境中对自动化车辆的机动性预测是一项艰巨的任务,在自动车辆被任意使用时,必须掌握这一任务,许多因素影响交通参与者今后从交通规则开始、从相互作用到人驾驶员的个人习惯的运动,因此,我们提出了一个基于图表的预测新办法,其基础是综合时间信息、特性和交通参与者之间的关系以及同公路网络等静态元素的关系的综合整体图示,以图为基础的预测。信息通过不同种类的节点和边缘进行编码,这两种节点和边缘都具有任意性。我们评价了InterACTION和Argovers数据集的方法,并进行了信息化的模拟研究,以展示不同类型的信息对运动预测质量的好处。