The prediction of surrounding agents' motion is a key for safe autonomous driving. In this paper, we explore navigation maps as an alternative to the predominant High Definition (HD) maps for learning-based motion prediction. Navigation maps provide topological and geometrical information on road-level, HD maps additionally have centimeter-accurate lane-level information. As a result, HD maps are costly and time-consuming to obtain, while navigation maps with near-global coverage are freely available. We describe an approach to integrate navigation maps into learning-based motion prediction models. To exploit locally available HD maps during training, we additionally propose a model-agnostic method for knowledge distillation. In experiments on the publicly available Argoverse dataset with navigation maps obtained from OpenStreetMap, our approach shows a significant improvement over not using a map at all. Combined with our method for knowledge distillation, we achieve results that are close to the original HD map-reliant models. Our publicly available navigation map API for Argoverse enables researchers to develop and evaluate their own approaches using navigation maps.

翻译：周围智能体运动预测是安全自动驾驶的关键。本文探索了将导航地图作为主流高精地图的替代方案，用于基于学习的运动预测。导航地图提供道路级别的拓扑与几何信息，而高精地图额外具备厘米级车道级信息。因此，高精地图获取成本高昂且耗时，而几乎覆盖全球的导航地图可免费获取。我们提出了一种将导航地图集成到基于学习的运动预测模型中的方法。为在训练中利用局部高精地图，我们进一步提出了一种与模型无关的知识蒸馏方法。在基于OpenStreetMap获取导航地图的公开Argoverse数据集上的实验表明，我们的方法相比不使用地图具有显著改进。结合我们的知识蒸馏方法，我们取得了接近原始依赖高精地图模型的结果。我们公开的Argoverse导航地图API使研究人员能够开发并评估基于导航地图的自主方法。