Using HD maps directly as training data for machine learning tasks has seen a massive surge in popularity and shown promising results, e.g. in the field of map perception. Despite that, a standardized HD map framework supporting all parts of map-based automated driving and training label generation from map data does not exist. Furthermore, feeding map perception models with map data as part of the input during real-time inference is not addressed by the research community. In order to fill this gap, we presentlanelet2_ml_converter, an integrated extension to the HD map framework Lanelet2, widely used in automated driving systems by academia and industry. With this addition Lanelet2 unifies map based automated driving, machine learning inference and training, all from a single source of map data and format. Requirements for a unified framework are analyzed and the implementation of these requirements is described. The usability of labels in state of the art machine learning is demonstrated with application examples from the field of map perception. The source code is available embedded in the Lanelet2 framework under https://github.com/fzi-forschungszentrum-informatik/Lanelet2/tree/feature_ml_converter

翻译：直接利用高精地图作为机器学习任务的训练数据已获得广泛关注并展现出显著成效，例如在地图感知领域。尽管如此，目前尚缺乏能够同时支持基于地图的自动驾驶全流程及从地图数据生成训练标签的标准化高精地图框架。此外，研究界尚未解决在实时推理过程中将地图数据作为输入馈送给地图感知模型的问题。为填补这一空白，我们提出lanelet2_ml_converter——这是对学术界与工业界广泛使用的自动驾驶高精地图框架Lanelet2的集成扩展。通过此扩展，Lanelet2实现了基于地图的自动驾驶、机器学习推理与训练的统一，全部源自单一的地图数据源与格式。本文分析了统一框架的需求，阐述了相应需求的实现方案，并通过地图感知领域的应用示例论证了所生成标签在尖端机器学习中的实用性。源代码已集成于Lanelet2框架中，可通过https://github.com/fzi-forschungszentrum-informatik/Lanelet2/tree/feature_ml_converter 获取。