论文标题
vectormapnet：端到端矢量化的高清图学习
VectorMapNet: End-to-end Vectorized HD Map Learning
论文作者
论文摘要
自动驾驶系统需要高清（HD）语义图才能在城市道路上浏览。现有的解决方案通过离线手动注释来解决语义映射问题，这遭受了严重的可扩展性问题。最近的基于学习的方法产生密集的栅格分割预测来构建图。但是，这些预测不包括各个地图元素的实例信息，需要启发式后处理才能获得矢量化地图。为了应对这些挑战，我们引入了端到端矢量化的高清图学习管道，称为vectormapnet。 Vectormapnet进行了载板传感器观测，并预测了鸟眼的稀疏聚集线。该管道可以明确对地图元素之间的空间关系进行建模，并生成对下游自主驾驶任务友好的矢量图。广泛的实验表明，vectormapnet在Nuscenes和Argoverse2数据集上都能达到强大的地图学习性能，从而超过了14.2 MAP和14.6map的先前最新方法。从定性上讲，Vectormapnet能够生成全面的地图并捕获道路几何形状的细粒细节。据我们所知，VectorMapnet是针对端到端矢量化地图从车载观测中学习而设计的第一部作品。我们的项目网站可在\ url {https://tsinghua-mars-lab.github.io/vectormapnet/}获得。
Autonomous driving systems require High-Definition (HD) semantic maps to navigate around urban roads. Existing solutions approach the semantic mapping problem by offline manual annotation, which suffers from serious scalability issues. Recent learning-based methods produce dense rasterized segmentation predictions to construct maps. However, these predictions do not include instance information of individual map elements and require heuristic post-processing to obtain vectorized maps. To tackle these challenges, we introduce an end-to-end vectorized HD map learning pipeline, termed VectorMapNet. VectorMapNet takes onboard sensor observations and predicts a sparse set of polylines in the bird's-eye view. This pipeline can explicitly model the spatial relation between map elements and generate vectorized maps that are friendly to downstream autonomous driving tasks. Extensive experiments show that VectorMapNet achieve strong map learning performance on both nuScenes and Argoverse2 dataset, surpassing previous state-of-the-art methods by 14.2 mAP and 14.6mAP. Qualitatively, VectorMapNet is capable of generating comprehensive maps and capturing fine-grained details of road geometry. To the best of our knowledge, VectorMapNet is the first work designed towards end-to-end vectorized map learning from onboard observations. Our project website is available at \url{https://tsinghua-mars-lab.github.io/vectormapnet/}.
