We propose a novel end-to-end pipeline for online long-range vectorized high-definition (HD) map construction using on-board camera sensors. The vectorized representation of HD maps, employing polylines and polygons to represent map elements, is widely used by downstream tasks. However, previous schemes designed with reference to dynamic object detection overlook the structural constraints within linear map elements, resulting in performance degradation in long-range scenarios. In this paper, we exploit the properties of map elements to improve the performance of map construction. We extract more accurate bird's eye view (BEV) features guided by their linear structure, and then propose a hierarchical sparse map representation to further leverage the scalability of vectorized map elements and design a progressive decoding mechanism and a supervision strategy based on this representation. Our approach, ScalableMap, demonstrates superior performance on the nuScenes dataset, especially in long-range scenarios, surpassing previous state-of-the-art model by 6.5 mAP while achieving 18.3 FPS. Code is available at https://github.com/jingy1yu/ScalableMap.

提出了一种新颖的端到端流程，使用车载摄像头传感器实现在线长距离矢量化高清地图构建。通过提取线性结构来改进地图构建的性能，并设计了一种渐进解码机制和基于此表示的监督策略，该方法在nuScenes数据集上展现出优越性能，特别是在长距离场景中，超过了以往的最新模型6.5 mAP，同时达到18.3 FPS。

可扩展地图：在线远程矢量化高清地图构建的可扩展地图学习