Vectorized high-definition map (HD-map) construction, which focuses on the perception of centimeter-level environmental information, has attracted significant research interest in the autonomous driving community. Most existing approaches first obtain rasterized map with the segmentation-based pipeline and then conduct heavy post-processing for downstream-friendly vectorization. In this paper, by delving into parameterization-based methods, we pioneer a concise and elegant scheme that adopts unified piecewise Bezier curve. In order to vectorize changeful map elements end-to-end, we elaborate a simple yet effective architecture, named Piecewise Bezier HD-map Network (BeMapNet), which is formulated as a direct set prediction paradigm and postprocessing-free. Concretely, we first introduce a novel IPM-PE Align module to inject 3D geometry prior into BEV features through common position encoding in Transformer. Then a well-designed Piecewise Bezier Head is proposed to output the details of each map element, including the coordinate of control points and the segment number of curves. In addition, based on the progressively restoration of Bezier curve, we also present an efficient Point-Curve-Region Loss for supervising more robust and precise HD-map modeling. Extensive comparisons show that our method is remarkably superior to other existing SOTAs by 18.0 mAP at least.

翻译：摘要：矢量化高精地图构建聚焦于厘米级环境信息感知，已引起自动驾驶领域广泛研究兴趣。现有方法多数首先通过基于分割的流程获取栅格化地图，随后进行繁重的后处理以生成便于下游任务使用的矢量化地图。本文深入探究参数化方法，开创性地提出采用统一分段贝塞尔曲线的简洁优雅方案。为实现变化多样的地图要素端到端矢量化，我们精心设计了一种简单高效的架构——分段贝塞尔高精地图网络（BeMapNet），该网络采用直接集合预测范式且无需后处理。具体而言，我们首先引入新型IPM-PE Align模块，通过Transformer中的通用位置编码将3D几何先验注入BEV特征；随后提出精心设计的分段贝塞尔头，输出各地图要素的细节信息，包括控制点坐标与曲线分段数。此外，基于贝塞尔曲线的渐进式重建，我们提出高效的"点-曲线-区域"损失函数，以监督更鲁棒精准的高精地图建模。大量对比实验表明，本方法在平均精度（mAP）上至少超越现有最优方法18.0个点。