Vectorized high-definition map online construction has garnered considerable attention in the field of autonomous driving research. Most existing approaches model changeable map elements using a fixed number of points, or predict local maps in a two-stage autoregressive manner, which may miss essential details and lead to error accumulation. Towards precise map element learning, we propose a simple yet effective architecture named PivotNet, which adopts unified pivot-based map representations and is formulated as a direct set prediction paradigm. Concretely, we first propose a novel Point-to-Line Mask module to encode both the subordinate and geometrical point-line priors in the network. Then, a well-designed Pivot Dynamic Matching module is proposed to model the topology in dynamic point sequences by introducing the concept of sequence matching. Furthermore, to supervise the position and topology of the vectorized point predictions, we propose a Dynamic Vectorized Sequence loss. Extensive experiments and ablations show that PivotNet is remarkably superior to other SOTAs by 5.9 mAP at least. The code will be available soon.

翻译：矢量化高精地图在线构建在自动驾驶研究领域引起了广泛关注。现有方法大多采用固定数量点建模变化的地图元素，或以两阶段自回归方式预测局部地图，这可能导致关键细节丢失并引发误差累积。为精确学习地图元素，我们提出一种简洁而高效的架构PivotNet，该架构采用统一的基于枢轴的地图表示，并构建为直接集合预测范式。具体而言，我们首先提出一种新颖的点到线掩码模块，用于在网络中编码从属和几何点线先验。随后，设计了一种精巧的枢轴动态匹配模块，通过引入序列匹配概念来建模动态点序列中的拓扑结构。此外，为监督矢量化点预测的位置与拓扑，我们提出动态矢量化序列损失。大量实验与消融研究表明，PivotNet相比其他最优方法至少提升5.9 mAP。代码将不久后公开。