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：向量化高清地图的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可变地图元素，或采用两阶段自回归方式预测局部地图，这可能导致关键细节缺失和误差累积。为了实现精确的地图元素学习，我们提出了一种简洁高效的架构PivotNet，该架构采用统一的基于枢轴的地图表示，并采用直接集预测范式。具体而言，我们首先提出一种新颖的点-线掩码模块，用于在网络中编码从属和几何点-线先验知识。随后，我们设计了精心设计的枢轴动态匹配模块，通过引入序列匹配概念对动态点序列中的拓扑结构进行建模。此外，为了监督向量化点预测的位置和拓扑结构，我们提出了一种动态向量化序列损失函数。大量实验和消融研究表明，PivotNet相比其他最先进方法至少提升5.9 mAP。代码即将开源。