  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.
