Autonomous vehicles are gradually entering city roads today, with the help of high-definition maps (HDMaps). However, the reliance on HDMaps prevents autonomous vehicles from stepping into regions without this expensive digital infrastructure. This fact drives many researchers to study online HDMap generation algorithms, but the performance of these algorithms at far regions is still unsatisfying. We present P-MapNet, in which the letter P highlights the fact that we focus on incorporating map priors to improve model performance. Specifically, we exploit priors in both SDMap and HDMap. On one hand, we extract weakly aligned SDMap from OpenStreetMap, and encode it as an additional conditioning branch. Despite the misalignment challenge, our attention-based architecture adaptively attends to relevant SDMap skeletons and significantly improves performance. On the other hand, we exploit a masked autoencoder to capture the prior distribution of HDMap, which can serve as a refinement module to mitigate occlusions and artifacts. We benchmark on the nuScenes and Argoverse2 datasets. Through comprehensive experiments, we show that: (1) our SDMap prior can improve online map generation performance, using both rasterized (by up to $+18.73$ $\rm mIoU$) and vectorized (by up to $+8.50$ $\rm mAP$) output representations. (2) our HDMap prior can improve map perceptual metrics by up to $6.34\%$. (3) P-MapNet can be switched into different inference modes that covers different regions of the accuracy-efficiency trade-off landscape. (4) P-MapNet is a far-seeing solution that brings larger improvements on longer ranges. Codes and models are publicly available at https://jike5.github.io/P-MapNet.

翻译：当今自动驾驶汽车正逐步驶入城市道路，这得益于高清地图（HDMaps）的辅助。然而，对高清地图的依赖使得自动驾驶汽车难以进入缺乏此类昂贵数字基础设施的区域。这一现状促使众多研究者探索在线高清地图生成算法，但此类算法在远距离区域的表现仍不尽人意。我们提出P-MapNet，其中字母P强调我们专注于融合地图先验以提升模型性能。具体而言，我们同时利用SDMap和HDMap中的先验信息。一方面，我们从OpenStreetMap提取弱对齐的SDMap，并将其编码为额外条件分支。尽管存在对齐挑战，我们的注意力机制架构能够自适应地关注相关SDMap骨架，显著提升性能。另一方面，我们利用掩码自编码器捕捉HDMap的先验分布，该模块可作为细化组件缓解遮挡与伪影问题。我们在nuScenes和Argoverse2数据集上进行基准测试。通过全面实验表明：（1）我们的SDMap先验可提升在线地图生成性能，在栅格化（最高提升$+18.73$ $\rm mIoU$）和向量化（最高提升$+8.50$ $\rm mAP$）输出表示中均有效；（2）HDMap先验可使地图感知指标最高提升$6.34\%$；（3）P-MapNet可切换至不同推理模式，覆盖精度-效率权衡曲线的不同区域；（4）P-MapNet是一种具有远见的解决方案，在更长距离上带来更大性能提升。代码与模型已开源至https://jike5.github.io/P-MapNet。