Go to ICLR 2024 Conference homepageP-MapNet: Far-seeing Map Constructer Enhanced by both SDMap and HDMap Priors
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 construction algorithms, but the performance of these algorithms 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 alternative 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. Both priors lead to performance improvements, especially in farther regions. We benchmark on the nuScenes dataset, demonstrating 13.46% mIoU margin over the baseline. Codes and models will be publicly available.