P-MapNet: Far-seeing Map Generator Enhanced by both SDMap and HDMap Priors
arxiv(2024)
摘要
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 mIoU) and vectorized (by up to +8.50
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.
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