  Vision-centric Bird's-Eye View (BEV) representation is essential for
autonomous driving systems (ADS). Multi-frame temporal fusion which leverages
historical information has been demonstrated to provide more comprehensive
perception results. While most research focuses on ego-centric maps of fixed
settings, long-range local map generation remains less explored. This work
outlines a new paradigm, named NeMO, for generating local maps through the
utilization of a readable and writable big map, a learning-based fusion module,
and an interaction mechanism between the two. With an assumption that the
feature distribution of all BEV grids follows an identical pattern, we adopt a
shared-weight neural network for all grids to update the big map. This paradigm
supports the fusion of longer time series and the generation of long-range BEV
local maps. Furthermore, we release BDD-Map, a BDD100K-based dataset
incorporating map element annotations, including lane lines, boundaries, and
pedestrian crossing. Experiments on the NuScenes and BDD-Map datasets
demonstrate that NeMO outperforms state-of-the-art map segmentation methods. We
also provide a new scene-level BEV map evaluation setting along with the
corresponding baseline for a more comprehensive comparison.
