  High-definition (HD) semantic maps are crucial in enabling autonomous
vehicles to navigate urban environments. The traditional method of creating
offline HD maps involves labor-intensive manual annotation processes, which are
not only costly but also insufficient for timely updates. Recent studies have
proposed an alternative approach that generates local maps using online sensor
observations. However, this approach is limited by the sensor's perception
range and its susceptibility to occlusions. In this study, we propose Neural
Map Prior (NMP), a neural representation of global maps. This representation
automatically updates itself and improves the performance of local map
inference. Specifically, we utilize two approaches to achieve this. Firstly, to
integrate a strong map prior into local map inference, we apply
cross-attention, a mechanism that dynamically identifies correlations between
current and prior features. Secondly, to update the global neural map prior, we
utilize a learning-based fusion module that guides the network in fusing
features from previous traversals. Our experimental results, based on the
nuScenes dataset, demonstrate that our framework is highly compatible with
various map segmentation and detection architectures. It significantly improves
map prediction performance, even in challenging weather conditions and
situations with a longer perception range. To the best of our knowledge, this
is the first learning-based system for creating a global map prior.
