  Vector maps are essential in autonomous driving for tasks like localization
and planning, yet their creation and maintenance are notably costly. While
recent advances in online vector map generation for autonomous vehicles are
promising, current models lack adaptability to different sensor configurations.
They tend to overfit to specific sensor poses, leading to decreased performance
and higher retraining costs. This limitation hampers their practical use in
real-world applications. In response to this challenge, we propose a modular
pipeline for vector map generation with improved generalization to sensor
configurations. The pipeline leverages probabilistic semantic mapping to
generate a bird's-eye-view (BEV) semantic map as an intermediate
representation. This intermediate representation is then converted to a vector
map using the MapTRv2 decoder. By adopting a BEV semantic map robust to
different sensor configurations, our proposed approach significantly improves
the generalization performance. We evaluate the model on datasets with sensor
configurations not used during training. Our evaluation sets includes larger
public datasets, and smaller scale private data collected on our platform. Our
model generalizes significantly better than the state-of-the-art methods.
