  Driving systems often rely on high-definition (HD) maps for precise
environmental information, which is crucial for planning and navigation. While
current HD map constructors perform well under ideal conditions, their
resilience to real-world challenges, \eg, adverse weather and sensor failures,
is not well understood, raising safety concerns. This work introduces MapBench,
the first comprehensive benchmark designed to evaluate the robustness of HD map
construction methods against various sensor corruptions. Our benchmark
encompasses a total of 29 types of corruptions that occur from cameras and
LiDAR sensors. Extensive evaluations across 31 HD map constructors reveal
significant performance degradation of existing methods under adverse weather
conditions and sensor failures, underscoring critical safety concerns. We
identify effective strategies for enhancing robustness, including innovative
approaches that leverage multi-modal fusion, advanced data augmentation, and
architectural techniques. These insights provide a pathway for developing more
reliable HD map construction methods, which are essential for the advancement
of autonomous driving technology. The benchmark toolkit and affiliated code and
model checkpoints have been made publicly accessible.
