  Building and maintaining High-Definition (HD) maps represents a large barrier
to autonomous vehicle deployment. This, along with advances in modern online
map detection models, has sparked renewed interest in the online mapping
problem. However, effectively predicting online maps at a high enough quality
to enable safe, driverless deployments remains a significant challenge. Recent
work on these models proposes training robust online mapping systems using low
quality map priors with synthetic perturbations in an attempt to simulate
out-of-date HD map priors. In this paper, we investigate how models trained on
these synthetically perturbed map priors generalize to performance on
deployment-scale, real world map changes. We present a large-scale experimental
study to determine which synthetic perturbations are most useful in
generalizing to real world HD map changes, evaluated using multiple years of
real-world autonomous driving data. We show there is still a substantial
sim2real gap between synthetic prior perturbations and observed real-world
changes, which limits the utility of current prior-informed HD map prediction
models.
