Exploring Real World Map Change Generalization of Prior-Informed HD Map Prediction Models

Samuel M. Bateman, Ning Xu, H. Charles Zhao, Yael Ben Shalom, Vince Gong, Greg Long, Will Maddern; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4568-4578

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robust online mapping systems using low quality map priors with synthetic perturbations in an attempt to simulate out-of-date HD map priors.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.