Generating Synthetic Deviation Maps for Prior-Enhanced Vectorized HD Map Construction

High-definition (HD) maps are essential for autonomous driving, providing detailed and accurate environmental information. Recent advancements in online vectorized HD map construction have shown great promise, particularly methods that leverage existing maps as prior knowledge to improve performance. However, the robustness of these prior-enhanced methods under varying deviations between the priors and the real world remains a critical concern. This paper introduces a novel framework for generating synthetic maps, which allows for the controllable magnitude of diverse deviations, including geometric distortions, topological errors, and semantic inconsistencies, simulating real-world scenarios where prior maps may be outdated or inaccurate. Furthermore, lane group constraints are designed to avoid positional conflicts when map elements are modified. The synthesis method can overcome the time-consuming challenge of collecting real road changes. We demonstrate the utility of the synthetic deviation maps by incorporating them into the state-of-the-art prior-enhanced construction methods. The results reveal how different types and degrees of deviations affect the prediction accuracy, providing worthwhile insights into their robustness. Overall, this work contributes to a data augmentation and provides a valuable tool for developing more robust and reliable autonomous driving systems. The code is opensource and available at https://github.com/healenrens/Syn-D-maps.