HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps
This addresses a data scarcity issue for autonomous driving systems, enabling better testing and generalization, though it is incremental as it builds on existing generative models.
The paper tackles the problem of limited real-world high-definition (HD) map data for autonomous driving testing by introducing a new task to generate HD maps, proposing HDMapGen, a hierarchical graph generative model that outperforms baselines on datasets like Argoverse.
High Definition (HD) maps are maps with precise definitions of road lanes with rich semantics of the traffic rules. They are critical for several key stages in an autonomous driving system, including motion forecasting and planning. However, there are only a small amount of real-world road topologies and geometries, which significantly limits our ability to test out the self-driving stack to generalize onto new unseen scenarios. To address this issue, we introduce a new challenging task to generate HD maps. In this work, we explore several autoregressive models using different data representations, including sequence, plain graph, and hierarchical graph. We propose HDMapGen, a hierarchical graph generation model capable of producing high-quality and diverse HD maps through a coarse-to-fine approach. Experiments on the Argoverse dataset and an in-house dataset show that HDMapGen significantly outperforms baseline methods. Additionally, we demonstrate that HDMapGen achieves high scalability and efficiency.