LaneDAG: Automatic HD Map Topology Generator Based on Geometry and Attention Fusion Mechanism

In high-definition maps (HD maps), the road lane centerline and lane topology graph play essential roles in navigation, planning, and decision-making. Existing research focusing on extracting physical infrastructure, such as lane boundaries, has made significant progress. But lane centerline detection and topology reasoning still remains challenging due to the severe overlapping centerlines and complicated topology. To tackle these challenges, we introduce an automatic lane topology extraction method for HD maps, termed LaneDAG, which extracts vectorized centerlines and their topology from prebuilt lane lines and road boundaries in HD maps. It formulates centerline extraction as a set prediction problem and lane topology prediction as a directed acyclic graph (DAG) construction problem. A novel mechanism that fusing geometric and attention-based features in the DAG is proposed to model the topological relationship between centerlines. Experiments conducted on the Argoverse 2 dataset demonstrate the proposed method’s superior performance compared to existing methods, showcasing its capability to extract lane centerlines and topology in HD maps automatically.