ICCV 2025 | 高德SeqGrowGraph：一种车道图增量式生成新范式

Core Insights - The article presents SeqGrowGraph, an innovative framework for lane graph autoregressive modeling, which addresses the challenges of constructing high-precision lane maps for autonomous driving systems [18] Group 1: Background and Motivation - The construction of local high-precision maps (online mapping) has become a hot topic in the industry, with lane graph generation being a critical component [2] - Current mainstream technical routes for lane graph generation can be categorized into detection-based and generation-based methods [2] Group 2: Methodology - SeqGrowGraph defines the lane graph as a directed graph G=(V, E), where V represents intersections or key topological nodes, and E represents the lane centerlines connecting the nodes [6] - The core method involves a chain of graph expansions, where the graph construction is completed incrementally by introducing new nodes and updating adjacency and geometry matrices [8][10] - The model architecture follows a mainstream Encoder-Decoder structure, utilizing a BEV encoder to extract features and a Transformer decoder for autoregressive sequence generation [10][11] Group 3: Experimental Validation - SeqGrowGraph was comprehensively evaluated on large-scale autonomous driving datasets nuScenes and Argoverse 2, demonstrating superior performance compared to leading methods in the field [13][14] - Quantitative analysis showed that SeqGrowGraph achieved state-of-the-art performance in topology accuracy metrics such as Landmark and Reachability on both standard and challenging dataset partitions [14][15] Group 4: Qualitative Analysis - Visual results highlighted the advantages of SeqGrowGraph, showcasing its ability to generate topologically continuous, structurally complete, and geometrically accurate lane graphs, while effectively merging redundant nodes from real-world map data [16] Group 5: Conclusion - The SeqGrowGraph framework not only aligns more closely with human structured reasoning but also effectively overcomes inherent limitations of existing methods in handling complex topologies, such as loops [18]