Hierarchical Queries for 3D Lane Detection Based on Multi-Frame Point Clouds

3D lane detection based on multi-frame point clouds is a critical task for autonomous driving. The challenge lies in efficiently performing temporal fusion using multiple data frames with incomplete yet complementary contexts. Existing methods either directly concatenate consecutive frames, avoiding intrinsic limitations of the raw data, or fuse entire feature maps, without distinguishing lane-related features from backgrounds. These solutions exhibit room for improvement in both precision and efficiency. In this paper, we propose an end-to-end lane detection network with hierarchical queries, which decodes lane features at different levels in a top-down manner for high-precision localization. This framework can be deployed on multi-frame inputs, as it efficiently achieves lane-related sequence fusion with reduced computational costs and improved inference speed. Specifically, we design semi-parametric lane geometry representations to model lanes as parametric curves and discrete points. Accordingly, hierarchical queries are proposed to focus on two-level lane geometries, including curve queries and point queries. Curve queries capture global structures of lanes projected onto the bird’s-eye-view (BEV) flat ground, while point queries aggregate multi-frame sequences obtained through curve-guided sampling, acquiring comprehensive and reliable point-level features. In the training stage, our proposed curve matching and point localization loss optimizes the detected lane geometries at both levels. Experiments conducted on the self-collected MultiBEV dataset validate that our method outperforms previously published single-frame and multi-frame methods. Codes are released at https://github.com/lrx02/HQNet