LaneDiffusion++: Feature-Level Generative Priors for Structured Lane Graph Learning

Learning structured lane topology from visual observations is challenging due to structural ambiguity, occlusions, and incomplete lane markings in complex urban environments. Most existing centerline graph learning methods rely on deterministic regression, which implicitly assumes a single optimal topology for each scene and therefore struggles under uncertain visual conditions. In this work, we propose LaneDiffusion++, a unified feature-level generative framework that reformulates centerline graph learning as the conditional restoration of prior-enhanced Bird's Eye View (BEV) features, rather than directly generating vectorized graphs in a discrete output space. Specifically, LaneDiffusion++ first injects structural lane priors into intermediate BEV features through a Lane Prior Injection Module (LPIM), and then employs a Lane Prior Diffusion Module (LPDM) to perform conditional generative modeling in feature space, progressively restoring topology-aware representations while capturing multiple plausible lane configurations. To further exploit the stochasticity of diffusion inference, we introduce a Lane Hypothesis Aggregation Module (LHAM), which explicitly consolidates multiple diffusion-generated feature hypotheses into a stable topology-aware representation before decoding. Extensive experiments on large-scale benchmarks demonstrate consistent improvements in both geometric precision and topological consistency over strong deterministic and generative baselines. These results suggest that feature-level generative modeling, together with uncertainty-aware hypothesis aggregation, provides an effective paradigm for structured topology reasoning in autonomous perception.