SIGMA-Lane: Scale-pyramId Gated MAmba for Temporally Consistent Video Lane Detection

Video lane detection requires predictions that remain sta-ble across frames, yet severe vehicle occlusions can break temporal cues.In streaming recurrent models, corrupted observations may enter thehidden state and produce errors that persist into later frames. Existingocclusion-aware refinements usually provide obstacle masks as auxiliaryinputs, so the state-update path is only indirectly protected. We proposeSIGMA-Lane, which treats this failure mode as state contamination inState Space Model (SSM)-based temporal modeling. SIGMA-Lane placesocclusion-aware gates on the SSM write and residual-fusion paths, con-trolling how current observations enter temporal memory and are fusedback after temporal propagation. After coordinate-consistent affine align-ment, the model combines two complementary paths: SSM-consistentdual-gating for temporal filtering and Structural Spatial Retrieval (SSR)for recovering missing lane structure from aligned historical priors. Ex-periments on VIL-100 and OpenLane-V show improved temporal stabil-ity under heavy occlusion, with competitive F1 and mIoU scores.