发表机构
College of Computer Science and Technology, Zhejiang University of Technology（浙江工业大学计算机科学与技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SIGMA-Lane通过在SSM路径添加感知遮挡门控，结合双门控时间滤波与SSR，提升了视频车道检测在严重遮挡下的时间稳定性，在VIL-100等数据集上取得了有竞争力的指标。
AI 中文摘要
视频车道检测需要在帧间保持稳定的预测，但严重的车辆遮挡会破坏时间线索。在流式循环模型中，受损的观测值可能会进入隐藏状态，产生持续到后续帧的错误。现有的感知遮挡优化通常将障碍物掩码作为辅助输入，因此状态更新路径仅受到间接保护。我们提出SIGMA-Lane，将这种失效模式视为基于状态空间模型（SSM）的时间建模中的状态污染。SIGMA-Lane在SSM写入和残差融合路径上放置感知遮挡门控，控制当前观测值如何进入时间记忆，并在时间传播后融合返回。经过坐标一致的仿射对齐后，该模型结合两条互补路径：用于时间滤波的SSM一致双门控，以及用于从对齐的历史先验中恢复缺失车道结构的结构空间检索（SSR）。在VIL-100和OpenLane-V上进行的实验表明，该模型在严重遮挡下的时间稳定性有所提升，且具有有竞争力的F1和mIoU分数。
英文摘要
Video lane detection requires predictions that remain stable across frames, yet severe vehicle occlusions can break temporal cues. In streaming recurrent models, corrupted observations may enter the hidden state and produce errors that persist into later frames. Existing occlusion-aware refinements usually provide obstacle masks as auxiliary inputs, so the state-update path is only indirectly protected. We propose SIGMA-Lane, which treats this failure mode as state contamination in State Space Model (SSM)-based temporal modeling. SIGMA-Lane places occlusion-aware gates on the SSM write and residual-fusion paths, controlling how current observations enter temporal memory and are fused back after temporal propagation. After coordinate-consistent affine alignment, the model combines two complementary paths: SSM-consistent dual-gating for temporal filtering and Structural Spatial Retrieval (SSR) for recovering missing lane structure from aligned historical priors. Experiments on VIL-100 and OpenLane-V show improved temporal stability under heavy occlusion, with competitive F1 and mIoU scores.
Commentsaccepted by ECCV 2026
