解决视频车道检测在遮挡下的时序不一致问题
SIGMA-Lane: Scale-pyramId Gated MAmba for Temporally Consistent Video Lane Detection
- 用门控机制控制遮挡下信息进入时序记忆
- 在重叠遮挡下保持预测稳定，F1提升3.2%
- 适合自动驾驶中复杂交通场景的车道检测
视频车道检测需保证帧间预测稳定，但严重车辆遮挡会破坏时序线索。在流式递归模型中，受损观测可能污染隐藏状态，导致错误持续传播。现有遮挡感知修正方法通常依赖障碍物掩码作为辅助输入，对状态更新路径保护间接。本文提出SIGMA-Lane，将此失效模式视为基于状态空间模型（SSM）时序建模中的状态污染。在SSM写入和残差融合路径上引入遮挡感知门控，控制当前观测如何进入时序记忆并反馈融合。经坐标一致仿射对齐后，模型结合两条互补路径：用于时序滤波的SSM一致性双门控，以及利用对齐历史先验恢复缺失车道结构的结构空间恢复（SSR）。在VIL-100和OpenLane-V数据集上实验表明，在重度遮挡下显著提升时序稳定性，同时保持竞争性F1与mIoU得分。
原文摘要 · Abstract (English)
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.
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