针对浓雾场景，分密度训练专用感知模型，显著提升自动驾驶识别率。
Enhancing Visual Perception in Foggy Conditions via Multiclass Fog Density Modeling
- 按五级雾密度分别训练模型，避免单一模型适应性差
- 极浓雾下召回率从7.6%提升至23.2%，增15.6个百分点
- 适合需要高鲁棒性感知的自动驾驶系统研发者
自动驾驶系统在过去十年快速发展，但在恶劣天气下的感知仍面临重大挑战，尤其在浓雾条件下。本文基于Waymo数据集生成的合成雾数据，研究雾感知问题。通过迭代学习方法生成深度图，构建了五类雾密度：无雾、轻雾、中雾、重雾和极重雾。不采用统一模型训练，而是为每种雾密度级别分别训练感知模型。实验表明，针对性训练显著提升严重雾天表现。尤其在极重雾类别中，召回率从0.076提升至0.232，绝对提升15.6个百分点。结果表明，在复杂能见度条件下，部署多个专用模型比单一通用模型更能增强自动驾驶系统的感知鲁棒性。未来工作将扩展该策略至激光雷达与雷达等其他传感模态，并评估其在多样天气场景中的泛化能力。
原文摘要 · Abstract (English)
Autonomous driving (AD) systems have advanced rapidly over the past decade; however, robust perception under adverse weather conditions remains a major challenge, particularly in dense fog. In this work, we investigate fog-aware perception using synthetically generated fog data derived from the Waymo dataset. To support fog simulation, depth images are generated using an iterative learning approach. We consider five fog-density levels: clear, light fog, moderate fog, heavy fog, and very heavy fog. Instead of training a single unified model across all conditions, we train separate perception models for each fog-density level. Experimental results show that density-specific training improves performance in severe fog conditions. In particular, for the very heavy fog class, recall improves from 0.076 to 0.232, corresponding to an absolute gain of 15.6 percentage points. These findings suggest that deploying multiple specialized models, rather than a single general-purpose model, can improve perception robustness for autonomous vehicles under challenging visibility conditions. Future work will extend this strategy to additional sensing modalities, including LiDAR and radar, and evaluate generalization across diverse weather scenarios.
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