通过多类雾密度建模增强雾天条件下的视觉感知
Enhancing Visual Perception in Foggy Conditions via Multiclass Fog Density Modeling
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中文总结 AI 辅助
本研究针对自动驾驶雾天感知难题，基于Waymo数据集合成雾数据，按五类雾密度训练专用感知模型，使特浓雾召回率提升15.6个百分点，验证了多专用模型策略的有效性。
中文摘要 AI 辅助
自动驾驶（AD）系统在过去十年发展迅速，但恶劣天气条件下的鲁棒感知仍是重大挑战，尤其是浓雾场景。本研究基于Waymo数据集生成的合成雾数据，开展感知研究；为支持雾模拟，采用迭代学习方法生成深度图像。我们考虑五类雾密度等级：无雾、轻雾、中雾、浓雾、特浓雾。未针对所有条件训练单一统一模型，而是为每个雾密度等级单独训练感知模型。实验结果表明，针对特定密度的训练可提升浓雾条件下的性能，其中特浓雾类别的召回率从0.076提升至0.232，绝对提升15.6个百分点。这些结果表明，部署多个专用模型而非单一通用模型，可提升自动驾驶车辆在恶劣能见度条件下的感知鲁棒性。未来工作将把该策略扩展至更多传感模态，包括LiDAR和雷达，并评估其在不同天气场景下的泛化能力。
英文摘要
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.
发表机构
- University of Applied Sciences, Aschaffenburg（阿沙芬堡应用科学大学）
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