Large-scale deployment of fully autonomous vehicles requires a very high degree of robustness to unstructured traffic, and weather conditions, and should prevent unsafe mispredictions. While there are several datasets and benchmarks focusing on segmentation for drive scenes, they are not specifically focused on safety and robustness issues. We introduce the IDD-AW dataset, which provides 5000 pairs of high-quality images with pixel-level annotations, captured under rain, fog, low light, and snow in unstructured driving conditions. As compared to other adverse weather datasets, we provide i.) more annotated images, ii.) paired Near-Infrared (NIR) image for each frame, iii.) larger label set with a 4-level label hierarchy to capture unstructured traffic conditions. We benchmark state-of-the-art models for semantic segmentation in IDD-AW. We also propose a new metric called ''Safe mean Intersection over Union (Safe mIoU)'' for hierarchical datasets which penalizes dangerous mispredictions that are not captured in the traditional definition of mean Intersection over Union (mIoU). The results show that IDD-AW is one of the most challenging datasets to date for these tasks. The dataset and code will be available here: http://iddaw.github.io.

翻译：全自动驾驶汽车的大规模部署要求其对非结构化交通与天气条件具备极高的鲁棒性，且需避免不安全误预测。现有多个专注驾驶场景分割的数据集与基准方法，但均未专门聚焦安全性与鲁棒性问题。我们提出IDD-AW数据集，提供5000对高质量图像及其像素级标注，图像采集于非结构化驾驶条件下的雨、雾、弱光及雪天场景。相较于其他恶劣天气数据集，我们提供：i）更多标注图像；ii）每帧配对的近红外图像；iii）含4级标签层级结构的更大标签集以捕捉非结构化交通状况。我们基于IDD-AW对最先进的语义分割模型进行基准测试，同时针对层级结构数据集提出名为"安全平均交并比"的新指标，该指标可惩罚传统平均交并比定义中未捕获的危险误预测。实验结果表明，IDD-AW是目前此类任务最具挑战性的数据集之一。数据集与代码将发布于：http://iddaw.github.io。