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

IDD-AW: A Benchmark for Safe and Robust Segmentation of Drive Scenes in Unstructured Traffic and Adverse Weather