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Level 5 autonomy for self-driving cars requires a robust visual perceptionsystem that can parse input images under any visual condition. However,existing semantic segmentation datasets are either dominated by images capturedunder normal conditions or are small in scale. To address this, we introduceACDC, the Adverse Conditions Dataset with Correspondences for training andtesting semantic segmentation methods on adverse visual conditions. ACDCconsists of a large set of 4006 images which are equally distributed betweenfour common adverse conditions: fog, nighttime, rain, and snow. Eachadverse-condition image comes with a high-quality fine pixel-level semanticannotation, a corresponding image of the same scene taken under normalconditions, and a binary mask that distinguishes between intra-image regions ofclear and uncertain semantic content. Thus, ACDC supports both standardsemantic segmentation and the newly introduced uncertainty-aware semanticsegmentation. A detailed empirical study demonstrates the challenges that theadverse domains of ACDC pose to state-of-the-art supervised and unsupervisedapproaches and indicates the value of our dataset in steering future progressin the field. Our dataset and benchmark are publicly available.

ACDC: The Adverse Conditions Dataset with Correspondences for Semantic Driving Scene Understanding