  Level-5 driving automation requires a robust visual perception system that
can parse input images under any condition. However, existing driving datasets
for dense semantic perception are either dominated by images captured under
normal conditions or are small in scale. To address this, we introduce ACDC,
the Adverse Conditions Dataset with Correspondences for training and testing
methods for diverse semantic perception tasks on adverse visual conditions.
ACDC consists of a large set of 8012 images, half of which (4006) are equally
distributed between four common adverse conditions: fog, nighttime, rain, and
snow. Each adverse-condition image comes with a high-quality pixel-level
panoptic annotation, a corresponding image of the same scene under normal
conditions, and a binary mask that distinguishes between intra-image regions of
clear and uncertain semantic content. 1503 of the corresponding
normal-condition images feature panoptic annotations, raising the total
annotated images to 5509. ACDC supports the standard tasks of semantic
segmentation, object detection, instance segmentation, and panoptic
segmentation, as well as the newly introduced uncertainty-aware semantic
segmentation. A detailed empirical study demonstrates the challenges that the
adverse domains of ACDC pose to state-of-the-art supervised and unsupervised
approaches and indicates the value of our dataset in steering future progress
in the field. Our dataset and benchmark are publicly available at
https://acdc.vision.ee.ethz.ch
