  Predictive uncertainty estimation is essential for safe deployment of Deep
Neural Networks in real-world autonomous systems. However, disentangling the
different types and sources of uncertainty is non trivial for most datasets,
especially since there is no ground truth for uncertainty. In addition, while
adverse weather conditions of varying intensities can disrupt neural network
predictions, they are usually under-represented in both training and test sets
in public datasets.We attempt to mitigate these setbacks and introduce the MUAD
dataset (Multiple Uncertainties for Autonomous Driving), consisting of 10,413
realistic synthetic images with diverse adverse weather conditions (night, fog,
rain, snow), out-of-distribution objects, and annotations for semantic
segmentation, depth estimation, object, and instance detection. MUAD allows to
better assess the impact of different sources of uncertainty on model
performance. We conduct a thorough experimental study of this impact on several
baseline Deep Neural Networks across multiple tasks, and release our dataset to
allow researchers to benchmark their algorithm methodically in adverse
conditions. More visualizations and the download link for MUAD are available at
https://muad-dataset.github.io/.
