  We present a novel approach for unsupervised road segmentation in adverse
weather conditions such as rain or fog. This includes a new algorithm for
source-free domain adaptation (SFDA) using self-supervised learning. Moreover,
our approach uses several techniques to address various challenges in SFDA and
improve performance, including online generation of pseudo-labels and
self-attention as well as use of curriculum learning, entropy minimization and
model distillation. We have evaluated the performance on $6$ datasets
corresponding to real and synthetic adverse weather conditions. Our method
outperforms all prior works on unsupervised road segmentation and SFDA by at
least 10.26%, and improves the training time by 18-180x. Moreover, our
self-supervised algorithm exhibits similar accuracy performance in terms of
mIOU score as compared to prior supervised methods.
