Abstract
We present a novel approach SAfE that can identify parts of an outdoor scene that are safe for driving, based on attention models. Our formulation is designed for hazardous weather conditions that can impair the visibility of human drivers as well as autonomous vehicles, increasing the risk of accidents. Our approach is unsupervised and uses domain adaptation, with entropy minimization and attention transfer discriminators, to leverage the large amounts of labeled data corresponding to clear weather conditions. Our attention transfer discriminator uses attention maps from the clear weather image to help the network learn relevant regions to attend to, on the images from the hazardous weather dataset. We conduct experiments on CityScapes simulated datasets depicting various weather conditions such as rain, fog and snow under different intensities, and additionally on Berkeley Deep Drive. Our result show that using attention models improves the standard unsupervised domain adaptation performance by 29.29%. Furthermore, we also compare with unsupervised domain adaptation methods and show an improvement of at least 12.02% (mIoU) over the state-of-the-art.
Abstract (translated)
URL
https://arxiv.org/abs/2012.08939
