  Image segmentation is a common and challenging task in autonomous driving.
Availability of sufficient pixel-level annotations for the training data is a
hurdle. Active learning helps learning from small amounts of data by suggesting
the most promising samples for labeling. In this work, we propose a new
pool-based method for active learning, which proposes promising patches
extracted from full image, in each acquisition step. The problem is framed in
an exploration-exploitation framework by combining an embedding based on
Uniform Manifold Approximation to model representativeness with entropy as
uncertainty measure to model informativeness. We applied our proposed method to
the autonomous driving datasets CamVid and Cityscapes and performed a
quantitative comparison with state-of-the-art baselines. We find that our
active learning method achieves better performance compared to previous
methods.
