  Autonomous driving (AD) datasets have progressively grown in size in the past
few years to enable better deep representation learning. Active learning (AL)
has re-gained attention recently to address reduction of annotation costs and
dataset size. AL has remained relatively unexplored for AD datasets, especially
on point cloud data from LiDARs. This paper performs a principled evaluation of
AL based dataset distillation on (1/4th) of the large Semantic-KITTI dataset.
Further on, the gains in model performance due to data augmentation (DA) are
demonstrated across different subsets of the AL loop. We also demonstrate how
DA improves the selection of informative samples to annotate. We observe that
data augmentation achieves full dataset accuracy using only 60\% of samples
from the selected dataset configuration. This provides faster training time and
subsequent gains in annotation costs.
