  Training vision-based Urban Autonomous driving models is a challenging
problem, which is highly researched in recent times. Training such models is a
data-intensive task requiring the storage and processing of vast volumes of
(possibly redundant) driving video data. In this paper, we study the problem of
developing data-efficient autonomous driving systems. In this context, we study
the problem of multi-criteria online video frame subset selection. We study
convex optimization-based solutions and show that they are unable to provide
solutions with high weightage to the loss of selected video frames. We design a
novel convex optimization-based multi-criteria online subset selection
algorithm that uses a thresholded concave function of selection variables. We
also propose and study a submodular optimization-based algorithm. Extensive
experiments using the driving simulator CARLA show that we are able to drop 80%
of the frames while succeeding to complete 100% of the episodes w.r.t. the
model trained on 100% data, in the most difficult task of taking turns. This
results in a training time of less than 30% compared to training on the whole
dataset. We also perform detailed experiments on prediction performances of
various affordances used by the Conditional Affordance Learning (CAL) model and
show that our subset selection improves performance on the crucial affordance
"Relative Angle" during turns.
