  In this paper, we investigate the robustness of traffic sign recognition
algorithms under challenging conditions. Existing datasets are limited in terms
of their size and challenging condition coverage, which motivated us to
generate the Challenging Unreal and Real Environments for Traffic Sign
Recognition (CURE-TSR) dataset. It includes more than two million traffic sign
images that are based on real-world and simulator data. We benchmark the
performance of existing solutions in real-world scenarios and analyze the
performance variation with respect to challenging conditions. We show that
challenging conditions can decrease the performance of baseline methods
significantly, especially if these challenging conditions result in loss or
misplacement of spatial information. We also investigate the effect of data
augmentation and show that utilization of simulator data along with real-world
data enhance the average recognition performance in real-world scenarios. The
dataset is publicly available at https://ghassanalregib.com/cure-tsr/.
