  For safety of autonomous driving, vehicles need to be able to drive under
various lighting, weather, and visibility conditions in different environments.
These external and environmental factors, along with internal factors
associated with sensors, can pose significant challenges to perceptual data
processing, hence affecting the decision-making and control of the vehicle. In
this work, we address this critical issue by introducing a framework for
analyzing robustness of the learning algorithm w.r.t varying quality in the
image input for autonomous driving. Using the results of sensitivity analysis,
we further propose an algorithm to improve the overall performance of the task
of "learning to steer". The results show that our approach is able to enhance
the learning outcomes up to 48%. A comparative study drawn between our approach
and other related techniques, such as data augmentation and adversarial
training, confirms the effectiveness of our algorithm as a way to improve the
robustness and generalization of neural network training for autonomous
driving.
