  Automated vehicles' neural networks suffer from overfit, poor
generalizability, and untrained edge cases due to limited data availability.
Researchers synthesize randomized edge-case scenarios to assist in the training
process, though simulation introduces potential for overfit to latent rules and
features. Automating worst-case scenario generation could yield informative
data for improving self driving. To this end, we introduce a "Physically
Adversarial Intelligent Network" (PAIN), wherein self-driving vehicles interact
aggressively in the CARLA simulation environment. We train two agents, a
protagonist and an adversary, using dueling double deep Q networks (DDDQNs)
with prioritized experience replay. The coupled networks alternately
seek-to-collide and to avoid collisions such that the "defensive" avoidance
algorithm increases the mean-time-to-failure and distance traveled under
non-hostile operating conditions. The trained protagonist becomes more
resilient to environmental uncertainty and less prone to corner case failures
resulting in collisions than the agent trained without an adversary.
