Using Collision Momentum in Deep Reinforcement Learning Based
  Adversarial Pedestrian Modeling
Dianwei Chen Ekim Yurtsever Keith Redmill Umit Ozguner
Recent research in pedestrian simulation often aims to develop realistic
behaviors in various situations, but it is challenging for existing algorithms
to generate behaviors that identify weaknesses in automated vehicles'
performance in extreme and unlikely scenarios and edge cases. To address this,
specialized pedestrian behavior algorithms are needed. Current research focuses
on realistic trajectories using social force models and reinforcement learning
based models. However, we propose a reinforcement learning algorithm that
specifically targets collisions and better uncovers unique failure modes of
automated vehicle controllers. Our algorithm is efficient and generates more
severe collisions, allowing for the identification and correction of weaknesses
in autonomous driving algorithms in complex and varied scenarios.