  Autonomous vehicle (AV) evaluation has been the subject of increased interest
in recent years both in industry and in academia. This paper focuses on the
development of a novel framework for generating adversarial driving behavior of
background vehicle interfering against the AV to expose effective and rational
risky events. Specifically, the adversarial behavior is learned by a
reinforcement learning (RL) approach incorporated with the cumulative prospect
theory (CPT) which allows representation of human risk cognition. Then, the
extended version of deep deterministic policy gradient (DDPG) technique is
proposed for training the adversarial policy while ensuring training stability
as the CPT action-value function is leveraged. A comparative case study
regarding the cut-in scenario is conducted on a high fidelity
Hardware-in-the-Loop (HiL) platform and the results demonstrate the adversarial
effectiveness to infer the weakness of the tested AV.
