  It is important to build a rigorous verification and validation (V&V) process
to evaluate the safety of highly automated vehicles (HAVs) before their wide
deployment on public roads. In this paper, we propose an interaction-aware
framework for HAV safety evaluation which is suitable for some
highly-interactive driving scenarios including highway merging, roundabout
entering, etc. Contrary to existing approaches where the primary other vehicle
(POV) takes predetermined maneuvers, we model the POV as a game-theoretic
agent. To capture a wide variety of interactions between the POV and the
vehicle under test (VUT), we characterize the interactive behavior using
level-k game theory and social value orientation and train a diverse set of
POVs using reinforcement learning. Moreover, we propose an adaptive test case
sampling scheme based on the Gaussian process regression technique to generate
customized and diverse challenging cases. The highway merging is used as the
example scenario. We found the proposed method is able to capture a wide range
of POV behaviors and achieve better coverage of the failure modes of the VUT
compared with other evaluation approaches.
