  Testing and evaluation is a crucial step in the development and deployment of
Connected and Automated Vehicles (CAVs). To comprehensively evaluate the
performance of CAVs, it is of necessity to test the CAVs in safety-critical
scenarios, which rarely happen in naturalistic driving environment. Therefore,
how to purposely and systematically generate these corner cases becomes an
important problem. Most existing studies focus on generating adversarial
examples for perception systems of CAVs, whereas limited efforts have been put
on the decision-making systems, which is the highlight of this paper. As the
CAVs need to interact with numerous background vehicles (BVs) for a long
duration, variables that define the corner cases are usually high dimensional,
which makes the generation a challenging problem. In this paper, a unified
framework is proposed to generate corner cases for the decision-making systems.
To address the challenge brought by high dimensionality, the driving
environment is formulated based on Markov Decision Process, and the deep
reinforcement learning techniques are applied to learn the behavior policy of
BVs. With the learned policy, BVs will behave and interact with the CAVs more
aggressively, resulting in more corner cases. To further analyze the generated
corner cases, the techniques of feature extraction and clustering are utilized.
By selecting representative cases of each cluster and outliers, the valuable
corner cases can be identified from all generated corner cases. Simulation
results of a highway driving environment show that the proposed methods can
effectively generate and identify the valuable corner cases.
