  Simulation is an indispensable tool in the development and testing of
autonomous vehicles (AVs), offering an efficient and safe alternative to road
testing by allowing the exploration of a wide range of scenarios. Despite its
advantages, a significant challenge within simulation-based testing is the
generation of safety-critical scenarios, which are essential to ensure that AVs
can handle rare but potentially fatal situations. This paper addresses this
challenge by introducing a novel generative framework, CaDRE, which is
specifically designed for generating diverse and controllable safety-critical
scenarios using real-world trajectories. Our approach optimizes for both the
quality and diversity of scenarios by employing a unique formulation and
algorithm that integrates real-world data, domain knowledge, and black-box
optimization techniques. We validate the effectiveness of our framework through
extensive testing in three representative types of traffic scenarios. The
results demonstrate superior performance in generating diverse and high-quality
scenarios with greater sample efficiency than existing reinforcement learning
and sampling-based methods.
