  Advancements in Autonomous Driving Systems (ADS) have brought significant
benefits, but also raised concerns regarding their safety. Virtual tests are
common practices to ensure the safety of ADS because they are more efficient
and safer compared to field operational tests. However, capturing the complex
dynamics of real-world driving environments and effectively generating risk
scenarios for testing is challenging. In this paper, we propose a novel
paradigm shift towards utilizing Causal Bayesian Networks (CBN) for scenario
generation in ADS. The CBN is built and validated using Maryland accident data,
providing a deeper insight into the myriad factors influencing autonomous
driving behaviors. Based on the constructed CBN, we propose an algorithm that
significantly enhances the process of risk scenario generation, leading to more
effective and safer ADS. An end-to-end testing framework for ADS is established
utilizing the CARLA simulator. Through experiments, we successfully generated
89 high-risk scenarios from 5 seed scenarios, outperforming baseline methods in
terms of time and iterations required.
