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.

翻译：自动驾驶系统（ADS）的进步带来了显著效益，同时也引发了对其安全性的担忧。虚拟测试因其比实地操作测试更高效、更安全，已成为确保ADS安全性的常用方法。然而，捕捉真实驾驶环境的复杂动态并有效生成用于测试的风险场景仍具挑战性。本文提出一种利用因果贝叶斯网络（CBN）进行ADS场景生成的新范式。该CBN基于马里兰州事故数据构建并验证，为理解影响自动驾驶行为的众多因素提供了更深入的洞察。基于所构建的CBN，我们提出一种算法，显著改进了风险场景生成过程，从而实现了更高效、更安全的ADS。利用CARLA模拟器建立了一个端到端的ADS测试框架。通过实验，我们成功从5个种子场景生成了89个高风险场景，在所需时间和迭代次数方面均优于基线方法。