Recently, there has been a significant escalation in both academic and industrial commitment towards the development of autonomous driving systems (ADSs). A number of simulation testing approaches have been proposed to generate diverse driving scenarios for ADS testing. However, scenarios generated by these previous approaches are static and lack interactions between the EGO vehicle and the NPC vehicles, resulting in a large amount of time on average to find violation scenarios. Besides, a large number of the violations they found are caused by aggressive behaviors of NPC vehicles, revealing none bugs of ADS. In this work, we propose the concept of adversarial NPC vehicles and introduce AdvFuzz, a novel simulation testing approach, to generate adversarial scenarios on main lanes (e.g., urban roads and highways). AdvFuzz allows NPC vehicles to dynamically interact with the EGO vehicle and regulates the behaviors of NPC vehicles, finding more violation scenarios caused by the EGO vehicle more quickly. We compare AdvFuzz with a random approach and three state-of-the-art scenario-based testing approaches. Our experiments demonstrate that AdvFuzz can generate 198.34% more violation scenarios compared to the other four approaches in 12 hours and increase the proportion of violations caused by the EGO vehicle to 87.04%, which is more than 7 times that of other approaches. Additionally, AdvFuzz is at least 92.21% faster in finding one violation caused by the EGO vehicle than that of the other approaches.

翻译：近年来，学术界和工业界对自动驾驶系统（ADS）的研发投入显著增加。已有多种仿真测试方法被提出，用于生成多样化的驾驶场景以测试ADS。然而，这些现有方法生成的场景是静态的，缺乏EGO车辆与NPC车辆之间的交互，导致平均需要大量时间才能发现违规场景。此外，它们发现的违规行为中有大量是由NPC车辆的激进行为引起的，并未揭示ADS的缺陷。在本工作中，我们提出了对抗性NPC车辆的概念，并介绍了AdvFuzz——一种新颖的仿真测试方法，用于在主车道（如城市道路和高速公路）上生成对抗性场景。AdvFuzz允许NPC车辆与EGO车辆动态交互，并规范NPC车辆的行为，从而更快地发现更多由EGO车辆引发的违规场景。我们将AdvFuzz与一种随机方法和三种最先进的基于场景的测试方法进行了比较。实验结果表明，在12小时内，AdvFuzz生成的违规场景比其他四种方法多198.34%，并将由EGO车辆引发的违规比例提高至87.04%，是其他方法的7倍以上。此外，AdvFuzz在发现一个由EGO车辆引发的违规场景时，速度至少比其他方法快92.21%。