With the increasing adoption of autonomous vehicles, ensuring the reliability of autonomous driving systems (ADSs) deployed on autonomous vehicles has become a significant concern. Driving simulators have emerged as crucial platforms for testing autonomous driving systems, offering realistic, dynamic, and configurable environments. However, existing simulation-based ADS testers have largely overlooked the reliability of the simulators, potentially leading to overlooked violation scenarios and subsequent safety security risks during real-world deployment. In our investigations, we identified that collision detectors in simulators could fail to detect and report collisions in certain collision scenarios, referred to as ignored collision scenarios. This paper aims to systematically discover ignored collision scenarios to improve the reliability of autonomous driving simulators. To this end, we present ICSFuzz, a black-box fuzzing approach to discover ignored collision scenarios efficiently. Drawing upon the fact that the ignored collision scenarios are a sub-type of collision scenarios, our approach starts with the determined collision scenarios. Following the guidance provided by empirically studied factors contributing to collisions, we selectively mutate arbitrary collision scenarios in a step-wise manner toward the ignored collision scenarios and effectively discover them. We compare ICSFuzz with DriveFuzz, a state-of-the-art simulation-based ADS testing method, by replacing its oracle with our ignored-collision-aware oracle. The evaluation demonstrates that ICSFuzz outperforms DriveFuzz by finding 10-20x more ignored collision scenarios with a 20-70x speedup. All the discovered ignored collisions have been confirmed by developers with one CVE ID assigned.

翻译：随着自动驾驶车辆的日益普及，确保部署于自动驾驶车辆上的自动驾驶系统（ADS）的可靠性已成为重要关切。驾驶模拟器已成为测试自动驾驶系统的关键平台，提供逼真、动态且可配置的环境。然而，现有的基于模拟的ADS测试方法在很大程度上忽视了模拟器自身的可靠性，可能导致在真实世界部署时忽略违规场景，进而引发安全风险。在我们的研究中，我们发现模拟器中的碰撞检测器在某些碰撞场景下可能无法检测并报告碰撞，此类场景称为被忽略的碰撞场景。本文旨在系统性地发现被忽略的碰撞场景，以提升自动驾驶模拟器的可靠性。为此，我们提出了ICSFuzz，一种黑盒模糊测试方法，用于高效发现被忽略的碰撞场景。基于被忽略的碰撞场景是碰撞场景的一个子类型这一事实，我们的方法从已确定的碰撞场景出发。依据对导致碰撞的经验性因素研究提供的指导，我们有选择地对任意碰撞场景进行逐步变异，使其趋近于被忽略的碰撞场景，从而有效地发现它们。我们将ICSFuzz与最先进的基于模拟的ADS测试方法DriveFuzz进行比较，用我们具备忽略碰撞感知能力的预言机替换其原有预言机。评估结果表明，ICSFuzz在发现被忽略碰撞场景的数量上超出DriveFuzz 10-20倍，同时速度提升20-70倍。所有发现的被忽略碰撞均已得到开发者确认，并分配了一个CVE ID。