3D virtual simulation, which generates diversified test scenarios and tests full-stack of Autonomous Driving Systems (ADSes) modules dynamically as a whole, is a promising approach for Safety of The Intended Functionality (SOTIF) ADS testing. However, as different configurations of a test scenario will affect the sensor perceptions and environment interaction, e.g. light pulses emitted by the LiDAR sensor will undergo backscattering and attenuation, which is usually overlooked by existing works, leading to false positives or wrong results. Moreover, the input space of an ADS is extremely large, with infinite number of possible initial scenarios and mutations, along both temporal and spatial domains. This paper proposes a first-principles based sensor modeling and environment interaction scheme, and integrates it into CARLA simulator. With this scheme, a long-overlooked category of adverse weather related corner cases are discovered, along with their root causes. Moreover, a meta-heuristic algorithm is designed based on several empirical insights, which guide both seed scenarios and mutations, significantly reducing the search dimensions of scenarios and enhancing the efficiency of corner case identification. Experimental results show that under identical simulation setups, our algorithm discovers about four times as many corner cases as compared to state-of-the-art work.

翻译：三维虚拟仿真能够生成多样化的测试场景，并以整体动态方式测试自动驾驶系统全栈模块，是实现预期功能安全（SOTIF）自动驾驶系统测试的一种有前景的方法。然而，测试场景的不同配置会影响传感器感知与环境交互，例如激光雷达传感器发出的光脉冲会经历后向散射和衰减，这一现象常被现有研究忽略，导致误报或错误结果。此外，自动驾驶系统的输入空间极为庞大，在时空域中存在无限数量的初始场景及其变异可能。本文提出了一种基于第一性原理的传感器建模与环境交互方案，并将其集成至CARLA仿真器中。利用该方案，我们发现了长期被忽视的一类与恶劣天气相关的边界场景及其根本原因。同时，基于若干经验洞见设计了一种元启发式算法，该算法通过指导种子场景与变异生成，显著降低了场景搜索维度，提升了边界场景识别效率。实验结果表明，在相同仿真设置下，与现有最优方法相比，我们的算法能发现约四倍数量的边界场景。