Simulation-based testing represents an important step to ensure the reliability of autonomous driving software. In practice, when companies rely on third-party general-purpose simulators, either for in-house or outsourced testing, the generalizability of testing results to real autonomous vehicles is at stake. In this paper, we strengthen simulation-based testing by introducing the notion of digital siblings, a novel framework in which the AV is tested on multiple general-purpose simulators, built with different technologies. First, test cases are automatically generated for each individual simulator. Then, tests are migrated between simulators, using feature maps to characterize of the exercised driving conditions. Finally, the joint predicted failure probability is computed and a failure is reported only in cases of agreement among the siblings. We implemented our framework using two open-source simulators and we empirically compared it against a digital twin of a physical scaled autonomous vehicle on a large set of test cases. Our study shows that the ensemble failure predictor by the digital siblings is superior to each individual simulator at predicting the failures of the digital twin. We discuss several ways in which our framework can help researchers interested in automated testing of autonomous driving software.

翻译：基于仿真的测试是确保自动驾驶软件可靠性的重要环节。现实中，当企业依赖第三方通用仿真器进行内部或外包测试时，测试结果向真实自动驾驶车辆的泛化性面临挑战。本文通过引入"数字孪生兄弟"概念强化基于仿真的测试——这是一种创新框架，利用采用不同技术构建的多个通用仿真器对自动驾驶车辆进行测试。首先，针对每个仿真器自动生成测试用例；随后，通过特征映射表征所模拟的行驶环境，实现测试用例在仿真器间的迁移；最后，计算联合故障预测概率，仅当孪生兄弟间达成一致时才判定故障。我们基于两个开源仿真器实现该框架，并在大规模测试用例集合中，将其与实体缩比自动驾驶车辆的数字孪生体进行实证对比。研究表明，数字孪生兄弟的集成故障预测器在预测数字孪生体故障方面优于单个仿真器。本文探讨了该框架可助力自动驾驶软件自动化测试研究者的多种途径。