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.

通过引入数字同胞的概念，本研究提出一种新的模拟测试框架，该框架可以在多个通用模拟器上测试自动驾驶软件，并可以计算集成预测失败概率，并将失败仅在同胞之间达成一致时报告。研究结果表明，数字同胞的集成失败预测器优于每个单独的模拟器，可以帮助感兴趣的研究人员进行自动化测试的研究。