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Simulation-based testing represents an important step to ensure thereliability of autonomous driving software. In practice, when companies rely onthird-party general-purpose simulators, either for in-house or outsourcedtesting, the generalizability of testing results to real autonomous vehicles isat stake.  In this paper, we strengthen simulation-based testing by introducing thenotion of digital siblings, a novel framework in which the AV is tested onmultiple 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 ofthe exercised driving conditions. Finally, the joint predicted failureprobability is computed and a failure is reported only in cases of agreementamong the siblings.  We implemented our framework using two open-source simulators and weempirically compared it against a digital twin of a physical scaled autonomousvehicle on a large set of test cases. Our study shows that the ensemble failurepredictor by the digital siblings is superior to each individual simulator atpredicting the failures of the digital twin. We discuss several ways in whichour framework can help researchers interested in automated testing ofautonomous driving software.

Two is Better Than One: Digital Siblings to Improve Autonomous Driving Testing