Two is Better Than One: Digital Siblings to Improve Autonomous Driving Testing
arxiv(2023)
摘要
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 enhance simulation-based testing by introducing the
notion of digital siblings, a multi-simulator approach that tests a given
autonomous vehicle on multiple general-purpose simulators built with different
technologies, that operate collectively as an ensemble in the testing process.
We exemplify our approach on a case study focused on testing the lane-keeping
component of an autonomous vehicle. We use two open-source simulators as
digital siblings, and we empirically compare such a multi-simulator approach
against a digital twin of a physical scaled autonomous vehicle on a large set
of test cases. Our approach requires generating and running test cases for each
individual simulator, in the form of sequences of road points. Then, test cases
are migrated between simulators, using feature maps to characterize 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.
Our empirical evaluation 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 the findings of our case study and
detail how our approach can help researchers interested in automated testing of
autonomous driving software.
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