DRiVERSITY - Synthetic Torture Testing to Find Limits of Autonomous Driving Algorithms

Frassinelli, Daniel and Gambi, Alessio and Nürnberger, Stefan and Park, Sohyeon
(2018) DRiVERSITY - Synthetic Torture Testing to Find Limits of Autonomous Driving Algorithms.
In: ACM Computer Science in Cars Conference.
Conference: None | Not Set

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Abstract

Autonomous driving is expected to significantly improve road safety. Carmakers are conducting extensive testing of their autonomous vehicles on proofing grounds and in virtual pre-defined scenarios. Because proofing grounds do not offer a deterministic test field and are time-consuming, virtual hardware- and software-in-the-loop testing is en vogue as it provides reproducibility. However, pre-defined tests (real or virtual) represent low coverage in comparison to all physically possible driving scenarios. Furthermore, they are unlikely to systematically discover corner cases that emerge due to software bugs or absurd but possible scenarios. In this abstract, we introduce the DRiVERSITY framework for systematic testing of autonomous driving algorithms. Our DRiVERSITY framework builds scenes on the fly – adapting to how a car handles situations while driving. DRiVERSITY pronounces misbehaviour by tailoring new scenes based on monitored driving behaviour during fuzzing stimuli. DRiVERSITY shall provide a standard testing framework to evaluate and compare driving algorithms in a reproducible and controllable way.

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