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3D virtual simulation, which generates diversified test scenarios and testsfull-stack of Autonomous Driving Systems (ADSes) modules dynamically as awhole, is a promising approach for Safety of The Intended Functionality (SOTIF)ADS testing. However, as different configurations of a test scenario willaffect the sensor perceptions and environment interaction, e.g. light pulsesemitted by the LiDAR sensor will undergo backscattering and attenuation, whichis usually overlooked by existing works, leading to false positives or wrongresults. Moreover, the input space of an ADS is extremely large, with infinitenumber of possible initial scenarios and mutations, along both temporal andspatial domains.  This paper proposes a first-principles based sensor modeling and environmentinteraction scheme, and integrates it into CARLA simulator. With this scheme, along-overlooked category of adverse weather related corner cases arediscovered, along with their root causes. Moreover, a meta-heuristic algorithmis designed based on several empirical insights, which guide both seedscenarios and mutations, significantly reducing the search dimensions ofscenarios and enhancing the efficiency of corner case identification.Experimental results show that under identical simulation setups, our algorithmdiscovers about four times as many corner cases as compared to state-of-the-artwork.

First-principles Based 3D Virtual Simulation Testing for Discovering SOTIF Corner Cases of Autonomous Driving