  3D virtual simulation, which generates diversified test scenarios and tests
full-stack of Autonomous Driving Systems (ADSes) modules dynamically as a
whole, is a promising approach for Safety of The Intended Functionality (SOTIF)
ADS testing. However, as different configurations of a test scenario will
affect the sensor perceptions and environment interaction, e.g. light pulses
emitted by the LiDAR sensor will undergo backscattering and attenuation, which
is usually overlooked by existing works, leading to false positives or wrong
results. Moreover, the input space of an ADS is extremely large, with infinite
number of possible initial scenarios and mutations, along both temporal and
spatial domains.
  This paper proposes a first-principles based sensor modeling and environment
interaction scheme, and integrates it into CARLA simulator. With this scheme, a
long-overlooked category of adverse weather related corner cases are
discovered, along with their root causes. Moreover, a meta-heuristic algorithm
is designed based on several empirical insights, which guide both seed
scenarios and mutations, significantly reducing the search dimensions of
scenarios and enhancing the efficiency of corner case identification.
Experimental results show that under identical simulation setups, our algorithm
discovers about four times as many corner cases as compared to state-of-the-art
work.
