  Simulation-based virtual testing has become an essential step to ensure the
safety of autonomous driving systems. Testers need to handcraft the virtual
driving scenes and configure various environmental settings like surrounding
traffic, weather conditions, etc. Due to the huge amount of configuration
possibilities, the human efforts are subject to the inefficiency in detecting
flaws in industry-class autonomous driving system. This paper proposes a
coverage-driven fuzzing technique to automatically generate diverse
configuration parameters to form new driving scenes. Experimental results show
that our fuzzing method can significantly reduce the cost in deriving new risky
scenes from the initial setup designed by testers. We expect automated fuzzing
will become a common practice in virtual testing for autonomous driving
systems.
