  Despite recent advances in autonomous driving systems, accidents such as the
fatal Uber crash in 2018 show these systems are still susceptible to edge
cases. Such systems must be thoroughly tested and validated before being
deployed in the real world to avoid such events. Testing in open-world
scenarios can be difficult, time-consuming, and expensive. These challenges can
be addressed by using driving simulators such as CARLA instead. A key part of
such tests is adversarial testing, in which the goal is to find scenarios that
lead to failures of the given system. While several independent efforts in
testing have been made, a well-established testing framework that enables
adversarial testing has yet to be made available for CARLA. We therefore
propose ANTI-CARLA, an automated testing framework in CARLA for simulating
adversarial weather conditions (e.g., heavy rain) and sensor faults (e.g.,
camera occlusion) that fail the system. The operating conditions in which a
given system should be tested are specified in a scenario description language.
The framework offers an efficient search mechanism that searches for
adversarial operating conditions that will fail the tested system. In this way,
ANTI-CARLA extends the CARLA simulator with the capability of performing
adversarial testing on any given driving pipeline. We use ANTI-CARLA to test
the driving pipeline trained with Learning By Cheating (LBC) approach. The
simulation results demonstrate that ANTI-CARLA can effectively and
automatically find a range of failure cases despite LBC reaching an accuracy of
100% in the CARLA benchmark.
