  A rigorous and comprehensive testing plays a key role in training
self-driving cars to handle variety of situations that they are expected to see
on public roads.
  The physical testing on public roads is unsafe, costly, and not always
reproducible. This is where testing in simulation helps fill the gap, however,
the problem with simulation testing is that it is only as good as the simulator
used for testing and how representative the simulated scenarios are of the real
environment. In this paper, we identify key requirements that a good simulator
must have. Further, we provide a comparison of commonly used simulators. Our
analysis shows that CARLA and LGSVL simulators are the current state-of-the-art
simulators for end to end testing of self-driving cars for the reasons
mentioned in this paper. Finally, we also present current challenges that
simulation testing continues to face as we march towards building fully
autonomous cars.
