  In the past two decades, autonomous driving has been catalyzed into reality
by the growing capabilities of machine learning. This paradigm shift possesses
significant potential to transform the future of mobility and reshape our
society as a whole. With the recent advances in perception, planning, and
control capabilities, autonomous driving technologies are being rolled out for
public trials, yet we remain far from being able to rigorously ensure the
resilient operations of these systems across the long-tailed nature of the
driving environment. Given the limitations of real-world testing, autonomous
vehicle simulation stands as the critical component in exploring the edge of
autonomous driving capabilities, developing the robust behaviors required for
successful real-world operation, and enabling the extraction of hidden risks
from these complex systems prior to deployment. This paper presents the current
state-of-the-art simulation frameworks and methodologies used in the
development of autonomous driving systems, with a focus on outlining how
simulation is used to build the resiliency required for real-world operation
and the methods developed to bridge the gap between simulation and reality. A
synthesis of the key challenges surrounding autonomous driving simulation is
presented, specifically highlighting the opportunities to further advance the
ability to continuously learn in simulation and effectively transfer the
learning into the real-world - enabling autonomous vehicles to exit the
guardrails of simulation and deliver robust and resilient operations at scale.
