  Learning-based methodologies increasingly find applications in
safety-critical domains like autonomous driving and medical robotics. Due to
the rare nature of dangerous events, real-world testing is prohibitively
expensive and unscalable. In this work, we employ a probabilistic approach to
safety evaluation in simulation, where we are concerned with computing the
probability of dangerous events. We develop a novel rare-event simulation
method that combines exploration, exploitation, and optimization techniques to
find failure modes and estimate their rate of occurrence. We provide rigorous
guarantees for the performance of our method in terms of both statistical and
computational efficiency. Finally, we demonstrate the efficacy of our approach
on a variety of scenarios, illustrating its usefulness as a tool for rapid
sensitivity analysis and model comparison that are essential to developing and
testing safety-critical autonomous systems.
