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

Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems