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While recent developments in autonomous vehicle (AV) technology highlightsubstantial progress, we lack tools for rigorous and scalable testing.Real-world testing, the $\textit{de facto}$ evaluation environment, places thepublic in danger, and, due to the rare nature of accidents, will requirebillions of miles in order to statistically validate performance claims. Weimplement a simulation framework that can test an entire modern autonomousdriving system, including, in particular, systems that employ deep-learningperception and control algorithms. Using adaptive importance-sampling methodsto accelerate rare-event probability evaluation, we estimate the probability ofan accident under a base distribution governing standard traffic behavior. Wedemonstrate our framework on a highway scenario, accelerating system evaluationby $2$-$20$ times over naive Monte Carlo sampling methods and $10$-$300\mathsf{P}$ times (where $\mathsf{P}$ is the number of processors) overreal-world testing.

Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation