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• W2891468099 abstract "While recent developments in autonomous vehicle (AV) technology highlight substantial progress, we lack tools for rigorous and scalable testing. Real-world testing, the $textit{de facto}$ evaluation environment, places the public in danger, and, due to the rare nature of accidents, will require billions of miles in order to statistically validate performance claims. We implement a simulation framework that can test an entire modern autonomous driving system, including, in particular, systems that employ deep-learning perception and control algorithms. Using adaptive importance-sampling methods to accelerate rare-event probability evaluation, we estimate the probability of an accident under a base distribution governing standard traffic behavior. We demonstrate our framework on a highway scenario, accelerating system evaluation by $2$-$20$ times over naive Monte Carlo sampling methods and $10$-$300 mathsf{P}$ times (where $mathsf{P}$ is the number of processors) over real-world testing." @default.
• W2891468099 created "2018-09-27" @default.
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• W2891468099 date "2018-10-31" @default.
• W2891468099 modified "2023-09-22" @default.
• W2891468099 title "Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation" @default.
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