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arXiv · 1811.00145

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

Matthew O'Kelly·Aman Sinha·Hongseok Namkoong·John Duchi·Russ Tedrake
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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.

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Matthew O'Kelly, Aman Sinha, Hongseok Namkoong, John Duchi, Russ Tedrake. 2018-10-31. Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation. https://arxiv.org/abs/1811.00145

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