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

Authors:
Matthew O'Kelly University of Pennsylvania
Aman Sinha Stanford University
Hong Namkoong Stanford University
Russ Tedrake MIT
John Duchi Stanford

Introduction:

While recent developments in autonomous vehicle (AV) technology highlight substantial progress, the authors lack tools for rigorous and scalable testing.The authors 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.

Abstract:

While recent developments in autonomous vehicle (AV) technology highlight substantial progress, we lack tools for rigorous and scalable testing. Real-world testing, the 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 10-300P times (where P is the number of processors) over real-world testing.

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