Scalable end-to-end autonomous vehicle testing via rare-event simulation
Name
NeurIPS-2018-scalable-end-to-end-autonomous-vehicle-testing-via-rare-event-simulation-Paper.pdf
Description
Published version
Size
678.7 KB
Format
Adobe PDF
Checksum (MD5)
3bbd3384cce13a3df83a127edb26d9d5
Author(s)
O'Kelly, M
Duchi, J
Sinha, A
Namkoong, H
Tedrake, R
Date Issued
January 1, 2018
Journal
Advances in Neural Information Processing Systems
Citation
O'Kelly, M, Duchi, J, Sinha, A, Namkoong, H and Tedrake, R. 2018. "Scalable end-to-end autonomous vehicle testing via rare-event simulation." Advances in Neural Information Processing Systems, 2018-December.
Version
Final published version
Abstract
© 2018 Curran Associates Inc.All rights reserved. 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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://papers.nips.cc/paper/2018/hash/653c579e3f9ba5c03f2f2f8cf4512b39-Abstract.html
