Impact of circuit-level non-idealities on vision-based autonomous driving systems

Published

Conference Paper

© 2017 IEEE. We describe a novel methodology to validate vision-based autonomous driving systems over different circuit corners with consideration of temperature variation and circuit aging. The proposed work is motivated by the fact that low-level circuit implementation may have a significant impact on system performance, even though such effects have not been appropriately taken into account today. Our approach seamlessly integrates the image data recorded under nominal conditions with comprehensive statistical circuit models to synthetically generate the critical corner cases for which an autonomous driving system is likely to fail. As such, a given automotive system can be robustly validated for these worst-case scenarios that cannot be easily captured by physical experiments.

Full Text

Duke Authors

Cited Authors

  • Yu, H; Yan, C; Zeng, X; Li, X

Published Date

  • December 13, 2017

Published In

Volume / Issue

  • 2017-November /

Start / End Page

  • 976 - 983

International Standard Serial Number (ISSN)

  • 1092-3152

International Standard Book Number 13 (ISBN-13)

  • 9781538630938

Digital Object Identifier (DOI)

  • 10.1109/ICCAD.2017.8203887

Citation Source

  • Scopus