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Intelligent corner synthesis via cycle-consistent generative adversarial networks for efficient validation of autonomous driving systems

Authors:

Xin LiAuthors Info & Claims

ASPDAC '18: Proceedings of the 23rd Asia and South Pacific Design Automation Conference

Pages 9 - 15

Published: 22 January 2018 Publication History

Abstract

Today's automotive vehicles are often equipped with powerful data processing systems for driver assistance and/or autonomous driving. To meet the rigorous safety standard, one critical task is to ensure extremely small failure rate over all possible operation conditions. Such a validation task requires a large amount of on-road testing data to cover all possible corners. In this paper, we describe a novel general-purpose methodology to synthetically and efficiently generate a broad spectrum of corner cases for validation purpose. Our proposed method is based upon cycle-consistent generative adversarial networks (CycleGANs) trained by a small set of image samples to mathematically map a nominal case to other corner cases. By taking STOP sign detection as an example, our numerical experiments demonstrate that the proposed approach is able to reduce the validation error by up to 100× given a limited data set for corner cases.

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Cited By

Tang SZhang ZZhang YZhou JGuo YLiu SGuo SLi YMa LXue YLiu Y(2023)A Survey on Automated Driving System Testing: Landscapes and TrendsACM Transactions on Software Engineering and Methodology10.1145/357964232:5(1-62)Online publication date: 24-Jul-2023
https://dl.acm.org/doi/10.1145/3579642

Intelligent corner synthesis via cycle-consistent generative adversarial networks for efficient validation of autonomous driving systems
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

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Published In

cover image ACM Conferences

ASPDAC '18: Proceedings of the 23rd Asia and South Pacific Design Automation Conference

January 2018

774 pages

General Chair:
Youngsoo Shin
KAIST

Sponsors

IEEE Circuits and Systems Society
SIGDA: ACM Special Interest Group on Design Automation
IEEE Council on Electronic Design Automation (CEDA)

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IEEE Press

Publication History

Published: 22 January 2018

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ASPDAC '18

Sponsor:

SIGDA

ASPDAC '18: 23rd Asia and South Pacific Design Automation Conference

January 22 - 25, 2018

Jeju, Republic of Korea

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Overall Acceptance Rate 466 of 1,454 submissions, 32%

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ASPDAC '25

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30th Asia and South Pacific Design Automation Conference

January 20 - 23, 2025

Tokyo , Japan

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Cited By

Tang SZhang ZZhang YZhou JGuo YLiu SGuo SLi YMa LXue YLiu Y(2023)A Survey on Automated Driving System Testing: Landscapes and TrendsACM Transactions on Software Engineering and Methodology10.1145/357964232:5(1-62)Online publication date: 24-Jul-2023
https://dl.acm.org/doi/10.1145/3579642

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