skip to main content
10.1145/3487075.3487186acmotherconferencesArticle/Chapter ViewAbstractPublication PagescsaeConference Proceedingsconference-collections
research-article

Extreme Scenario Generation Based on Adversarial Attack

Authors Info & Claims
Published:07 December 2021Publication History

ABSTRACT

The transportation field requires a large number of simulation scenarios for testing. At present, there is relatively little research on the generation of extreme scenarios. In this paper, we give the definition of extreme scenarios, which are prone to problems, and divide them into two categories: the extreme scenarios based on primitive value and the extreme scenarios based on primitive coupling. This paper focuses on the second which considers the coupling effect of different primitives in the scenarios, using the methods of adversarial attack: FGSM, FGSM-target, BIM, ILCM, PGD and strategically-timed attack. Using vehicle agent for test, the first five methods prove the feasibility and effectiveness of extreme scenario generation, and the sixth method simplifies the generation process.

References

  1. Kalra N, Paddock S M (2016). Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability? Transportation Research Part A: Policy and Practice, 94, 182-193.Google ScholarGoogle ScholarCross RefCross Ref
  2. Christensen A, Cunningham A, Engelman J, (2015). Key considerations in the development of driving automation systems. 24th enhanced safety vehicles conference. Gothenburg, Sweden.Google ScholarGoogle Scholar
  3. Benmimoun M (2017). Effective evaluation of automated driving systems. SAE Technical Paper.Google ScholarGoogle Scholar
  4. Urmson C, Anhalt J, Bagnell D, (2008). Autonomous driving in urban environments: Boss and the urban challenge. Journal of Field Robotics, 25(8), 425-466.Google ScholarGoogle ScholarCross RefCross Ref
  5. Saust F, Wille J M, Lichte B, (2011). Autonomous vehicle guidance on braunschweig's inner ring road within the stadtpilot project. 2011 IEEE Intelligent Vehicles Symposium (IV). IEEE, 169-174.Google ScholarGoogle ScholarCross RefCross Ref
  6. Ardelt M, Coester C, Kaempchen N (2012). Highly automated driving on freeways in real traffic using a probabilistic framework. IEEE Transactions on Intelligent Transportation Systems, 13(4), 1576-1585.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Anderson J M, Nidhi K, Stanley K D, (2014). Autonomous Vehicle Technology: A Guide for Policymakers[M]. New York: Rand Corporation.Google ScholarGoogle Scholar
  8. Masuda S (2017). Software testing design techniques used in automated vehicle simulations. 2017 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 300-303.Google ScholarGoogle ScholarCross RefCross Ref
  9. Browne C B, Powley E, Whitehouse D, (2012). A survey of monte carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in games, 4(1), 1-43.Google ScholarGoogle ScholarCross RefCross Ref
  10. Mnih V, Kavukcuoglu K, Silver D, (2015). Human-level control through deep reinforcement learning. nature, 518(7540), 529-533.Google ScholarGoogle Scholar
  11. Van Hasselt H, Guez A, Silver D (2016). Deep reinforcement learning with double q-learning. Proceedings of the AAAI Conference on Artificial Intelligence: volume 30.Google ScholarGoogle ScholarCross RefCross Ref
  12. Wang Z, Schaul T, Hessel M, (2016). Dueling network architectures for deep reinforcement learning. International conference on machine learning. PMLR, 1995-2003.Google ScholarGoogle Scholar
  13. Sorokin I, Seleznev A, Pavlov M, (2015). Deep attention recurrent Q-network. arXiv preprint arXiv:1512.01693.Google ScholarGoogle Scholar
  14. Goodfellow I J, Shlens J, Szegedy C (2014). Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572.Google ScholarGoogle Scholar
  15. Kurakin A, Goodfellow I, Bengio S (2016). Adversarial machine learning at scale. arXiv preprint arXiv:1611.01236.Google ScholarGoogle Scholar
  16. Kurakin A, Goodfellow I J, Bengio S (2016). Adversarial examples in the physical world. arXiv preprint arXiv:1607.02533.Google ScholarGoogle Scholar
  17. Madry A, Makelov A, Schmidt L, (2017). Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083.Google ScholarGoogle Scholar
  18. Lin Y C, Hong Z W, Liao Y H, (2017). Tactics of adversarial attack on deep reinforcement learning agents. arXiv preprint arXiv:1703.06748.Google ScholarGoogle ScholarDigital LibraryDigital Library

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in
  • Published in

    cover image ACM Other conferences
    CSAE '21: Proceedings of the 5th International Conference on Computer Science and Application Engineering
    October 2021
    660 pages
    ISBN:9781450389853
    DOI:10.1145/3487075

    Copyright © 2021 ACM

    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 7 December 2021

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article
    • Research
    • Refereed limited

    Acceptance Rates

    Overall Acceptance Rate368of770submissions,48%
  • Article Metrics

    • Downloads (Last 12 months)9
    • Downloads (Last 6 weeks)0

    Other Metrics

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format .

View HTML Format