IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v12y2021i1d10.1038_s41467-021-21007-8.html
   My bibliography  Save this article

Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment

Author

Listed:
  • Shuo Feng

    (University of Michigan)

  • Xintao Yan

    (University of Michigan)

  • Haowei Sun

    (University of Michigan)

  • Yiheng Feng

    (University of Michigan Transportation Research Institute)

  • Henry X. Liu

    (University of Michigan
    University of Michigan Transportation Research Institute)

Abstract

Driving intelligence tests are critical to the development and deployment of autonomous vehicles. The prevailing approach tests autonomous vehicles in life-like simulations of the naturalistic driving environment. However, due to the high dimensionality of the environment and the rareness of safety-critical events, hundreds of millions of miles would be required to demonstrate the safety performance of autonomous vehicles, which is severely inefficient. We discover that sparse but adversarial adjustments to the naturalistic driving environment, resulting in the naturalistic and adversarial driving environment, can significantly reduce the required test miles without loss of evaluation unbiasedness. By training the background vehicles to learn when to execute what adversarial maneuver, the proposed environment becomes an intelligent environment for driving intelligence testing. We demonstrate the effectiveness of the proposed environment in a highway-driving simulation. Comparing with the naturalistic driving environment, the proposed environment can accelerate the evaluation process by multiple orders of magnitude.

Suggested Citation

  • Shuo Feng & Xintao Yan & Haowei Sun & Yiheng Feng & Henry X. Liu, 2021. "Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21007-8
    DOI: 10.1038/s41467-021-21007-8
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-021-21007-8
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-021-21007-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xintao Yan & Zhengxia Zou & Shuo Feng & Haojie Zhu & Haowei Sun & Henry X. Liu, 2023. "Learning naturalistic driving environment with statistical realism," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    2. Wei, Cheng & Hui, Fei & Khattak, Asad J. & Zhao, Xiangmo & Jin, Shaojie, 2023. "Batch human-like trajectory generation for multi-motion-state NPC-vehicles in autonomous driving virtual simulation testing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 616(C).
    3. Wenhao Yu & Chengxiang Zhao & Hong Wang & Jiaxin Liu & Xiaohan Ma & Yingkai Yang & Jun Li & Weida Wang & Xiaosong Hu & Ding Zhao, 2024. "Online legal driving behavior monitoring for self-driving vehicles," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    4. Wang, Zhixia & Du, Hongzhi & Wang, Wei & Zhang, Qichang & Gu, Fengshou & Ball, Andrew D. & Liu, Cheng & Jiao, Xuanbo & Qiu, Hongyun & Shi, Dawei, 2024. "A high performance contra-rotating energy harvester and its wireless sensing application toward green and maintain free vehicle monitoring," Applied Energy, Elsevier, vol. 356(C).
    5. Sun-Ting Tsai & Eric Fields & Yijia Xu & En-Jui Kuo & Pratyush Tiwary, 2022. "Path sampling of recurrent neural networks by incorporating known physics," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    6. Demin Nalic & Aleksa Pandurevic & Arno Eichberger & Martin Fellendorf & Branko Rogic, 2021. "Software Framework for Testing of Automated Driving Systems in the Traffic Environment of Vissim," Energies, MDPI, vol. 14(11), pages 1-9, May.
    7. Henry X. Liu & Shuo Feng, 2024. "Curse of rarity for autonomous vehicles," Nature Communications, Nature, vol. 15(1), pages 1-5, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21007-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.