IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-44694-5.html
   My bibliography  Save this article

Online legal driving behavior monitoring for self-driving vehicles

Author

Listed:
  • Wenhao Yu

    (Tsinghua University)

  • Chengxiang Zhao

    (Beijing Institute of Technology)

  • Hong Wang

    (Tsinghua University)

  • Jiaxin Liu

    (Tsinghua University)

  • Xiaohan Ma

    (Beijing Institute of Technology)

  • Yingkai Yang

    (Tsinghua University)

  • Jun Li

    (Tsinghua University)

  • Weida Wang

    (Beijing Institute of Technology)

  • Xiaosong Hu

    (Chongqing University)

  • Ding Zhao

    (Carnegie Mellon University)

Abstract

Defined traffic laws must be respected by all vehicles when driving on the road, including self-driving vehicles without human drivers. Nevertheless, the ambiguity of human-oriented traffic laws, particularly compliance thresholds, poses a significant challenge to the implementation of regulations on self-driving vehicles, especially in detecting illegal driving behaviors. To address these challenges, here we present a trigger-based hierarchical online monitor for self-assessment of driving behavior, which aims to improve the rationality and real-time performance of the monitoring results. Furthermore, the general principle to determine the ambiguous compliance threshold based on real driving behaviors is proposed, and the specific outcomes and sensitivity of the compliance threshold selection are analyzed. In this work, the effectiveness and real-time capability of the online monitor were verified using both Chinese human driving behavior datasets and real vehicle field tests, indicating the potential for implementing regulations in self-driving vehicles for online monitoring.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-44694-5
    DOI: 10.1038/s41467-024-44694-5
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-44694-5
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-024-44694-5?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
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Amitai Y. Bin-Nun & Patricia Derler & Noushin Mehdipour & Radboud Duintjer Tebbens, 2022. "How should autonomous vehicles drive? Policy, methodological, and social considerations for designing a driver," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-13, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jack Stilgoe & Miloš Mladenović, 2022. "The politics of autonomous vehicles," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-6, 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. 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.
    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. 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.
    6. 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.

    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:15:y:2024:i:1:d:10.1038_s41467-024-44694-5. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.