IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i17p9228-d627297.html
   My bibliography  Save this article

Evidence for the Crash Avoidance Effectiveness of Intelligent and Connected Vehicle Technologies

Author

Listed:
  • Hong Tan

    (State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
    Tsinghua Automotive Strategy Research Institute, Tsinghua University, Beijing 100084, China)

  • Fuquan Zhao

    (State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
    Tsinghua Automotive Strategy Research Institute, Tsinghua University, Beijing 100084, China)

  • Han Hao

    (State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
    Tsinghua Automotive Strategy Research Institute, Tsinghua University, Beijing 100084, China)

  • Zongwei Liu

    (State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
    Tsinghua Automotive Strategy Research Institute, Tsinghua University, Beijing 100084, China)

Abstract

The Intelligent and Connected Vehicle (ICV) is regarded as a high-tech solution to reducing road traffic crashes in many countries across the world. However, it is not clear how effective these technologies are in avoiding crashes. This study sets out to summarize the evidence for the crash avoidance effectiveness of technologies equipped on ICVs. In this study, three common methods for safety benefit evaluation were identified: Field operation test (FOT), safety impact methodology (SIM), and statistical analysis methodology (SAM). The advantages and disadvantages of the three methods are compared. In addition, evidence for the crash avoidance effectiveness of Advanced Driver Assistance Systems (ADAS) and Vehicle-to-Vehicle communication Systems (V2V) are presented in the paper. More specifically, target crash scenarios and the effectiveness of technologies including FCW/AEB, ACC, LDW/LDP, BSD, IMA, and LTA are different. Overall, based on evidence from the literature, technologies on ICVs could significantly reduce the number of crashes.

Suggested Citation

  • Hong Tan & Fuquan Zhao & Han Hao & Zongwei Liu, 2021. "Evidence for the Crash Avoidance Effectiveness of Intelligent and Connected Vehicle Technologies," IJERPH, MDPI, vol. 18(17), pages 1-12, September.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:17:p:9228-:d:627297
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/17/9228/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/17/9228/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Hong Tan & Fuquan Zhao & Haokun Song & Zongwei Liu, 2023. "Quantifying the Impact of Deployments of Autonomous Vehicles and Intelligent Roads on Road Safety in China: A Country-Level Modeling Study," IJERPH, MDPI, vol. 20(5), pages 1-15, February.

    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:gam:jijerp:v:18:y:2021:i:17:p:9228-:d:627297. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.