IDEAS home Printed from https://ideas.repec.org/p/cdl/itsrrp/qt2qr5975v.html
   My bibliography  Save this paper

Intelligent Intersections Reduce Crashes and Will Support the Safe Introduction of Autonomous Vehicles

Author

Listed:
  • Kurzhanskiy, Alex
  • Varaiya, Pravin

Abstract

Intersections are dangerous. In the U.S., approximately 40% of all crashes, 50% of serious collisions, and 20% of fatalities occur in intersections. Intersections are challenging due to complex interactions among pedestrians, bicycles and vehicles; absence of lane markings; difficulty in determining who has the right of way; blind spots; and illegal movements (e.g., vehicles running red lights). Many cities have developed Vision Zero plans seeking to eliminate traffic injuries and deaths through modifications to road infrastructure, such as adding bike lanes and pedestrian refuge islands. These modifications can be expensive (e.g., the cost of a protected intersection can range between $250,000 to more than a $1 million) and have mixed safety results. It is claimed autonomous vehicles (AVs) will prevent 94% of all crashes involving human error. However, the safety performance of AVs is far below that of human-driven cars. In California, the number of accidents and disengagements per AV mile traveled is 13 to 100 times worse than human-driven cars. The AV fatality rate is equally as bad. AVs find intersections especially challenging; 58 of 66 (88%) AV crashes reported to the California Department of Motor Vehicles (DMV) occurred in intersections.

Suggested Citation

  • Kurzhanskiy, Alex & Varaiya, Pravin, 2018. "Intelligent Intersections Reduce Crashes and Will Support the Safe Introduction of Autonomous Vehicles," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt2qr5975v, Institute of Transportation Studies, UC Berkeley.
  • Handle: RePEc:cdl:itsrrp:qt2qr5975v
    as

    Download full text from publisher

    File URL: https://www.escholarship.org/uc/item/2qr5975v.pdf;origin=repeccitec
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Engineering;

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:cdl:itsrrp:qt2qr5975v. 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: Lisa Schiff (email available below). General contact details of provider: https://edirc.repec.org/data/itucbus.html .

    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.