IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v195y2025ics0965856425000916.html
   My bibliography  Save this article

Refined high-definition map model for roadside rest area

Author

Listed:
  • Guo, Yuan
  • Zhou, Jian
  • Dong, Quanhua
  • Li, Bijun
  • Xiao, Jinsheng
  • Li, Zhijiang

Abstract

With the rapid advancements in information and communication technology, sensor technology, and artificial intelligence, intelligent vehicles are advancing rapidly, creating new demands for navigation electronic maps. In response to these emerging requirements, high-definition (HD) maps have quickly risen to prominence. However, current HD maps primarily focus on road data and often simplify roadside rest areas (RRAs) into points of interest (POIs), presenting challenges for intelligent vehicles navigating within RRAs. To address these challenges, this paper introduces a refined HD map model specifically tailored for RRA. It categorizes the traffic elements within RRAs into four distinct classes: roads, lanes, markings and signs, and service areas. Each class of elements is thoroughly defined and explained within the proposed map model. In practical RRA scenarios, the proposed map model is visualized and validated using map exchange formats and physical application formats. The introduction of the RRA HD map model not only ensures that vehicles can achieve intelligent driving within RRAs, but also contributes to the establishment of data management platforms for RRAs, providing travellers users with enhanced services.

Suggested Citation

  • Guo, Yuan & Zhou, Jian & Dong, Quanhua & Li, Bijun & Xiao, Jinsheng & Li, Zhijiang, 2025. "Refined high-definition map model for roadside rest area," Transportation Research Part A: Policy and Practice, Elsevier, vol. 195(C).
    • Handle: RePEc:eee:transa:v:195:y:2025:i:c:s0965856425000916
    DOI: 10.1016/j.tra.2025.104463
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856425000916
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tra.2025.104463?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:eee:transa:v:195:y:2025:i:c:s0965856425000916. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    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.