Efficient 3D Road Map Data Exchange for Intelligent Vehicles in Vehicular Fog Networks

Ivan Wang Hei Ho, Sid Chi Kin Chau, Elmer R. Magsino, Kanghao Jia

Research output: Journal article publicationJournal articleAcademic researchpeer-review

25 Citations (Scopus)


Through connecting intelligent vehicles as well as the roadside infrastructure, the perception range of vehicles can be significantly extended, and hidden objects at blind spots can be efficiently detected and avoided. To realize this, accurate road map data must be downloaded in real time to these intelligent vehicles for navigation and localization purposes. Besides, the cloud must be updated with dynamic changes that happened in the road network. These involve the transmissions of high-definition 3D road map data for accurately representing the physical environments. In this work, we propose solutions under the fog computing architecture in a heterogeneous vehicular network to optimize data exchange among intelligent vehicles, the roadside infrastructure, as well as regional databases. Specifically, the efficiency of 3D road map data dissemination at roadside fog nodes is achieved by exploiting index coding techniques to reduce the overall data load, while opportunistic scheduling of heterogeneous transmissions can be done to judiciously manage network resources and minimize operating cost. In addition, 3D point cloud coding and hashing techniques are applied to expedite the updates of various dynamic changes in the network. We empirically evaluate the proposed solutions based on real-world mobility traces of vehicles and 3D LIght Detection And Ranging (LIDAR) data of city streets. The proposed system is also implemented in a multi-robotic testbed for practical evaluation.

Original languageEnglish
Article number8946549
Pages (from-to)3151-3165
Number of pages15
JournalIEEE Transactions on Vehicular Technology
Issue number3
Publication statusPublished - Mar 2020


  • fog computing
  • index coding
  • Intelligent connected vehicles
  • opportunistic scheduling
  • vehicular networks

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics


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