  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.
