Data Fusing Algorithms for Improved Localization within V2X Network Using Low Cost GPS


Jing Sun, PhD
Professor, Naval Architecture and Marine Engineering


May 2016 – April 2018


Ding Zhao, PhD
Research Fellow, U-M Transportation Research Institute

Macheng Shen
Graduate Research Assistant, Department of Naval Architecture and Marine Engineering


To improve the positioning accuracy of non-RTK GPS from meters to centimeters using V2X communication.


  1. Build an accurate base-map using data collected from the MTC Pillar 1 project.
  2. Query Safety Pilot Model Deployment and Pillar 1 data.
  3. Develop algorithms to improve positioning accuracy based on multiple vehicle GPS information and the base-map of the road network.
  4. Test the algorithms on smartphones and demonstrate their potential application to pedestrian safety in Mcity.
  5. Validate the approach in the Pillar 1 living laboratory.


We showed that the CMM Network has the potential to provide cooperative localization with the desired accuracy for connected vehicles as a low-cost GNSS method with in both centralized and distributed manner, and can be applied on real work traffic scenarios. The following key findings led us to  this conclusion:

  • A Rao-Blackwellized particle filter has been proposed for the simultaneous estimation of GNSS common biases and vehicles cooperative localization using map matching
  • The impact of road configuration on the CMM localization accuracy was studied theoretically, which was then used to evaluate the CMM accuracy in the real world
  • A theoretical study was presented for quantitatively evaluating effects of the road constraints on the CMM accuracy and for eventually optimization of the CMM network
  • A fusion mechanism for distributed CMM was provided and evaluated the correlation between the estimation variance the MSE over the network
  • An implementation of CMM on real-world traffic was presented, based on Safety-Pilot database for dynamic vehicles on roads, and considered  DSRC packet loss