Using V2X communications and data fusion to achieve lane-level positioning for road vehicles

Tsu Kuang Lee, Juyi Lin, Jen-Jee Chen*, Yu-Chee Tseng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Precise positioning is a key issue for road vehicles in navigation, safety, and autonomous driving applications. While global positioning system (GPS) is widely accepted, it is still a challenge to achieve lane-level positioning. In this work, we consider the fusion of multi-sensory data using particle filter (PF), which is flexible in integrating different information in complex outdoor environments. We focus on three types of popular sensors: controller area network (CAN bus), GPS, and roadside camera. We propose a PF model that can adopt these types of sensory inputs for vehicle positioning. We show that in scenarios where vision sensory inputs are available, lane-level precision can be achieved. When there is no vision coverage, seamless localisation with reasonable precision can still be supported by GPS. Field trial results are presented to validate our model.

Original languageEnglish
Pages (from-to)238-246
Number of pages9
JournalInternational Journal of Sensor Networks
Issue number4
StateE-pub ahead of print - 7 Apr 2020


  • Data fusion
  • GPS
  • Global positioning system
  • MEC
  • Mobile edge computing
  • OBU
  • On board unit
  • Particle filter
  • Positioning
  • RSU
  • Road side unit
  • V2X
  • Vehicular network


Dive into the research topics of 'Using V2X communications and data fusion to achieve lane-level positioning for road vehicles'. Together they form a unique fingerprint.

Cite this