Dynamic Map Update Using Connected Vehicle Data
Henry Liu, PhD
Professor in Civil and Environmental Engineering, and Research Professor, U-M Transportation Institute
June 2016 – December 2017
To develop a system that can utilize connected vehicle (CV) data collected by the roadside equipment (RSE) to automatically and dynamically update the Geometric Intersection Description (GID) map at lane level, in order to support various CV applications and automated vehicle technology.
We will leverage our experience from a recent research project funded by Crash Avoidance Metrics Partnership (CAMP) titled “Automatic Intersection Map Generation” to develop estimation algorithms to dynamically detect temporary lane closure and modification, and to dynamically update the GID map. A statistical approach will be developed to monitor lane occupancies and traffic speeds, and detect abnormal traffic patterns, and then estimate traffic lane closures and modification. The estimation algorithm will be integrated in the Connected Vehicle based Controller Interface Device (CV-CID) developed by the project team to interface with RSEs. A central software will also be developed and deployed in the MTC traffic management center to visualize dynamic map and related information of incidents, e.g. accident, game event, or road work.
- Algorithms for dynamic GID map update and incident detection, using CV data from the RSEs.
- A prototype system to integrate estimation algorithms with RSEs.
- Small-scale field implementation of the prototype system.
- A center management software to manage field devices and visualize estimated map incidents information.