Integration of Automated Vehicle Sensing with Adaptive Signal Control for Enhanced Mobility

Real-time adaptive signal control systems are limited by the accuracy of contemporary vehicle detection technologies. Regardless of the sensing modality chosen (video, thermal, radar, etc.), standard outputs consist of snapshots of vehicle counts and presence data as they pass through designated spatial regions on the roadway. The distance between such sensing zones typically results in considerable uncertainty (e.g., due to mid-block vehicle exits and entries from the roadway) in the adaptive signal control system’s predictive model of approaching traffic volumes and vehicle arrival times. Connected vehicle technology offers the promise of unprecedented improvement in predictive modeling accuracy through receipt of continuous location, heading and speed updates of approaching vehicles. Yet the full benefit will accrue only as the level of penetration of equipped vehicles increases, and full penetration is likely still decades away. This project proposes to use the sensing abilities of connected autonomous vehicles (CAVs) to compensate for the relatively low numbers of equipped vehicles on the road in the shorter term, and accelerate the sensing benefit that can be provided to adaptive signal control systems by connected vehicle technology over time. The basic idea is to have the CAV communicate not only its continuous location, heading and speed to the infrastructure, but also the continuous location, heading and speed of the vehicles that it perceives in its local vicinity as it proceeds along the road, in essence “virtually” increasing the number of equipped vehicles on the road. To take advantage of the existing Pittsburgh connected vehicle testbed in East Liberty, we will focus specifically on establishing interoperability between CAVs and the Surtrac adaptive signal control system, and on evaluating benefit of this extended vehicle-to-infrastructure (V2I) communication in this context. DSRC protocols will be developed to enable CAV communication of information about surrounding vehicles, and the Surtrac system will be extended to integrate this continuously received location, speed, and heading information into its baseline predictive model (derived from its conventional vehicle detection devices). We will evaluate the performance benefit accrued from using this additional sensing information first in simulation, by analyzing improvement with respect to standard mobility metrics (delay, number of stops, wait time, etc.). Then, in collaboration with Argo AI and Rapid Flow Technologies, we will develop a field implementation and experimentally validate the approach within the East Liberty test bed.


  • English


  • Status: Completed
  • Funding: $100000
  • Contract Numbers:


  • Sponsor Organizations:

    Carnegie Mellon University

    Mobility21 National USDOT UTC for Mobility of Goods and People
    Pittsburgh, PA  United States  15213

    Office of the Assistant Secretary for Research and Technology

    University Transportation Center Program
  • Managing Organizations:

    Carnegie Mellon University

    Mobility21 National USDOT UTC for Mobility of Goods and People
    Pittsburgh, PA  United States  15213
  • Project Managers:

    Kline, Robin

  • Performing Organizations:

    Carnegie Mellon University

  • Principal Investigators:

    Smith, Stephen

  • Start Date: 20190701
  • Expected Completion Date: 20200630
  • Actual Completion Date: 20210204
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01760139
  • Record Type: Research project
  • Source Agency: National University Transportation Center for Improving Mobility (Mobility21)
  • Contract Numbers: 69A3551747111
  • Files: UTC, RiP
  • Created Date: Dec 16 2020 2:05PM