Energy-aware Trajectory Optimization of Connected and Automated Vehicle Platoons through a Signalized Intersection

Details

• Creators:
  Han, Xiao ; Ma, Rui ; Zhang, H Michael
• Corporate Creators:
  University of California, Davis. Institute of Transportation Studies
• Corporate Contributors:
  University of California Institute of Transportation Studies
• Subject/TRT Terms:
  • Autonomous vehicles
  • Connected vehicles
  • Fuel consumption
  • Greenhouse gases
  • Signalized intersections
  • Traffic platooning
  • Trajectory control
  • Vehicle trajectories
• Publication/ Report Number:
  UC-ITS-2018-48, UCD-ITS-RR-19-11
• DOI:
  https://doi.org/10.7922/G2ZW1J4R
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report
• Corporate Publisher:
  University of California Institute of Transportation Studies
• Abstract:
  Traffic signals, while serving an important function to coordinate vehicle movements through intersections, also cause frequent stops and delays, particularly when they are not properly timed. Such stops and delays contribute to significant amount of fuel consumption and greenhouse gas emissions. The recent development of connected and automated vehicle (CAV) technology provides new opportunities to enable better control of vehicles and intersections, that in turn reduces fuel consumption and emissions. In this paper, we propose platoon-trajectory-optimization (PTO) to minimize the total fuel consumption of a CAV platoon through a signalized intersection. In this approach, all CAVs in one platoon are considered as a whole, that is, all other CAVs follow the trajectory of the leading one with a time delay and minimum safety gap, which is enabled by vehicle to vehicle communication. Moreover, the leading CAV in the platoon learns of the signal timing plan just after it enters the approach segment through vehicle to infrastructure communication.
• Format:
  PDF
• Funding:
  UC-ITS-2018-48
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/68880/dot_68880_DS1.pdf
• File Type:
  [PDF - 1.54 MB]
• Collection(s):
  Autonomous Vehicles ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:888e0c132cfe7a26a1fd0d3347c6d6432100812d3b73b67e0de63aa8c908813002befdf2ad7ccdb6c701cbbe96b3ee6c8fb21a97189cbd3cb8589d3f02ff031e