Real-Time Implementation Comparison of Urban Eco-Driving Controls

Details

• Creators:
  Rabinowitz, Aaron I ; Ang, Chon Chia ; Mahmoud, Yara Hazem ; Araghi, Farhang Motallebi ; Meyer, Richard T ; Kolmanovsky, Ilya ; Asher, Zachary D. ; Bradley, Thomas
• Corporate Creators:
  Colorado State University. Department of Systems Engineering
• Corporate Contributors:
  Mountain-Plains Consortium ; North Dakota State University ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Autonomous vehicles
  • Connected vehicles
  • Ecodriving
  • Intelligent transportation systems
  • Advanced traffic management systems
  • Fuel consumption
  • Infrastructure
  • Lagrangian functions
  • Sustainable transportation
  • Trajectory
  More +
• Publication/ Report Number:
  MPC-570
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report
• Corporate Publisher:
  Mountain-Plains Consortium
• Abstract:
  Connected autonomous vehicle (CAV) technology has the potential to enable significant gains in energy economy (EE). Much research attention has been focused on autonomous eco-driving control enabled by various methods. In this study, the state of the literature on autonomous eco-driving control is reviewed, an overall system’s description of eco-driving control for a CAV is provided, and representative methods are evaluated comparatively against each other in simulation. Simulations are conducted using real-world traffic signal data and a validated future automotive systems technology simulator (FASTSim) model. Results indicate that an EE improvement in the range of 5%–15% is attainable depending on the method and cost function used. This article shows that dynamic programming (DP) methods are most effective in improving EE but are significantly more computationally expensive than other methods. The genetic algorithm (GA) methods are shown to present the most potential in terms of EE improvement and run-time. Results also indicate that velocity-sensitive cost functions allow all the methods to perform better than pure acceleration minimization.
• Format:
  PDF
• Funding:
  DE-EE0008468
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/78894/dot_78894_DS1.pdf
• File Type:
  [PDF - 3.44 MB]
• Collection(s):
  Autonomous Vehicles ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:be35be799ed7819274811b4c1e81495ab4ae4b8b9d7c439b4d61fe64a86edbd2b683a276ed8bad3ba95b2008178a0c3071dca241abe00008bda2acb5f051e9c4