Deep Learning-Based Optimization of Eco-Driving Strategies with Connected and Autonomous Electric Vehicles on Transportation Networks

Connected and Autonomous Electric Vehicles (CAEVs) allow the design of more advanced driving strategies, such as eco-driving strategies, towards even lower energy consumption when passing intersections. This project will create a deep learning-based optimization system on eco-driving strategies for traffic operations over transportation networks with CAEVs under complicated dynamic traffic conditions. The research will start with collecting field operational data on CAEV in transportation networks (isolated intersections, arterial streets, and road networks). Then, deep learning-based algorithms will be developed to optimize the eco-driving strategies for CAEVs on various transportation networks, while the field test data will validate the optimization model. Finally, the driving simulator tests will be conducted to measure the optimization models and eco-driving strategies. About twenty subjects will be recruited for the simulator tests over different simulation scenes: with and without the optimized eco-driving strategies. The deep learning-based optimization of eco-driving strategy is expected to significantly reduce the energy consumption of CAEVs at isolated intersections, along arterial streets, and on road networks. This project will help both transportation and environmental agencies at all levels, and car manufacturers, to understand the design, operation, and impacts of optimal eco-driving strategies. The project will provide urgent science and test-based input to inform policy and practice development.

• Record URL:
  https://ncst.ucdavis.edu/project/deep-learning-based-optimization-eco-driving-strategies-connected-and-autonomous-electric

Language

• English

Project

• Status: Active
• Funding: $228000
• Contract Numbers:

  DOT 69A3552348319

  DOT 69A3552344814

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590

  National Center for Sustainable Transportation

  University of California, Davis
  Davis, CA  United States 

  Texas Southern University, Houston

  3100 Cleburne Street
  Houston, TX  United States  77004
• Managing Organizations:

  National Center for Sustainable Transportation

  University of California, Davis
  Davis, CA  United States 

  University of California, Davis

  1 Shields Ave
  Davis, California  United States  95616

  Texas Southern University, Houston

  3100 Cleburne Street
  Houston, TX  United States  77004
• Project Managers:

  Iacobucci, Lauren

• Performing Organizations:

  National Center for Sustainable Transportation

  University of California, Davis
  Davis, CA  United States 

  Texas Southern University, Houston

  3100 Cleburne Street
  Houston, TX  United States  77004
• Principal Investigators:

  Qiao, Fengxiang

• Start Date: 20240901
• Expected Completion Date: 20250831
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Connected vehicles; Driving simulators; Ecodriving; Electric vehicles; Intersections; Machine learning; Optimization
• Subject Areas: Data and Information Technology; Energy; Environment; Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01902068
• Record Type: Research project
• Source Agency: National Center for Sustainable Transportation
• Contract Numbers: DOT 69A3552348319, DOT 69A3552344814
• Files: UTC, RIP
• Created Date: Dec 13 2023 1:29PM