Optimization of Safe and Cost-Effective Driving Strategies with Connected and Autonomous Vehicles through Driving Simulator Tests

Connected and Autonomous Vehicles (CAVs) allow the development of more advanced driving strategies with reduced safety risks and delays when passing intersections with mixed traffic flow. This project will develop advanced optimization strategies for traffic operations over transportation networks with CAVs under complicated dynamic traffic conditions. Human-factor experiments will be conducted via a stationary driving simulator to evaluate the impact of three independent scenarios on drivers’ driving performance under mixed traffic flow: (1) absence or presence of crossing pedestrians, (2) availability of real-time voice alert prompts from the Advanced Driver-Assistance System (ADAS), and (3) disabled or activated safety and cost-effective traffic signal control algorithms. The research team will recruit licensed adult drivers from the TSU campus community and local Houston residents, covering different age ranges, driving experience, and familiarity with CAVs, representing regular urban road users to ensure generalizable experimental outcomes. A full within-subject design is adopted, and all participants shall complete all test scenarios for direct performance comparison. The sample size should meet the standard statistical power (≥0.85) for transportation simulator studies. The optimized traffic control strategies for intersections with mixed traffic flow will help transportation agencies at all levels and car manufacturers to understand the design, operation, and impacts of optimal signal control 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: 20261231
• 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