Shaping Automated Vehicle Behaviors in Mixed Autonomy Traffic to Benefit All Road Users and Reduce Greenhouse Gas Emissions

Autonomous vehicles (AVs) stand as the forefront of future societies, yet their coexistence with human-driven vehicles (HVs) in the near-to-mid-term necessitates a comprehensive understanding of the mixed autonomy traffic. Evidence has shown that (1) the real-world interactions between AVs and HVs are not well understood, (2) undesirable AV behaviors can have adverse effects on other road users, and (3) the benefits of reducing greenhouse gas (GHG) emissions are not significant, particularly at low AV penetration rates. This research includes a game theory-guided, AI-driven framework to address these issues. The goal is to understand vehicle interactions in mixed traffic and leverage the understanding to shape AV behaviors, simultaneously benefiting all road users (i.e., ensuring equity) and achieving emission reduction, even with a limited proportion of AVs. Centered on the concept of collective cooperation proposed in the researchers’ previous work (Li et al., 2022), they empirically verify its existence in real-world mixed traffic. Leveraging this property, the researchers will design AV behaviors using deep reinforcement learning (DRL), steering the system towards Pareto efficiency. The encouragement of spatial separation in collective cooperation also suggests the potential for emission reduction. Preliminary results show higher speeds for both AVs and HVs, along with spatial separations and platooning behaviors. These findings suggest the promise of the researchers’ proposed framework in fulfilling the equity and emission objectives, which is worth further investigation.

• Record URL:
  https://ncst.ucdavis.edu/project/shaping-av-behaviors-mixed-autonomy-traffic-benefit-all-road-users-and-reduce-greenhouse

Language

• English

Project

• Status: Active
• Funding: $35000
• 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 
• 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
• Project Managers:

  Iacobucci, Lauren

• Performing 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
• Principal Investigators:

  Chen, Di

  Zhang, Michael

• Start Date: 20240701
• Expected Completion Date: 20250630
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Artificial intelligence; Autonomous vehicles; Equity; Game theory; Greenhouse gases; Machine learning; Market penetration; Vehicle mix
• Subject Areas: Environment; Highways; Planning and Forecasting; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01911827
• Record Type: Research project
• Source Agency: National Center for Sustainable Transportation
• Contract Numbers: DOT 69A3552348319, DOT 69A3552344814
• Files: UTC, RIP
• Created Date: Mar 13 2024 5:50PM