Development of AI-based and control-based systems for safe and efficient operations of connected and autonomous vehicles

This research is in three parts. The first part recognizes the range limitations of onboard sensors such as LiDAR and cameras, and develops an AI control system that fuses sensed (local) information and longer-range information to make CAV lane-changing decisions. Deep Reinforcement Learning is being used to provide an end-to-end framework that will help identify the optimal connectivity range for each domain of prevailing operating traffic density. The second part is developing a method to demonstrate a CAV’s catalytic efficacy for addressing stop-and-go traffic perturbations that adversely affects operational efficiency, fuel economy, emissions, travel time, and driver/passenger comfort. The third part is developing a collision avoidance framework for CAVs, to reduce the likelihood of collision with surrounding vehicles, particularly HDVs that drive aggressively or have uncertain or unpredictable behavior.

Language

• English

Project

• Status: Active
• Funding: $740k (CCAT: $370,000; Cost share: $370,000)
• Contract Numbers:

  69A3551747105

• Sponsor Organizations:

  Department of Transportation

  Intelligent Transportation Systems Joint Program Office
  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590
• Managing Organizations:

  Center for Connected and Automated Transportation

  University of Michigan, Ann Arbor
  Ann Arbor, MI  United States  48109
• Project Managers:

  Tucker-Thomas, Dawn

• Performing Organizations:

  Purdue University, Lyles School of Civil Engineering

  550 Stadium Mall Drive
  West Lafayette, IN  United States  47907
• Principal Investigators:

  Labi, Samuel

• Start Date: 20200101
• Expected Completion Date: 20220930
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program
• Subprogram: Research

Subject/Index Terms

• TRT Terms: Aggression; Artificial intelligence; Autonomous land vehicles; Autonomous vehicles; Behavior; Cameras; Connected vehicles; Crash avoidance systems; Emissions testing; Long range planning; Passenger comfort; Reinforcement (Engineering); Sensors; Traffic; Traffic density; Travel time; Vehicles
• Subject Areas: Design; Policy; Research; Transportation (General); Vehicles and Equipment;

Filing Info

• Accession Number: 01742715
• Record Type: Research project
• Source Agency: Center for Connected and Automated Transportation
• Contract Numbers: 69A3551747105
• Files: UTC, RIP
• Created Date: Jun 18 2020 11:20AM