Lane Changing of Autonomous Vehicles in Mixed Traffic Environments: A Reinforcement Learning Approach

The purpose of this proposal is to develop innovative reinforcement learning control methods for lane changing of connected and autonomous vehicles (CAVs) in mixed traffic. In the proposed framework, before the CAV changes to the target lane, it needs to predict most likely behavior of surrounding vehicles related to the lane change and then determine the optimal path to the other lane. The lane changing maneuver will be completed by solving a linear quadratic regulator (LQR) problem that yields an optimal controller to monitor the CAV’s longitudinal and lateral movements during the lane-changing maneuver. A novel aspect of this research is to reduce the trajectory planning and tracking problem down to the minimization of a cost function that depends on the target way-point in the target lane the CAV will reach. In the proposal, the research team will integrate reinforcement learning and adaptive/approximate dynamic programming methods to solve this data-driven LQR control problem under constraints, without assuming the exact knowledge of surrounding vehicles, while avoiding the curses of dimensionality and modeling of conventional dynamic programming. Thanks to the systematic use of systems and control- theoretic methods, the proposed framework aims to yield desirable lane- changing controllers with guaranteed stability for CAVs from small samples of historical and real-time data.

• Record URL:
  https://c2smart.engineering.nyu.edu/lane-changing-of-autonomous-vehicles-in-mixed-traffic-environments-a-reinforcement-learning-approach/

Language

• English

Project

• Status: Completed
• Funding: $98990
• Contract Numbers:

  USDOT 69A3551747124'

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Connected Cities for Smart Mobility towards Accessible and Resilient Transportation Center (C2SMART)

  New York University
  Tandon School of Engineering
  Brooklyn, NY  United States 
• Performing Organizations:

  Connected Cities for Smart Mobility towards Accessible and Resilient Transportation Center (C2SMART)

  New York University
  Tandon School of Engineering
  Brooklyn, NY  United States 
• Principal Investigators:

  Ozbay, Kaan

  Jiang, Zhong-Ping

• Start Date: 20210301
• Expected Completion Date: 20220830
• Actual Completion Date: 20220830
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Connected vehicles; Dynamic programming; Lane changing; Machine learning; Vehicle mix
• Subject Areas: Highways; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01768986
• Record Type: Research project
• Source Agency: Connected Cities for Smart Mobility towards Accessible and Resilient Transportation Center (C2SMART)
• Contract Numbers: USDOT 69A3551747124'
• Files: UTC, RIP
• Created Date: Apr 1 2021 7:53PM