Development of a cooperative adaptive cruise control algorithm

The exclusive bus lane (XBL) is one of the most popular bus transit systems in US. The Lincoln Tunnel utilizes an XBL through the tunnel in the AM peak period. This project proposes a novel data-driven cooperative adaptive cruise control (CACC) algorithm for connected and autonomous bus along the XBL. Different from existing model-based CACC algorithms, the proposed approach employs the idea of reinforcement learning (RL), which does not rely on the accurate knowledge of bus dynamics.

Language

• English

Project

• Status: Completed
• Sponsor Organizations:

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

  New York University
  Tandon School of Engineering
  Brooklyn, NY  United States 

  Office of the Assistant Secretary for Research and Technology

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

  New York University Tandon School of Engineering

  6 Metrotech Center
  Brooklyn, NY  United States  11201
• Principal Investigators:

  Ozbay, Kaan

  Gao, Weinan

• Start Date: 20180101
• Expected Completion Date: 20181231
• Actual Completion Date: 20181231
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Adaptive control; Algorithms; Autonomous vehicles; Bus and high occupancy vehicle facilities; Bus lanes; Buses; Connected vehicles; Cruise control; Machine learning
• Subject Areas: Operations and Traffic Management; Public Transportation; Vehicles and Equipment;

Filing Info

• Accession Number: 01712948
• Record Type: Research project
• Source Agency: Connected Cities for Smart Mobility towards Accessible and Resilient Transportation Center (C2SMART)
• Files: UTC, RIP
• Created Date: Jul 30 2019 8:18AM