Exploring the use of LIDAR data from Autonomous Cars for Estimating Traffic Flow Parameters and Vehicle Trajectories

Autonomous vehicles are typically equipped with LIDAR or other similar sensors to detect obstacles in the surrounding environment. LIDAR also provides a means to detect and track other vehicles around the autonomous car. The main goal of this proposed study is to estimate traffic flow parameters along the path of the autonomous car from the point-cloud data generated by the LIDAR. The specific goals of the proposed research are: (1) Collect sample LIDAR data under different traffic conditions on freeways and urban arterials in Hampton Roads; (2) Develop algorithms to detect vehicles around a LIDAR-equipped car and classify them based on vehicle size; (3) Develop algorithms to track other vehicles while within the LIDAR range; and (4) Estimate macroscopic traffic flow parameters based on the detected vehicles along the path of the LIDAR-equipped vehicle. Expected benefits and impacts: (1) New algorithms and methods will be developed to extract traffic flow information from raw LIDAR data; and (2) The developed methods will make it possible to gather massive and detailed data on traffic flow and driving behavior (e.g., car following). This can benefit a variety of applications including microscopic simulation model development and calibration, safety studies, estimation of temporal and spatial traffic flow conditions, etc.

Language

• English

Project

• Status: Completed
• Funding: $200000
• Sponsor Organizations:

  Mid-Atlantic Transportation Sustainability Center

  University of Virginia
  Charlottesville, VA  United States 

  Old Dominion University

  Norfolk, VA  United States  23529

  Office of the Assistant Secretary for Research and Technology

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

  Mid-Atlantic Transportation Sustainability Center

  University of Virginia
  Charlottesville, VA  United States 
• Project Managers:

  Burden, Lindsay

• Performing Organizations:

  Old Dominion University

  Norfolk, VA  United States  23529
• Principal Investigators:

  Cetin, Mecit

• Start Date: 20151001
• Expected Completion Date: 0
• Actual Completion Date: 20170930

Subject/Index Terms

• TRT Terms: Algorithms; Detection and identification systems; Estimating; Intelligent vehicles; Laser radar; Traffic flow; Vehicle trajectories
• Geographic Terms: Hampton (Virginia)
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01659464
• Record Type: Research project
• Source Agency: Mid-Atlantic Transportation Sustainability Center
• Files: UTC, RIP
• Created Date: Feb 6 2018 5:25PM