An Airborne Lidar Scanning and Deep Learning System for Real-time Event Extraction and Control Policies in Urban Transportation Networks

The project team is currently investigating the capability to provide transportation and mobility solutions driven by real-time data generated from UAS using lidar and event identification through deep learning. Specific project tasks include: 1) developing optimal UAS-based lidar acquisition methodologies (payloads, sensor settings, and processing strategies) for transportation network scanning; 2) designing, implementing, and testing a deep learning algorithm that can extract features from the UAS lidar data, and 3) developing guidelines for state DOTs and other transportation agencies on the technical and operational requirements for UAS-based lidar data integration. The OSU project team recently integrated a Velodyne Puck lidar system and OxTS xNAV direct-georeferencing system on a DJI S1000 remote aircraft and have conducted test flights under an FAA-issued Certificate of Authorization (COA). Next steps will include working with ODOT to identify project sites to scan with the UAS-based lidar and transmitting the data to the UI project partners for implementing and testing the deep learning algorithms.

    Project

    • Status: Active
    • Funding: $180000
    • Contract Numbers:

      69A3551747110

    • Sponsor Organizations:

      Pacific Northwest Transportation Consortium

      University of Washington
      More Hall Room 112
      Seattle, WA  United States  98195-2700

      Office of the Assistant Secretary for Research and Technology

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

      Oregon State University, Corvallis

      Department of Civil Engineering
      202 Apperson Hall
      Corvallis, OR  United States  97331-2302
    • Project Managers:

      Parrish, Christopher

    • Performing Organizations:

      Oregon State University, Corvallis

      Department of Civil Engineering
      202 Apperson Hall
      Corvallis, OR  United States  97331-2302

      University of Idaho, Moscow

      Department of Civil Engineering
      P.O. Box 441022
      Moscow, ID  United States  83844-1022
    • Principal Investigators:

      Parrish, Christopher

      Sorour, Sameh

      Abdel-Rahim, Ahmed

      Hurwitz, David

    • Start Date: 20170816
    • Expected Completion Date: 20190815
    • Actual Completion Date: 0
    • USDOT Program: University Transportation Centers Program

    Subject/Index Terms

    Filing Info

    • Accession Number: 01701469
    • Record Type: Research project
    • Source Agency: Pacific Northwest Transportation Consortium
    • Contract Numbers: 69A3551747110
    • Files: UTC, RiP
    • Created Date: Apr 5 2019 3:19PM