Labeling Roads with Different Types of Automated Driving Functional Requirements using Machine Learning

The project aims to label roads with different types of automated driving functional requirements to safety deploy automated vehicles in variant communities regarding road types, geometries, lighting facilities, and human behaviors. Novel unsupervised learning approaches will be developed to synthesize the dynamic heterogeneous data which are being collected and analyzed by the vehicle platform and the infrastructure in PI’s projects supported by Toyota, Bosch, and Uber. Outputs are reports/suggestions to the city council and open datasets/tools for the industry partners.

Language

  • English

Project

  • Status: Active
  • Funding: $90000
  • Contract Numbers:

    69A3551747111

  • Sponsor Organizations:

    Carnegie Mellon University

    Mobility21 National USDOT UTC for Mobility of Goods and People
    Pittsburgh, PA  United States  15213

    Office of the Assistant Secretary for Research and Technology

    University Transportation Center Program
    ,    
  • Managing Organizations:

    Carnegie Mellon University

    Mobility21 National USDOT UTC for Mobility of Goods and People
    Pittsburgh, PA  United States  15213
  • Project Managers:

    Kline, Robin

  • Performing Organizations:

    Carnegie Mellon University

    ,    
  • Principal Investigators:

    Zhao, Ding

  • Start Date: 20190701
  • Expected Completion Date: 20200630
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01710714
  • Record Type: Research project
  • Source Agency: National University Transportation Center for Improving Mobility (Mobility21)
  • Contract Numbers: 69A3551747111
  • Files: UTC, RiP
  • Created Date: Jul 11 2019 10:11AM