Sensory Augmentation for Increased Awareness of Driving Environment

The goals of this project are to extend the state of the art of vehicle perception systems for use in roadway traffic and develop systems that can model and predict the actions of multiple simultaneous road users in order to identify potentially hazardous situations before they turn into accidents. The project proposes augmenting vehicles with sensors and processing capabilities to perceive obstacles (both static and dynamic), predict how those obstacles might move over time, identify locations where unseen hazards might appear, and continually evaluate these values to determine the possibility that an unsafe condition might occur in the immediate future. While the Urban Challenge focused on fully autonomous vehicles, similar perception systems could also be deployed in manually-driven cars that could alert the human driver if an unsafe road condition is approaching. The project uses behavioral models of traffic to identify the perceived intent of nearby vehicles, use those intent models to predict the most likely future positions of those vehicles and determine whether a potentially unsafe condition may arise in the near future.


    • English


    • Status: Active
    • Sponsor Organizations:

      Research and Innovative Technology Administration

      University Transportation Centers Program
      1200 New Jersey Avenue
      Washington, DC  United States  20590
    • Performing Organizations:

      Technologies for Safe and Efficient Transportation University Transportation Center

      Carnegie Mellon University
      Pittsburgh, PA  United States  15213
    • Principal Investigators:

      Rybski, Paul

    • Start Date: 20120611
    • Expected Completion Date: 0
    • Actual Completion Date: 0
    • Source Data: RiP Project 31801

    Subject/Index Terms

    Filing Info

    • Accession Number: 01464949
    • Record Type: Research project
    • Source Agency: Technologies for Safe and Efficient Transportation University Transportation Center
    • Files: RiP, USDOT
    • Created Date: Jan 3 2013 2:53PM