Robust Automatic Detection of Traffic Activity from Vehicle Pespectives

The accurate detection and prediction of actions by multiple traffic participants such as pedestrians, vehicles, cyclists and others is a critical prerequisite for enabling self driving vehicles to make autonomous decisions. Current approaches to teach an autonomous vehicle how to drive use reinforcement learning which is essentially relies on already collected situations as examples relying purely on visual similarity without any understanding of the semantics of the situation and therefore no ability to reason about other similar situations that may have different appearance. This can be overcome by methods that provide situation awareness to the vehicle. The idea is to enable semantically meaningful representations of road scenarios which include the physical layout of the scene, the various participants prior and current activities. The ability to abstract this semantic representation and apply it to multiple scenes that are conceptually similar allows much more robust decision-making strategies by autonomous vehicles. Essentially this allows endowing autonomous vehicles with a reasoning process.

Language

  • English

Project

  • Status: Active
  • Funding: $146000
  • 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:

    Hauptmann, Alex

  • Start Date: 20220701
  • Expected Completion Date: 20230630
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01848336
  • Record Type: Research project
  • Source Agency: National University Transportation Center for Improving Mobility (Mobility21)
  • Contract Numbers: 69A3551747111
  • Files: UTC, RIP
  • Created Date: Jun 10 2022 12:53PM