• DocumentCode
    1210923
  • Title

    A discriminating feature tracker for vision-based autonomous driving

  • Author

    Schneiderman, Henry ; Nashman, Marilyn

  • Author_Institution
    Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA
  • Volume
    10
  • Issue
    6
  • fYear
    1994
  • fDate
    12/1/1994 12:00:00 AM
  • Firstpage
    769
  • Lastpage
    775
  • Abstract
    A new vision-based technique for autonomous driving is described. This approach explicitly addresses and compensates for two forms of uncertainty: uncertainty about changes in road direction and uncertainty in the measurements of the road derived in each image. Autonomous driving has been demonstrated on both local roads and highways at speeds up to 100 km/h. The algorithm has performed well in the presence of non-ideal road conditions including gaps in the lane markers, sharp curves, shadows, cracks in the pavement, and wet roads. It has also performed well in rain, dark, and nighttime driving with headlights
  • Keywords
    feature extraction; mobile robots; path planning; robot vision; 100 km/h; cracks; dark; discriminating feature tracker; highways; local roads; nighttime driving; nonideal road conditions; rain; road direction; shadows; sharp curves; uncertainty; vision-based autonomous driving; wet roads; Cameras; Data mining; Least squares approximation; Measurement uncertainty; Rain; Recursive estimation; Road transportation; Robot vision systems; Robustness; Vehicles;
  • fLanguage
    English
  • Journal_Title
    Robotics and Automation, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1042-296X
  • Type

    jour

  • DOI
    10.1109/70.338531
  • Filename
    338531