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Improved Daytime Detection of Pavement Markings with Machine Vision Cameras

  • The cover page of the white paper shows vehicles driving on a multi-lane highway with clearly visible pavement markings.

    Learn how increasing the reflectivity and contrast on pavement markings can improve autonomous vehicle lane keeping and help reduce the risk of traffic accidents.

    Who should read this white paper:
     

    • Department of Transportation (DOT) Project Managers
    • Intelligent Transportation System (ITS) Program Engineers
    • DOT Traffic Research Directors
    • Connected and Autonomous Vehicles (CAV) Research Program Managers

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  • Key Takeaways:

    • Automotive machine vision camera systems commonly rely on edge detection schemes to locate pavement markings—enabling lane departure warning and lane keeping for advanced driver assistance systems (ADAS), as well as autonomous driving functions.
    • Edge detection algorithms perform a function known as thresholding. In this process, the algorithm looks for pixel intensity gradients (contrast) that are above a set threshold.
    • It’s possible to increase the Weber contrast of a pavement marking, and the representative contrast gradient based on a Sobel operator, by increasing the luminance of the marking over a range of conditions and by placing a low luminance contrast stripe adjacent to the pavement marking.
    • A contrast stripe adjacent to a white marking reduces dependence on illumination levels and enables higher Sobel contrast gradient values for darker (e.g. soiled, aged) markings.
    • The width of a pavement marking and contrast stripe can be optimized for a given camera field-of-view, focal length, sensor size, and pixel density.
    • Optimizing the luminance values, contrast, and size of pavement markings will enable more robust lane detection.

     

    Who should read this:
     

    • Department of Transportation (DOT) Project Managers
    • Intelligent Transportation System (ITS) Program Engineers
    • DOT Traffic Research Directors
    • Connected and Autonomous Vehicles (CAV) Research Program Managers
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Click here to access the video about How Machine Vision Reads Pavement Markings.
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Machine Vision Camera Detection of Pavement Markings
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