This paper is an extension of our previous work on the CHASE (Classification by Holistic Analysis of Scene Environment) algorithm, that automatically classifies the driving complexity of a road scene image during day-time conditions and assigns it an ‘Ease of Driving’ (EoD) score. At night, apart from traffic variations and road type conditions, illumination changes are a major predominant factor that affect the road visibility and the driving easiness. In order to resolve the problem of analyzing the driving complexity of roads at night, a brightness detection module is incorporated in our end-to-end nighttime EoD system, which computes the ‘brightness factor’ (bright or dark) for that given night-time road scene. The brightness factor along with a multi-level machine learning classifier is then used to classify the EoD score for a night-time road scene. Our end-to-end ‘Night-time EoD system’ is a real-time onboard system implemented and tested on road scene data collected in Japan. We have improved the scope of the CHASE algorithm for computing EoD score for night-time driving conditions by including a brightness detection module.


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    Title :

    “Ease of Driving” Road Classification for Night-time Driving Conditions


    Additional title:

    Sae Technical Papers


    Contributors:

    Conference:

    SAE 2016 World Congress and Exhibition ; 2016



    Publication date :

    2016-04-05




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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