Detecting the mental and physical states which occur in a driver immediately before a traffic accident and then providing information to or warning the driver is an effective means of reducing traffic accidents. This study is focused on driver distraction, a state which can easily lead to traffic accidents, and reproduced this distraction in a driving simulator by providing conversation or arithmetic tasks to the subjects. Stereo cameras were used as the means to track subjects’ eye and head movements. These movements were tracked and their standard deviations were set as classification features of pattern recognition, and the AdaBoost method was used to detect subject distraction. The interval between heart R-waves was also added as a classifier feature, in order to improve cognitive distraction detection performance. The results were then compared with the SVM method from the AIDE Project, which was carried out as part of the EU 6th Framework Programme.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Driver’s cognitive distraction detection using AdaBoost on pattern recognition basis


    Contributors:


    Publication date :

    2008-09-01


    Size :

    419926 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English