Virginia Tech
    • Log in
    View Item 
    •   VTechWorks Home
    • ETDs: Virginia Tech Electronic Theses and Dissertations
    • Masters Theses
    • View Item
    •   VTechWorks Home
    • ETDs: Virginia Tech Electronic Theses and Dissertations
    • Masters Theses
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Algorithms refinement and threshold determination for a drowsy driver detection system

    Thumbnail
    View/Open
    LD5655.V855_1995.F357.pdf (4.466Mb)
    Downloads: 120
    Date
    1995-12-05
    Author
    Fairbanks, Rollin J. III
    Metadata
    Show full item record
    Abstract
    Research conducted over the past three years in the Vehicle Analysis and Simulation Laboratory at Virginia Tech has resulted in the development and validation of algorithms for the detection of driver drowsiness. Specifically, the goal of the research has been to develop the best possible drowsiness-detection algorithms using measures that can be computed while a vehicle is in motion with minimal interference with the driver. The results of these studies, which have been previously reported, generally support the feasibility of drowsy-driver detection and indicate that further analysis and refinement of the algorithms is warranted. This thesis researches several methods of refining existing driver-status algorithms, the integration of driver-performance deterioration measures, and the selection of appropriate alarm thresholds to be used in test and evaluation study. The results of five algorithm optimization refinements are described. Chapter 2 reports that the elimination of outlier dependent measure data prior to algorithm development was found not to improve algorithm accuracy. Chapter 3 describes that the addition of cross product and squared terms to the algorithms did not provide consistent improvement in algorithm accuracy. Chapter 4 reports that, although time-on-task variables were found to have some improved capability, they did not consistently add to the accuracy of the algorithms.
    URI
    http://hdl.handle.net/10919/41746
    Collections
    • Masters Theses [19662]

    If you believe that any material in VTechWorks should be removed, please see our policy and procedure for Requesting that Material be Amended or Removed. All takedown requests will be promptly acknowledged and investigated.

    Virginia Tech | University Libraries | Contact Us
     

     

    VTechWorks

    AboutPoliciesHelp

    Browse

    All of VTechWorksCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    Log inRegister

    Statistics

    View Usage Statistics

    If you believe that any material in VTechWorks should be removed, please see our policy and procedure for Requesting that Material be Amended or Removed. All takedown requests will be promptly acknowledged and investigated.

    Virginia Tech | University Libraries | Contact Us