Graphical abstract

    HighlightsUsing high-resolution vehicle trajectories, unsafe driving behaviors was identified.A Discrete-Fourier Transform was used to find crashes and near-crashes in NDS data.K-Means Clusters correctly identified 78% of the events in this study.Applicable for a variety of purposes in both the private and public sector.

    AbstractRecent technological advances have made it both feasible and practical to identify unsafe driving behaviors using second-by-second trajectory data. Presented in this paper is a unique approach to detecting safety-critical events using vehicles’ longitudinal accelerations. A Discrete Fourier Transform is used in combination with K-means clustering to flag patterns in the vehicles’ accelerations in time-series that are likely to be crashes or near-crashes. The algorithm was able to detect roughly 78% of crasjavascript:void(0)hes and near-crashes (71 out of 91 validated events in the Naturalistic Driving Study data used), while generating about 1 false positive every 2.7h. In addition to presenting the promising results, an implementation strategy is discussed and further research topics that can improve this method are suggested in the paper.


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    Titel :

    Identification of safety-critical events using kinematic vehicle data and the discrete fourier transform


    Beteiligte:
    Kluger, Robert (Autor:in) / Smith, Brian L. (Autor:in) / Park, Hyungjun (Autor:in) / Dailey, Daniel J. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2016-08-04


    Format / Umfang :

    7 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






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