Recent 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




    Erscheinungsdatum :

    2016




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch



    Klassifikation :

    BKL:    44.80 / 44.80 Unfallmedizin, Notfallmedizin / 55.84 / 55.24 / 55.84 Straßenverkehr / 55.24 Fahrzeugführung, Fahrtechnik



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

    Kluger, Robert / Smith, Brian L. / Park, Hyungjun et al. | Elsevier | 2016



    Parallelization of the Hypercomplex Discrete Fourier Transform

    Aliev, M. V. / Belov, A. M. / Yershov, A. V. et al. | British Library Online Contents | 2005


    The Fourier Transform Applied to Vehicle Exterior Noise Source Identification

    Veres, R. E. / Daniels, V. A. | SAE Technical Papers | 1976


    The Fourier transform applied to vehicle exterior noise source identification

    Daniels,V.A. / Veres,R.E. / Ford Motor,US | Kraftfahrwesen | 1976