AKurtc, Valentina large-scale naturalistic vehicle trajectory dataset from German highways called highD is used to investigate the car-following behavior of individual drivers. These data include trajectories of 1,10,000 vehicles with the total length of 16.5  h. Solving a nonlinear optimization problem, the Intelligent Driver Model is calibrated by minimizing the deviations between observed and simulated gaps, when following the prescribed leading vehicle. The averaged calibration error is 7.6%, which is a little bit lower compared to previous findings (NGSIM I-80). It can be explaind by the shorter highD trajectories, predominantly free flow traffic and good precision metrics of this dataset. The ratio between inter-driver and intra-driver variabilityInter-driver and intra-driver variability is inversigated by performing global and platoon calibrationGlobal and platoon calibration. Inter-driver variation accounts for a larger part of the calibration errors than intra-driver variation does.


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    Titel :

    The HighD Dataset: Is This Dataset Suitable for Calibration of Vehicular Traffic Models?


    Weitere Titelangaben:

    Springer Proceedings Phys.


    Beteiligte:
    Zuriguel, Iker (Herausgeber:in) / Garcimartin, Angel (Herausgeber:in) / Cruz, Raul (Herausgeber:in) / Kurtc, Valentina (Autor:in)

    Erschienen in:

    Traffic and Granular Flow 2019 ; Kapitel : 64 ; 523-529


    Erscheinungsdatum :

    17.11.2020


    Format / Umfang :

    7 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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