Abstract This paper proposes a methodology to estimate uncertainties in automated vehicle (AV) dynamics in real time via Bayesian inference. Based on the estimated uncertainty, the method aims to track the car-following (CF) performance of the AV to support strategic actions to maintain desired performance. Our methodology consists of three sequential components: (i) the Stochastic Gradient Langevin Dynamics (SGLD) is adopted to estimate parameter uncertainty relative to vehicular dynamics in real time, (ii) dynamic monitoring of car-following stability (local and string-wise), and (iii) strategic actions for control adjustment if anomaly is detected. The proposed methodology provides means to gauge AV car-following performance in real time and preserve desired performance against real time uncertainty that are unaccounted for in the vehicle control algorithm.

    Highlights Real-time car-following (CF) performance monitoring framework is developed. Probabilistic approach is adopted for uncertainty estimation in lower-level controller. Strategic adjustments are offered for performance loss in CF behavior.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A strategic approach to handle performance uncertainties in autonomous vehicle’s car-following behavior


    Beteiligte:
    Kontar, Wissam (Autor:in) / Ahn, Soyoung (Autor:in)


    Erscheinungsdatum :

    2024-01-29




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch







    Development and classification of autonomous vehicle’s ambiguous driving scenario

    Baby, Tiju / Ippoliti, Hatice Şahin / Wintersberger, Philipp et al. | Elsevier | 2024


    VEHICLE'S BRAKE

    KIM DONG GUEN | Europäisches Patentamt | 2016

    Freier Zugriff