Innovation in intelligent transportation systems relies on analysis of high-quality data. In this paper, we describe the design principles behind our data management infrastructure. The principles we adopt place an emphasis on flexibility and maintainability. This is achieved by breaking up code into a modular design that can be run on many independent processes. Message passing over a publish-subscribe network enables interprocess communication and promotes data-driven execution. By following these principles, rapid prototyping and experimentation with new sensing modalities and algorithms are possible. The communication library underpinning our proposed architecture is compared against several popular communication libraries. Features designed into the system make it decentralized, robust to failure, and amenable to scaling across multiple machines with minimal configuration. Code written using the proposed architecture is compact, transparent, and easy to maintain. Experimentation shows that our proposed architecture offers a high performance when compared against alternative communication libraries.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Flexible System Architecture for Acquisition and Storage of Naturalistic Driving Data




    Erscheinungsdatum :

    2016




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch



    Klassifikation :

    BKL:    55.84 / 55.24 / 55.84 Straßenverkehr / 55.24 Fahrzeugführung, Fahrtechnik



    A Flexible System Architecture for Acquisition and Storage of Naturalistic Driving Data

    Bender, Asher / Ward, James R. / Worrall, Stewart et al. | IEEE | 2016


    Analysis of Naturalistic Driving Data

    Shankar, Venky / Jovanis, Paul P. / Aguero-Valverde, Jonathan et al. | Transportation Research Record | 2008


    Driving Style Clustering using Naturalistic Driving Data

    Chen, Kuan-Ting / Chen, Huei-Yen Winnie | Transportation Research Record | 2019


    A big data-as-a-service architecture for naturalistic driving studies

    Alam, Md Rakibul / Al Haddad, Christelle / Antoniou, Constantinos et al. | IEEE | 2021


    Naturalistic Driving Study: Field Data Collection

    Blatt A. / Pierowicz J. / Flanigan M. et al. | NTIS | 2015