Automated driving functions are under intensified development by industry and academia since the last decade. Due to the large operation space and various complex scenarios automated driving functions have to cope with, assessment efforts are expected to rise dramatically. In order to quantify benefits and risks of these functions in an efficient way, this paper describes a holistic approach for the assessment of automated driving by using real world driving data. Based on a scenario definition a suitable method for identifying relevant scenarios from real world driving data is described which is able to handle scenario specific characteristics such as the temporal and spatial dependencies of all traffic participants. For quantifying the effect of automated driving within the considered driving scenarios, the statistical indicator ‘effect size’ is applied. The basic requirement that automated driving needs to operate within mixed traffic implies that the reference for assessment needs to be human manual driving behaviour.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A scenario-based assessment approach for automated driving by using time series classification of human-driving behaviour


    Beteiligte:
    Roesener, Christian (Autor:in) / Fahrenkrog, Felix (Autor:in) / Uhlig, Axel (Autor:in) / Eckstein, Lutz (Autor:in)


    Erscheinungsdatum :

    01.11.2016


    Format / Umfang :

    576220 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Scenario-Based Infrastructure Requirements for Automated Driving

    Lu, Xiaolin / Madadi, Bahman / Farah, Haneen et al. | ASCE | 2019


    Scenario-based approach for developing ADAS and automated driving functions

    Höfer, Andreas / Herrmann, Martin | Springer Verlag | 2017



    Highly immersive driving simulator for scenario based testing of automated driving functions

    Prokop, Günther / Tüschen, Thomas / Eisenköck, Norman et al. | Springer Verlag | 2022


    Highly immersive driving simulator for scenario based testing of automated driving functions

    Prokop, G. / Tüschen, T. / Eisenköck, N. et al. | British Library Conference Proceedings | 2022