Automated driving is one of the main drivers in the automotive industry. On the way to full automation current Advanced Driver Assistant Systems (ADAS) and Automated Driving Systems (ADS) backed by new and enhanced sensor systems take over more and more driving tasks. Developers and engineers are challenged with the increasing Operational Design Domain (ODD) of their systems. The application of these systems has become a daunting task, as a manifold of new situations has to be covered. The number of application parameters has skyrocketed and their scopes are intertwined and not always visible to the vehicle's driver. Virtual, simulation based approaches are on the rise to give developers another tool for the application of their systems. In order to retrieve valid evaluations from the simulation, current research focuses on objectifying the subjective passenger assessments that so far were only conceivable in real world driving tests. Furthermore, with objective and comparable simulation results on statistically significant data samples, application parameters whose effects are not clearly visible in real world driving tests can now be applied systematically and justified. We propose a process reference model for the virtual application of predictive control features with a clear focus on the quality and representativity of the virtual application.


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    Titel :

    A Process Reference Model for the Virtual Application of Predictive Control Features


    Beteiligte:
    Langner, Jacob (Autor:in) / Bauer, Kai-Lukas (Autor:in) / Holzapfel, Marc (Autor:in) / Sax, Eric (Autor:in)


    Erscheinungsdatum :

    2020-10-19


    Format / Umfang :

    361513 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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