Connected and automated technologies poised to change the way vehicles operate are starting to enter the mainstream market. Methods to accurately evaluate these technologies, in particular for their impact on safety and energy, are complex due to the influence of static and environmental factors, such as road environment and traffic scenarios. Therefore, it is important to develop modeling and testing frameworks that can support the development of complex vehicle functionalities in a realistic environment. Microscopic traffic simulations have been increasingly used to assess the performance of connected and automated vehicle technologies in traffic networks. In this paper, we propose and apply an evaluation method based on a combination of microscopic traffic simulation (AIMSUN) and a chassis dynamometer-based vehicle-in-the-loop environment, developed at Argonne National Laboratory. This method is applied to analyze the impact of surrounding traffic on an eco-driving vehicle approaching an intersection with a traffic light. The eco-driving strategy is applied on a vehicle driven on the chassis-dynamometer while its behavior is mirrored in the traffic microsimulation environment. The energy consumption impact for the vehicle under test is evaluated for different levels of surrounding traffic density. The experiment highlights the impact of traffic on the eco-approach performance, but also the need to expand the current testing environment to include real-world scenarios in the energy evaluation of automated technologies.


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    Titel :

    Microsimulation-Based Evaluation of an Eco-Approach Strategy for Automated Vehicles Using Vehicle-in-the-Loop


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Stutenberg, Kevin (Autor:in) / Di Russo, Miriam (Autor:in) / Iliev, Simeon (Autor:in) / Crain, Trevor (Autor:in)

    Kongress:

    SAE WCX Digital Summit ; 2021



    Erscheinungsdatum :

    2021-04-06




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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