The increasing presence of autonomous vehicles (AVs) in urban environments introduces both opportunities and challenges, particularly regarding their interactions with traditional vehicles and other road users. This paper presents a comprehensive framework designed to assess the integration of AVs in mixed traffic scenarios. The framework is built upon real-world data collected from AV trials conducted in Turin, Italy. By leveraging traffic microsimulation along with machine learning techniques, the study proposes a framework aimed at assessing ex-ante the impacts on traffic of AVs introduction, thus constituting a relevant tool of virtual testing of CCAM (Cooperative, Connected, and Automated Mobility) trials before the physical introduction of autonomous vehicles on public roads. The integration of High-Performance Computing (HPC) ensures the efficiency of these simulations, enabling real-time analysis and testing. The proposed framework not only provides decision-makers with a tool for virtual testing of AV deployment, but also offers actionable insights into traffic management strategies. The study’s findings contribute to a deeper understanding of the role AVs can play in future urban mobility systems, particularly as cities prepare for the broader adoption of CCAM technologies.


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    Titel :

    A Data-Driven Integrated Framework for Virtual Testing of Autonomous Vehicles in Mixed Traffic Scenarios


    Weitere Titelangaben:

    Lect.Notes Social.Inform.


    Beteiligte:
    Kocian, Alexander (Herausgeber:in) / Milazzo, Paolo (Herausgeber:in) / Henriques Martins, Ana Lúcia (Herausgeber:in) / Nanni, Mirco (Herausgeber:in) / Pappalardo, Luca (Herausgeber:in) / Caroleo, Brunella (Autor:in) / Sadeghi, Javad (Autor:in) / Botta, Cristiana (Autor:in) / Nikneshan, Shadi (Autor:in) / Arnone, Maurizio (Autor:in)

    Kongress:

    International Conference on Intelligent Transport Systems ; 2024 ; Pisa, Italy December 05, 2024 - December 06, 2024



    Erscheinungsdatum :

    03.04.2025


    Format / Umfang :

    18 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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