With the growing popularity of digital twin and autonomous driving in transportation, the demand for simulation systems capable of generating high-fidelity and reliable scenarios is increasing. Existing simulation systems suffer from a lack of support for different types of scenarios, and the vehicle models used in these systems are too simplistic. Thus, such systems fail to represent driving styles and multi-vehicle interactions, and struggle to handle corner cases in the dataset. In this paper, we propose LimSim, the Long-term Interactive Multi-scenario traffic Simulator, which aims to provide a long-term continuous simulation capability under the urban road network. LimSim can simulate fine-grained dynamic scenarios and focus on the diverse interactions between multiple vehicles in the traffic flow. This paper provides a detailed introduction to the framework and features of the LimSim, and demonstrates its performance through case studies and experiments. The source code of LimSim is available on GitHub: github.com/PJLab-ADG/LimSim.


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    Titel :

    LimSim: A Long-Term Interactive Multi-Scenario Traffic Simulator


    Beteiligte:
    Wenl, Licheng (Autor:in) / Fu, Daocheng (Autor:in) / Mao, Song (Autor:in) / Cai, Pinlong (Autor:in) / Dou, Min (Autor:in) / Li, Yikang (Autor:in) / Qiao, Yu (Autor:in)


    Erscheinungsdatum :

    24.09.2023


    Format / Umfang :

    4570114 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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