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. LimSim is now open source on GitHub: https://www.github.com/PJLab-ADG/LimSim .

翻译：随着数字孪生与自动驾驶在交通领域的日益普及，对能够生成高保真、高可靠性场景的模拟系统的需求不断增长。现有模拟系统存在对多种场景类型支持不足的问题，且系统内置的车辆模型过于简化。因此，此类系统无法体现驾驶风格与多车交互特征，也难以处理数据集中的边缘场景。本文提出LimSim，即长期交互式多场景交通模拟器，旨在提供城市路网下的长时连续仿真能力。LimSim能够模拟细粒度动态场景，并聚焦交通流中多车辆间的多样交互行为。本文详细阐述了LimSim的框架与特性，并通过案例研究与实验验证其性能。LimSim现已开源于GitHub平台：https://www.github.com/PJLab-ADG/LimSim。