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技术在交通领域的日益普及，对能够生成高保真、高可靠性场景的仿真系统的需求持续增长。现有仿真系统存在对多种场景类型支持不足的问题，且系统内所采用的车辆模型过于简化。因此，这类系统无法有效表征驾驶风格与多车交互行为，也难以处理数据集中存在的极端案例。本文提出LimSim（长时交互式多场景交通模拟器），旨在为城市路网环境下的长期连续仿真提供能力支撑。LimSim可模拟细粒度的动态场景，并聚焦于交通流中多个车辆间的多样化交互。本文详细介绍了LimSim的框架与特性，并通过案例研究与实验验证其性能表现。LimSim现已在GitHub上开源：https://www.github.com/PJLab-ADG/LimSim。