用大模型一键生成城市交通仿真场景，无需编程经验。
ChatSUMO: Large Language Model for Automating Traffic Scenario Generation in Simulation of Urban MObility
- 通过自然语言输入生成道路网络与交通条件，自动调用SUMO仿真
- 在阿尔巴尼市真实场景验证中达到96%精度，支持自定义编辑
- 适合交通规划、智能驾驶研究者快速构建仿真环境
大型语言模型（LLM）能处理文本、语音、图像和视频等多模态信息，正在改变信息处理方式。本文提出ChatSUMO，一个基于大模型的智能代理，可集成于广泛使用的交通仿真平台SUMO，实现抽象与真实世界交通场景的自动化生成。用户通过自然语言输入，系统将提取关键词并执行Python脚本，完成从指定区域到坐标转换、从OpenStreetMap获取数据、构建道路网络，并运行带有指定交通条件的SUMO仿真。仿真输出由大模型解析，生成可读性强的对比与总结报告。用户可持续交互，生成多样化定制化场景，无需交通仿真专业知识。实验中，针对阿尔巴尼市的真实场景生成准确率达96%。系统还支持用户自定义路网编辑、信号灯优化与车辆参数调整。
原文摘要 · Abstract (English)
Large Language Models (LLMs), capable of handling multi-modal input and outputs such as text, voice, images, and video, are transforming the way we process information. Beyond just generating textual responses to prompts, they can integrate with different software platforms to offer comprehensive solutions across diverse applications. In this paper, we present ChatSUMO, a LLM-based agent that integrates language processing skills to generate abstract and real-world simulation scenarios in the widely-used traffic simulator - Simulation of Urban MObility (SUMO). Our methodology begins by leveraging the LLM for user input which converts to relevant keywords needed to run python scripts. These scripts are designed to convert specified regions into coordinates, fetch data from OpenStreetMap, transform it into a road network, and subsequently run SUMO simulations with the designated traffic conditions. The outputs of the simulations are then interpreted by the LLM resulting in informative comparisons and summaries. Users can continue the interaction and generate a variety of customized scenarios without prior traffic simulation expertise. For simulation generation, we created a real-world simulation for the city of Albany with an accuracy of 96\%. ChatSUMO also realizes the customizing of edge edit, traffic light optimization, and vehicle edit by users effectively.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。