| |||||||
ShanghaiTech University Knowledge Management System
| Optimizing Efficiency of Mixed Traffic through Reinforcement Learning: A Topology-Independent Approach and Benchmark | |
| 2025-01-28 | |
| 状态 | 已发表 |
| 摘要 | This paper presents a mixed traffic control policy designed to optimize traffic efficiency across diverse road topologies, addressing issues of congestion prevalent in urban environments. A model-free reinforcement learning (RL) approach is developed to manage large-scale traffic flow, using data collected by autonomous vehicles to influence human-driven vehicles. A real-world mixed traffic control benchmark is also released, which includes 444 scenarios from 20 countries, representing a wide geographic distribution and covering a variety of scenarios and road topologies. This benchmark serves as a foundation for future research, providing a realistic simulation environment for the development of effective policies. Comprehensive experiments demonstrate the effectiveness and adaptability of the proposed method, achieving better performance than existing traffic control methods in both intersection and roundabout scenarios. To the best of our knowledge, this is the first project to introduce a real-world complex scenarios mixed traffic control benchmark. Videos and code of our work are available at https://sites.google.com/berkeley.edu/mixedtrafficplus/home |
| 语种 | 英语 |
| DOI | arXiv:2501.16728 |
| 相关网址 | 查看原文 |
| 出处 | Arxiv |
| 收录类别 | PPRN.PPRN |
| WOS记录号 | PPRN:120980759 |
| WOS类目 | Computer Science, Artificial Intelligence |
| 文献类型 | 预印本 |
| 条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/507028 |
| 专题 | 信息科学与技术学院 信息科学与技术学院_本科生 信息科学与技术学院_PI研究组_马月昕 |
| 通讯作者 | Xiao, Chuyang |
| 作者单位 | 1.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China 2.Univ Hong Kong, Dept Comp Sci & TransGP, Pokfulam, Hong Kong, Peoples R China 3.Univ Hong Kong, Dept Civil Engn, Pokfulam, Hong Kong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xiao, Chuyang,Wang, Dawei,Tang, Xinzheng,et al. Optimizing Efficiency of Mixed Traffic through Reinforcement Learning: A Topology-Independent Approach and Benchmark. 2025. |
| 条目包含的文件 | ||||||
| 条目无相关文件。 | ||||||
修改评论
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。