用强化学习优化混行交通效率，适配各种道路拓扑。
Optimizing Efficiency of Mixed Traffic through Reinforcement Learning: A Topology-Independent Approach and Benchmark
- 基于无模型强化学习，利用自动驾驶车数据调控人工驾驶车辆。
- 在444个真实场景中表现优于现有方法，跨路口与环岛均有效。
- 首个真实世界混行交通基准，适合交通智能控制研究者使用。
本文提出一种混合交通控制策略，旨在优化不同道路拓扑下的交通效率，解决城市中普遍存在的拥堵问题。采用无模型强化学习（RL）方法管理大规模交通流，利用自动驾驶车辆采集的数据影响人类驾驶车辆的行为。同时发布了一个真实世界混合交通控制基准，包含来自20个国家的444个场景，覆盖广泛地理分布和多样化的交通情景与道路结构。该基准为未来研究提供真实模拟环境，支持高效策略的开发。全面实验表明，所提方法在交叉口和环岛场景中均显著优于现有交通控制方法。据我们所知，这是首个引入真实复杂场景混合交通控制基准的项目。视频与代码已公开于 https://sites.google.com/berkeley.edu/mixedtrafficplus/home。
原文摘要 · Abstract (English)
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
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