Traffic congestion is a persistent problem in our society. Previous methods for traffic control have proven futile in alleviating current congestion levels leading researchers to explore ideas with robot vehicles given the increased emergence of vehicles with different levels of autonomy on our roads. This gives rise to mixed traffic control, where robot vehicles regulate human-driven vehicles through reinforcement learning (RL). However, most existing studies use precise observations that require domain expertise and hand engineering for each road network's observation space. Additionally, precise observations use global information, such as environment outflow, and local information, i.e., vehicle positions and velocities. Obtaining this information requires updating existing road infrastructure with vast sensor environments and communication to potentially unwilling human drivers. We consider image observations, a modality that has not been extensively explored for mixed traffic control via RL, as the alternative: 1) images do not require a complete re-imagination of the observation space from environment to environment; 2) images are ubiquitous through satellite imagery, in-car camera systems, and traffic monitoring systems; and 3) images only require communication to equipment. In this work, we show robot vehicles using image observations can achieve competitive performance to using precise information on environments, including ring, figure eight, intersection, merge, and bottleneck. In certain scenarios, our approach even outperforms using precision observations, e.g., up to 8% increase in average vehicle velocity in the merge environment, despite only using local traffic information as opposed to global traffic information.
Intelligent Coordination among Multiple Traffic Intersections Using Multi-Agent Reinforcement Learning
Siemens Technology and Services Pvt. Ltd.（西门子技术服务私人有限公司）；International Institute of Information Technology Hyderabad（海得拉巴国际信息技术学院）；Indian Institute of Science Bengaluru（班加罗尔印度科学研究所）
Deep Reinforcement Learning in Lane Merge Coordination for Connected Vehicles
King’s College London（伦敦国王学院）；Orange Labs Services（奥兰治实验室服务公司）
在本文中，车道合并协调的框架，提出了利用一个集中的系统，连接的车辆。向道路上的联网车辆提供轨迹建议是基于交通导航器和数据融合作为主要组件。深度强化学习和数据分析用于预测联网车辆的轨迹建议，同时考虑未联网车辆的建议。结果突出了流量控制器的适应性，当在看不见的现实世界合并场景中使用Dueling Deep Q-Network时。最后，对不同的强化学习模型进行了性能比较，并对关键性能指标（KPI）进行了评价。
HALO: Hierarchical Reinforcement Learning for Large-Scale Adaptive Traffic Signal Control
University of Exeter Department of Computer Science Exeter UK（埃克塞特大学计算机科学系埃克塞特英国）；University of Warwick Department of Computer Science Coventry UK（沃里克大学计算机科学系科文特里英国）；University of Exeter（埃克塞特大学）；University of Warwick（沃里克大学）
C$^2$T: Captioning-Structure and LLM-Aligned Common-Sense Reward Learning for Traffic--Vehicle Coordination
The State Key Laboratory of Internet of Things for Smart City, University of Macau（物联网智能城市国家重点实验室，澳门大学）；Wuhan University（武汉大学）；Hong Kong Polytechnic University（香港理工大学）；Computer and Information Science, University of Macau（澳门大学计算机与信息科学）
Decentralized Coordination of Autonomous Traffic Through Advanced Air Mobility Corridors
Department of Aeronautics and Astronautics（航空航天系）；AIAA Student Member（美国航空航天学会学生会员）；Associate Dean of Engineering（工程副院长）；William E. Leonhard (1940) Professor（威廉·E·莱昂哈德（1940）教授）；AIAA Fellow（美国航空航天学会会士）
Decentralized traffic management of autonomous drones
Eötvös University of Budapest（布达佩斯厄特沃什大学）；MTA-ELTE Statistical and Biological Physics Research Group（匈牙利科学院-厄特沃什大学统计与生物物理研究组）；CollMot Robotics LLC（CollMot机器人有限责任公司）
Time-To-Reach Separation and Safety Filtering for Safe, Fair, and Efficient Multi-Agent Coordination
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley（加州大学伯克利分校电子工程与计算机科学系）；Department of Aeronautics and Astronautics, Massachusetts Institute of Technology（麻省理工学院航空与航天系）；GRASP Laboratory, University of Pennsylvania（宾夕法尼亚大学GRASP实验室）；Department of Electrical and Computer Engineering, University of California, Los Angeles（加州大学洛杉矶分校电子与计算机工程系）
VissimRL: A Multi-Agent Reinforcement Learning Framework for Traffic Signal Control Based on Vissim
Research Center for Information Technology Innovation, Academia Sinica（台湾中央研究院资讯科技创新研究中心）；Department of Computer Science, National Yang Ming Chiao Tung University（国立阳明交通大学计算机科学系）
