CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles using Deep Reinforcement Learning
The target of reducing travel time only is insufficient to support the development of future smart transportation systems. To align with the United Nations Sustainable Development Goals (UN-SDG), a further reduction of fuel and emissions, improvements of traffic safety, and the ease of infrastructure deployment and maintenance should also be considered. Different from existing work focusing on the optimization of the control in either traffic light signal (to improve the intersection throughput), or vehicle speed (to stabilize the traffic), this paper presents a multi-agent Deep Reinforcement Learning (DRL) system called CoTV, which Cooperatively controls both Traffic light signals and Connected Autonomous Vehicles (CAV). Therefore, our CoTV can well balance the achievement of the reduction of travel time, fuel, and emissions. In the meantime, CoTV can also be easy to deploy by cooperating with only one CAV that is the nearest to the traffic light controller on each incoming road. This enables more efficient coordination between traffic light controllers and CAV, thus leading to the convergence of training CoTV under the large-scale multi-agent scenario that is traditionally difficult to converge. We give the detailed system design of CoTV and demonstrate its effectiveness in a simulation study using SUMO under various grid maps and realistic urban scenarios with mixed-autonomy traffic.
Code (1)
Tasks
Autonomous VehiclesDeep Reinforcement LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
CoLLMLight: Cooperative Large Language Model Agents for Network-Wide Traffic Signal Control
Traffic Signal Control (TSC) plays a critical role in urban traffic management by optimizing traffic flow and mitigating congestion. While Large Language Models (LLMs) have recently emerged as promising tools for TSC due…
Computational EfficiencyLanguage ModelingLanguage ModellingLarge Language Model+1Learning to Help Emergency Vehicles Arrive Faster: A Cooperative Vehicle-Road Scheduling Approach
The ever-increasing heavy traffic congestion potentially impedes the accessibility of emergency vehicles (EVs), resulting in detrimental impacts on critical services and even safety of people's lives. Hence, it is signif…
Graph AttentionSchedulingTraffic Signal ControlSTMARL: A Spatio-Temporal Multi-Agent Reinforcement Learning Approach for Cooperative Traffic Light Control
The development of intelligent traffic light control systems is essential for smart transportation management. While some efforts have been made to optimize the use of individual traffic lights in an isolated way, relate…
Graph Neural NetworkManagementMulti-agent Reinforcement LearningQ-Learning+1Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems
Autonomous vehicles (AVs) are becoming increasingly popular, with their applications now extending beyond just a mode of transportation to serving as mobile actuators of a traffic flow to control flow dynamics. This cont…
Reinforcement LearningAutonomous VehiclesA Multi-intersection Vehicular Cooperative Control based on End-Edge-Cloud Computing
Cooperative Intelligent Transportation Systems (C-ITS) will change the modes of road safety and traffic management, especially at intersections without traffic lights, namely unsignalized intersections. Existing research…
Cloud ComputingManagement