DCL-AIM: Decentralized coordination learning of autonomous intersection management for connected and automated vehicles
Conventional intersection managements, such as signalized intersections, may not
necessarily be the optimal strategies when it comes to connected and automated vehicles …
necessarily be the optimal strategies when it comes to connected and automated vehicles …
Centralized cooperation for connected and automated vehicles at intersections by proximal policy optimization
Connected vehicles will change the modes of future transportation management and
organization, especially at an intersection without traffic light. Centralized coordination …
organization, especially at an intersection without traffic light. Centralized coordination …
CVLight: Decentralized learning for adaptive traffic signal control with connected vehicles
This paper develops a decentralized reinforcement learning (RL) scheme for multi-
intersection adaptive traffic signal control (TSC), called “CVLight”, that leverages data …
intersection adaptive traffic signal control (TSC), called “CVLight”, that leverages data …
Leveraging multiagent learning for automated vehicles scheduling at nonsignalized intersections
Recent advancements of Vehicle-to-Everything (V2X) communication combined with
artificial intelligence (AI) technologies have shown enormous potentials for improving traffic …
artificial intelligence (AI) technologies have shown enormous potentials for improving traffic …
Multi-agent deep reinforcement learning to manage connected autonomous vehicles at tomorrow's intersections
GP Antonio, C Maria-Dolores - IEEE Transactions on Vehicular …, 2022 - ieeexplore.ieee.org
In recent years, the growing development of Connected Autonomous Vehicles (CAV),
Intelligent Transport Systems (ITS), and 5G communication networks have led to the advent …
Intelligent Transport Systems (ITS), and 5G communication networks have led to the advent …
Reinforcement learning-based multi-agent system for network traffic signal control
A challenging application of artificial intelligence systems involves the scheduling of traffic
signals in multi-intersection vehicular networks. This paper introduces a novel use of a multi …
signals in multi-intersection vehicular networks. This paper introduces a novel use of a multi …
A deep reinforcement learning-based cooperative approach for multi-intersection traffic signal control
Abstract Recently, Adaptive Traffic Signal Control (ATSC) in the multi-intersection system is
considered as one of the most critical issues in Intelligent Transportation Systems (ITS) …
considered as one of the most critical issues in Intelligent Transportation Systems (ITS) …
A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework
This study develops a novel reinforcement learning algorithm for the challenging
coordinated signal control problem. Traffic signals are modeled as intelligent agents …
coordinated signal control problem. Traffic signals are modeled as intelligent agents …
An agent-based learning towards decentralized and coordinated traffic signal control
S El-Tantawy, B Abdulhai - 13th International IEEE conference …, 2010 - ieeexplore.ieee.org
Adaptive traffic signal control is a promising technique for alleviating traffic congestion.
Reinforcement Learning (RL) has the potential to tackle the optimal traffic control problem for …
Reinforcement Learning (RL) has the potential to tackle the optimal traffic control problem for …
[HTML][HTML] Decentralized network level adaptive signal control by multi-agent deep reinforcement learning
Adaptive traffic signal control systems are deployed to accommodate real-time traffic
conditions. Yet travel demand and behavior of the individual vehicles might be overseen by …
conditions. Yet travel demand and behavior of the individual vehicles might be overseen by …