Eco-vehicular edge networks for connected transportation: A distributed multi-agent reinforcement learning approach
This paper introduces an energy-efficient, software-defined vehicular edge network for the
growing intelligent connected transportation system. A joint user-centric virtual cell formation …
growing intelligent connected transportation system. A joint user-centric virtual cell formation …
Dynamic power allocation and virtual cell formation for Throughput-Optimal vehicular edge networks in highway transportation
This paper investigates highly mobile vehicular networks from users' perspectives in
highway transportation. Particularly, a centralized software-defined architecture is …
highway transportation. Particularly, a centralized software-defined architecture is …
Coordination for connected and automated vehicles at non-signalized intersections: A value decomposition-based multiagent deep reinforcement learning approach
Z Guo, Y Wu, L Wang, J Zhang - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The recent proliferation of the research on multi-agent deep reinforcement learning (MDRL)
offers an encouraging way to coordinate multiple connected and automated vehicles (CAVs) …
offers an encouraging way to coordinate multiple connected and automated vehicles (CAVs) …
Fedlight: Federated reinforcement learning for autonomous multi-intersection traffic signal control
Although Reinforcement Learning (RL) has been successfully applied in traffic control, it
suffers from the problems of high average vehicle travel time and slow convergence to …
suffers from the problems of high average vehicle travel time and slow convergence to …
HARL: A novel hierachical adversary reinforcement learning for automoumous intersection management
G Li, J Wu, Y He - arXiv preprint arXiv:2205.02428, 2022 - arxiv.org
As an emerging technology, Connected Autonomous Vehicles (CAVs) are believed to have
the ability to move through intersections in a faster and safer manner, through effective …
the ability to move through intersections in a faster and safer manner, through effective …
A survey on multi-agent reinforcement learning methods for vehicular networks
I Althamary, CW Huang, P Lin - 2019 15th International …, 2019 - ieeexplore.ieee.org
Under the rapid development of the Internet of Things (IoT), vehicles can be recognized as
mobile smart agents that communicating, cooperating, and competing for resources and …
mobile smart agents that communicating, cooperating, and competing for resources and …
A selective federated reinforcement learning strategy for autonomous driving
Currently, the complex traffic environment challenges the fast and accurate response of a
connected autonomous vehicle (CAV). More importantly, it is difficult for different CAVs to …
connected autonomous vehicle (CAV). More importantly, it is difficult for different CAVs to …
Federated multi-agent deep reinforcement learning for resource allocation of vehicle-to-vehicle communications
Dynamic topology, fast-changing channels and the time sensitivity of safety-related services
present challenges to the status quo of resource allocation for cellular-underlaying vehicle …
present challenges to the status quo of resource allocation for cellular-underlaying vehicle …
Next-generation edge computing assisted autonomous driving based artificial intelligence algorithms
Edge Computing and Network Function Virtualization (NFV) concepts can improve network
processing and multi-resources allocation when intelligent optimization algorithms are …
processing and multi-resources allocation when intelligent optimization algorithms are …
Federated learning for collaborative controller design of connected and autonomous vehicles
The deployment of future intelligent transportation systems is contingent upon seamless and
reliable operation of connected and autonomous vehicles (CAVs). One key challenge in …
reliable operation of connected and autonomous vehicles (CAVs). One key challenge in …