Joint velocity and spectrum optimization in urban air transportation system via multi-agent deep reinforcement learning

R Han, H Li, EJ Knoblock, MR Gasper… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The emerging concepts of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) open
a new paradigm for urban air transportation. One big challenge is that new aerial vehicles …

Deep reinforcement learning based resource allocation in delay-tolerance-aware 5G Industrial IoT systems

H Wang, Y Bai, X **e - IEEE Transactions on Communications, 2023 - ieeexplore.ieee.org
With the widespread application of 5G technology in the Industrial Internet of Things (IIoT),
dividing nodes into different network slices according to delay tolerance requirements can …

An adaptive route guidance model considering the effect of traffic signals based on deep reinforcement learning

Y Shi, Z Gu, X Yang, Y Li, Z Chu - IEEE Intelligent …, 2023 - ieeexplore.ieee.org
Navigation or route guidance systems are designed to provide drivers with real-time travel
information and the associated recommended routes for their trips. Classical route choice …

Noncooperative and cooperative urban intelligent systems: joint logistic and charging incentive mechanisms

S Zhang, X Zhu, S Wang, JQ James… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
Autonomous vehicles (AVs) have become an emerging crucial component of the intelligent
transportation system (ITS) in modern smart cities. In particular, coordinated operations of …

Dynamic and effect-driven output service selection for IoT environments using deep reinforcement learning

KD Baek, IY Ko - IEEE Internet of Things Journal, 2022 - ieeexplore.ieee.org
In the context of the recent emergence of the Internet of Things (IoT), human users and IoT-
based services are interacting via physical effects, such as light and sound. Therefore, it is …

Distributed Multi-Agent Reinforcement Learning for Collaborative Path Planning and Scheduling in Blockchain-Based Cognitive Internet of Vehicles

H Chang, Y Liu, Z Sheng - IEEE Transactions on Vehicular …, 2023 - ieeexplore.ieee.org
The collaborative path planning and scheduling can overcome the limitations of single
vehicle intelligence to obtain a globally optimal decision strategy in cognitive Internet of …

FedCruise: Collaborative Cruise Guidance With Federated Policy Distillation in Multiple Ride-Hailing Platforms

G Sun, GT Maale, H Zhang… - IEEE Internet of …, 2024 - ieeexplore.ieee.org
Recent technological advancements have led to the emergence of intelligent cruise
guidance systems tailored for ride-hailing platforms (RHPs) such as Uber, Didi Chuxing …

Large Vehicle Scheduling Based on Uncertainty Weighting Harmonic Twin-Critic Network

X Huang, K Yang, J Ling - IEEE Systems Journal, 2023 - ieeexplore.ieee.org
Large-scale online car-hailing platforms have greatly improved travel efficiency by
dispatching orders quickly. However, effectively scheduling vehicles for a large-scale fleet is …

D4: Dynamic, Decentralized, Distributed, Delegation-Based Network Control and Its Applications to Autonomous Vehicles

A Kaul, K Obraczka, R Fontes, T Turletti - Journal on Autonomous …, 2024 - dl.acm.org
Connected autonomous vehicles technology is expected to be an important component of
Intelligent Transportation Systems (ITS). With the help of artificial intelligence, cognitive …

Reinforcement learning assisted communication resources optimization in advanced air mobility.

R Han - 2024 - ir.library.louisville.edu
Advanced air mobility (AAM), which envisages a safe and efficient aviation transportation
system, has drawn significant attention to support the increasing mobility demand in …