Survey on artificial intelligence (AI) techniques for vehicular ad-hoc networks (VANETs)
A Mchergui, T Moulahi, S Zeadally - Vehicular Communications, 2022 - Elsevier
Advances in communications, smart transportation systems, and computer systems have
recently opened up vast possibilities of intelligent solutions for traffic safety, convenience …
recently opened up vast possibilities of intelligent solutions for traffic safety, convenience …
Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation
Traffic signal control is an important and challenging real-world problem that has recently
received a large amount of interest from both transportation and computer science …
received a large amount of interest from both transportation and computer science …
Deep reinforcement learning for intelligent transportation systems: A survey
Latest technological improvements increased the quality of transportation. New data-driven
approaches bring out a new research direction for all control-based systems, eg, in …
approaches bring out a new research direction for all control-based systems, eg, in …
Service offloading with deep Q-network for digital twinning-empowered internet of vehicles in edge computing
X Xu, B Shen, S Ding, G Srivastava… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
With the potential of implementing computing-intensive applications, edge computing is
combined with digital twinning (DT)-empowered Internet of vehicles (IoV) to enhance …
combined with digital twinning (DT)-empowered Internet of vehicles (IoV) to enhance …
Deep learning for intelligent transportation systems: A survey of emerging trends
M Veres, M Moussa - IEEE Transactions on Intelligent …, 2019 - ieeexplore.ieee.org
Transportation systems operate in a domain that is anything but simple. Many exhibit both
spatial and temporal characteristics, at varying scales, under varying conditions brought on …
spatial and temporal characteristics, at varying scales, under varying conditions brought on …
Multi-agent deep reinforcement learning for urban traffic light control in vehicular networks
As urban traffic condition is diverse and complicated, applying reinforcement learning to
reduce traffic congestion becomes one of the hot and promising topics. Especially, how to …
reduce traffic congestion becomes one of the hot and promising topics. Especially, how to …
Uncertainty-aware model-based reinforcement learning: Methodology and application in autonomous driving
To further improve learning efficiency and performance of reinforcement learning (RL), a
novel uncertainty-aware model-based RL method is proposed and validated in autonomous …
novel uncertainty-aware model-based RL method is proposed and validated in autonomous …
Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems
A Boukerche, Y Tao, P Sun - Computer networks, 2020 - Elsevier
In recent years, the Intelligent transportations system (ITS) has received considerable
attention, due to higher demands for road safety and efficiency in highly interconnected road …
attention, due to higher demands for road safety and efficiency in highly interconnected road …
Artificial intelligence for vehicle-to-everything: A survey
W Tong, A Hussain, WX Bo, S Maharjan - IEEE Access, 2019 - ieeexplore.ieee.org
Recently, the advancement in communications, intelligent transportation systems, and
computational systems has opened up new opportunities for intelligent traffic safety, comfort …
computational systems has opened up new opportunities for intelligent traffic safety, comfort …
[HTML][HTML] State-of-art review of traffic signal control methods: challenges and opportunities
Introduction Due to the menacing increase in the number of vehicles on a daily basis,
abating road congestion is becoming a key challenge these years. To cope-up with the …
abating road congestion is becoming a key challenge these years. To cope-up with the …