Comprehensive survey on machine learning in vehicular network: Technology, applications and challenges
Towards future intelligent vehicular network, the machine learning as the promising artificial
intelligence tool is widely researched to intelligentize communication and networking …
intelligence tool is widely researched to intelligentize communication and networking …
[HTML][HTML] Cybersecurity challenges in vehicular communications
Z El-Rewini, K Sadatsharan, DF Selvaraj… - Vehicular …, 2020 - Elsevier
As modern vehicles are capable to connect to an external infrastructure and Vehicle-to-
Everything (V2X) communication technologies mature, the necessity to secure …
Everything (V2X) communication technologies mature, the necessity to secure …
Future intelligent and secure vehicular network toward 6G: Machine-learning approaches
As a powerful tool, the vehicular network has been built to connect human communication
and transportation around the world for many years to come. However, with the rapid growth …
and transportation around the world for many years to come. However, with the rapid growth …
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 …
Machine learning for vehicular networks: Recent advances and application examples
The emerging vehicular networks are expected to make everyday vehicular operation safer,
greener, and more efficient and pave the path to autonomous driving in the advent of the fifth …
greener, and more efficient and pave the path to autonomous driving in the advent of the fifth …
Toward intelligent vehicular networks: A machine learning framework
As wireless networks evolve toward high mobility and providing better support for connected
vehicles, a number of new challenges arise due to the resulting high dynamics in vehicular …
vehicles, a number of new challenges arise due to the resulting high dynamics in vehicular …
Comprehensive survey of machine learning approaches in cognitive radio-based vehicular ad hoc networks
Nowadays, machine learning (ML), which is one of the most rapidly growing technical tools,
is extensively used to solve critical challenges in various domains. Vehicular ad hoc network …
is extensively used to solve critical challenges in various domains. Vehicular ad hoc network …
A machine learning approach to predict the k-coverage probability of wireless multihop networks considering boundary and shadowing effects
Network coverage is a pivotal performance metric of wireless multihop networks (WMNs)
determining the quality of service rendered by the network. Earlier, a few studies have …
determining the quality of service rendered by the network. Earlier, a few studies have …
Conditional sum-product networks: Imposing structure on deep probabilistic architectures
Probabilistic graphical models are a central tool in AI, however, they are generally not as
expressive as deep neural models, and inference is notoriously hard and slow. In contrast …
expressive as deep neural models, and inference is notoriously hard and slow. In contrast …
[HTML][HTML] Conditional sum-product networks: Modular probabilistic circuits via gate functions
While probabilistic graphical models are a central tool for reasoning under uncertainty in AI,
they are in general not as expressive as deep neural models, and inference is notoriously …
they are in general not as expressive as deep neural models, and inference is notoriously …