2022
DOI: 10.1109/tgcn.2022.3165262
|View full text |Cite
|
Sign up to set email alerts
|

Machine Learning-Driven APPs Recommendation for Energy Optimization in Green Communication and Networking for Connected and Autonomous Vehicles

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
1
0

Year Published

2022
2022
2023
2023

Publication Types

Select...
5
2

Relationship

0
7

Authors

Journals

citations
Cited by 8 publications
(4 citation statements)
references
References 35 publications
0
1
0
Order By: Relevance
“…Traffic signal control is a traffic control method that controls traffic signals and timing schemes by computer [4]. A traffic signal control system with good reliability and high stability has the advantages of high efficiency [5]. With the rapid development of artificial intelligence, urban traffic signal control scheme recommendations, traffic intersection flow prediction and traffic intersection spatiotemporal data analysis are increasingly popular directions [6].…”
Section: Introduction a Impact Of Road Congestionmentioning
confidence: 99%
“…Traffic signal control is a traffic control method that controls traffic signals and timing schemes by computer [4]. A traffic signal control system with good reliability and high stability has the advantages of high efficiency [5]. With the rapid development of artificial intelligence, urban traffic signal control scheme recommendations, traffic intersection flow prediction and traffic intersection spatiotemporal data analysis are increasingly popular directions [6].…”
Section: Introduction a Impact Of Road Congestionmentioning
confidence: 99%
“…Instead of depending on programming, its algorithm is learnt from a big volume of data [ 24 ]. Machine learning has been used for computer vision [ 25 ], face recognition [ 26 ], autonomous driving [ 27 , 28 ], auxiliary decision making [ 29 , 30 ], brain–machine interface [ 31 ], cancer diagnosis and assessment [ 32 ], and chess game [ 33 ]. It includes supervised learning, unsupervised learning, and reinforcement learning [ 34 ].…”
Section: Introductionmentioning
confidence: 99%
“…What DL approaches can be used to address energy efficiency challenges in AVs, such as optimizing power consumption, maximizing battery life, and minimizing energy waste during operations? [83] • What are the legal and regulatory challenges of integrating DL algorithms into AVs, including liability, safety regulations, and compliance with transportation laws? How can these challenges be addressed to facilitate the widespread adoption of DL technologies in AVs?…”
mentioning
confidence: 99%
“…DL techniques can be utilized to develop innovative algorithms that minimize energy waste during operation, leading to more sustainable and environmentally friendly AV systems. By harnessing the power of DL, researchers can drive advancements in energy-efficient technologies and pave the way for a greener future of transportation [83]. Tackling the legal and regulatory hurdles associated with integrating DL algorithms into AVs is paramount for their widespread adoption.…”
mentioning
confidence: 99%