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

Xu, Yueshen; Lin, Jun-Wei; Gao, Honghao; Li, Rui; Jiang, Zhiping; Yin, Yuyu; Wu, Yinchen

doi:10.1109/tgcn.2022.3165262

Cited by 8 publications

(4 citation statements)

References 35 publications

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“…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%

A Collaborative Control Scheme for Smart Vehicles Based on Multi-Agent Deep Reinforcement Learning

Shi,

Chen

2023

IEEE Access

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With the development of artificial intelligence and autonomous driving technology, the vehicle-road cooperative control system combined with artificial intelligence technology can provide more effective and adaptive traffic control solutions for intelligent transportation systems. Existing research works are confronted with two kinds of challenges. For one thing, traditional recurrent neural networks-based methods cannot model the long-time dependent information in traffic flow sequences. For another, the large sample correlation makes it difficult to optimize the trained strategies. In this paper, we propose a Multi-agent Deep Reinforcement Learning (MADRL)-based intelligent vehicle cooperative control method to deal remedy current gaps. To this end, a closed-loop control system of self-driving vehicles and signal controllers is used as the research object to achieve dynamic scheduling of traffic flow by MADRL. After designing relevant experimental validation, the feasibility of the method is verified in terms of both scheme comparison and operational effect analysis, which is a good aid to traffic signal timing. The simulation results show that the proposal can be well utilized to realize collaborative control of smart vehicles, and there is some performance improvement compared with several typical methods.INDEX TERMS collaborative control; smart vehicles; deep reinforcement learning; intelligent transportation systems

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Section: Introduction a Impact Of Road Congestionmentioning

confidence: 99%

A Collaborative Control Scheme for Smart Vehicles Based on Multi-Agent Deep Reinforcement Learning

Shi,

Chen

2023

IEEE Access

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“…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%

Machine Learning Techniques for Effective Pathogen Detection Based on Resonant Biosensors

Rong,

Xu,

Sawan

2023

Biosensors

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We describe a machine learning (ML) approach to processing the signals collected from a COVID-19 optical-based detector. Multilayer perceptron (MLP) and support vector machine (SVM) were used to process both the raw data and the feature engineering data, and high performance for the qualitative detection of the SARS-CoV-2 virus with concentration down to 1 TCID50/mL was achieved. Valid detection experiments contained 486 negative and 108 positive samples, and control experiments, in which biosensors without antibody functionalization were used to detect SARS-CoV-2, contained 36 negative samples and 732 positive samples. The data distribution patterns of the valid and control detection dataset, based on T-distributed stochastic neighbor embedding (t-SNE), were used to study the distinguishability between positive and negative samples and explain the ML prediction performance. This work demonstrates that ML can be a generalized effective approach to process the signals and the datasets of biosensors dependent on resonant modes as biosensing mechanism.

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“…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%

Deep Learning and Autonomous Vehicles: Strategic Themes, Applications, and Research Agenda Using SciMAT and Content-Centric Analysis, a Systematic Review

Morooka,

Junior,

Sigahi

et al. 2023

MAKE

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Applications of deep learning (DL) in autonomous vehicle (AV) projects have gained increasing interest from both researchers and companies. This has caused a rapid expansion of scientific production on DL-AV in recent years, encouraging researchers to conduct systematic literature reviews (SLRs) to organize knowledge on the topic. However, a critical analysis of the existing SLRs on DL-AV reveals some methodological gaps, particularly regarding the use of bibliometric software, which are powerful tools for analyzing large amounts of data and for providing a holistic understanding on the structure of knowledge of a particular field. This study aims to identify the strategic themes and trends in DL-AV research using the Science Mapping Analysis Tool (SciMAT) and content analysis. Strategic diagrams and cluster networks were developed using SciMAT, allowing the identification of motor themes and research opportunities. The content analysis allowed categorization of the contribution of the academic literature on DL applications in AV project design; neural networks and AI models used in AVs; and transdisciplinary themes in DL-AV research, including energy, legislation, ethics, and cybersecurity. Potential research avenues are discussed for each of these categories. The findings presented in this study can benefit both experienced scholars who can gain access to condensed information about the literature on DL-AV and new researchers who may be attracted to topics related to technological development and other issues with social and environmental impacts.

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

Cited by 8 publications

References 35 publications

A Collaborative Control Scheme for Smart Vehicles Based on Multi-Agent Deep Reinforcement Learning

A Collaborative Control Scheme for Smart Vehicles Based on Multi-Agent Deep Reinforcement Learning

Machine Learning Techniques for Effective Pathogen Detection Based on Resonant Biosensors

Deep Learning and Autonomous Vehicles: Strategic Themes, Applications, and Research Agenda Using SciMAT and Content-Centric Analysis, a Systematic Review

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