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Next-Generation Edge Computing Assisted Autonomous Driving Based Artificial Intelligence Algorithms
2022
IEEE Access
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Therefore, we are motivated by using recent Deep Reinforcement Learning (DRL) techniques to learn the behavior of exact optimization algorithms while enhancing the Quality of Service (QoS) of network operators ...
and satisfying the requirements of the next-generation Autonomous Vehicles (AVs). ...
The communication steps between the centralized Edge and the distributed connected AD vehicles.
FIGURE 3 . 3 FIGURE 3. Reinforcement learning technique: Placement on virtual edge server use case. ...
doi:10.1109/access.2022.3174548
fatcat:gzwvdk6uhrduvpha7chugp4qkm
Table of Contents
2021
2021 IEEE 46th Conference on Local Computer Networks (LCN)
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Intelligent Vehicles: A Deep Learning Approach
233
A Formal Method for Evaluating the Performance of TSN Traffic Shapers Using UPPAAL
241
Transfer Learning-Based Accelerated Deep Reinforcement Learning ...
and Resource Allocation for FANETs with Deep Reinforcement Learning 315 A Machine Learning Approach to Peer Connectivity Estimation for Reliable Blockchain Networking 319 Optimal Placement of Recurrent ...
doi:10.1109/lcn52139.2021.9524933
fatcat:bopsc4l2qrc7bobzfyb6343iou
Guest Editorial: Green Industrial Internet of Things
2021
IEEE Transactions on Industrial Informatics
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He is an Associate Editor or Guest Editor of many reputed journals and was also the TPC Chair or Member for many IEEE major conferences and workshops, e.g., IEEE Globecom, IEEE ICC, IEEE CCNC, IEEE APCC ...
In the eighth article, "NOMA assisted multi-task multi-access mobile edge computing via deep reinforcement learning for Industrial Internet of Things," Wu et al. exploited NOMA for the computation offloading ...
In many IIoT applications, WPT and autonomous vehicle technologies, in combination, have the potential to solve a number of residual problems concerning the maintenance of, and data collection from embedded ...
doi:10.1109/tii.2021.3051693
dblp:journals/tii/ChangZHW21
fatcat:onmpon4bxja3vaq4hriifpzug4
Review of Deep Reinforcement Learning for Autonomous Driving
[article]
2023
arXiv
pre-print
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It presents a nomenclature of autonomous driving in which DRL techniques have been used, thus discussing important computational issues in evaluating autonomous driving agents in the real environment. ...
This research outlines deep, reinforcement learning algorithms (DRL). ...
Latest advances in the area have demonstrated that numerous deep reinforcement learning methods can be successfully used for various stages of motion planning problems for autonomous vehicles, but several ...
arXiv:2302.06370v1
fatcat:zegdpnl3pratblc3l336znr5py
Table of Contents
2022
IEEE Internet of Things Journal
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Zeng 1427 A Dynamic Evolution Method for Autonomous Vehicle Groups in a Highway Scene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Ejlali 1503 Deep Reinforcement Learning for Energy-Efficient Computation Offloading in Mobile-Edge Computing . . . . . . . . . ...
doi:10.1109/jiot.2021.3136382
fatcat:aqxpiojnfrhdnhbddraejvq36u
An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
2023
Future Internet
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This paper aims to analyze the literature on task scheduling in fog computing published in the last five years to classify the criteria used for decision-making and the technique used to solve the task ...
The task scheduling problem in fog computing has been widely explored and addressed using many approaches, from traditional operational research to heuristics and machine learning. ...
[43] describe a study that uses Deep Reinforcement Learning (DRL) techniques applied to Vehicle-to-Vehicle communication to optimize the utility of scheduling tasks and minimize delay in a fog computing ...
doi:10.3390/fi16010016
fatcat:4yxcml3ehrdgjivi76qhyu6v2y
On the Interplay of Artificial Intelligence and Space-Air-Ground Integrated Networks: A Survey
[article]
2024
arXiv
pre-print
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SAGINs are envisioned to extend high-speed broadband coverage to remote areas, such as small towns or mining sites, or areas where terrestrial infrastructure cannot reach, such as airplanes or maritime use ...
applications such as remote surgery and autonomous vehicles [404] . ...
HAPS placement Reinforcement Learning Deep Q-Network, Prioritized Experience replay Coverage rate [180] 2021 Multiple HAPS coordination Reinforcement Learning, Unsupervised Learning Deep Q-Network, Swarm ...
arXiv:2402.00881v1
fatcat:dzxlvy4qo5dc3hkvk6gdpemot4
Convergence of Information-Centric Networks and Edge Intelligence for IoV: Challenges and Future Directions
2022
Future Internet
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It is promising for computation-intensive applications, such as autonomous and cooperative driving, and to alleviate storage burdens (by caching). ...
In this paper, we discuss the applicability of AI techniques in solving challenging vehicular problems and enhancing the learning capacity of edge devices and ICN networks. ...
Moreover, the authors of [102] considered the feasibility of using reinforcement learning, specifically Q-learning for efficient and adaptive forwarding in NDN networks. ...
doi:10.3390/fi14070192
fatcat:knlyn5uaurarlhq7a5p66rwrgi
Editorial: Emerging Trends on Data Analytics at the Network Edge
2020
Peer-to-Peer Networking and Applications
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Xuemin Shen for all the advice regarding this special issue, as well as Ms. Katherine Moretti for the help during the publication process. ...
By enabling user to prefetch data from the edge cache devices, the backhaul load can be significantly reduced. To conclude this editorial, we would like to thank Prof. ...
Underwater Vehicles" Hongran Li, Weiwei Xu, et al., proposes a data-driven control approach based on polynomial regressors for autonomous underwater vehicles. ...
doi:10.1007/s12083-020-00964-9
fatcat:nngnda75m5hjtnz3t6v37xeivu
Design and Development of an Autonomous Car using Object Detection with YOLOv4
2022
Zenodo
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This paper goes through the process of fabricating a model vehicle, from its embedded hardware platform, to the end-to-end ML pipeline necessary for automated data acquisition and model-training, thereby ...
In this regard, we present a self-driving model car capable of autonomous driving using object-detection as a primary means of steering, on a track made of colored cones. ...
Reinforcement learning methods can be introduced in addition to this method to better performance. This method can be used as a prototype for future citywide self-driving cars projects. ...
doi:10.5281/zenodo.5854560
fatcat:xkhycgsrk5dpxamjetmtl732gq
2020 Index IEEE Internet of Things Journal Vol. 7
2020
IEEE Internet of Things Journal
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., Rateless-Code-Based Secure Cooperative Transmission Scheme for Industrial IoT; JIoT July 2020 6550-6565 Jamalipour, A., see Murali, S., JIoT Jan. 2020 379-388 James, L.A., see Wanasinghe, T.R., ...
Wu, P., +, JIoT Sept. 2020 9201-9213
Broad Reinforcement Learning for Supporting Fast Autonomous IoT. ...
Zhaofeng, M., +, JIoT May 2020 4000-4015
Broad Reinforcement Learning for Supporting Fast Autonomous IoT. ...
doi:10.1109/jiot.2020.3046055
fatcat:wpyblbhkrbcyxpnajhiz5pj74a
Guest Editorial Introduction of the Special Issue on Edge Intelligence for Internet of Vehicles
2021
IEEE transactions on intelligent transportation systems (Print)
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These abilities greatly help realize high-performance processing for mission-critical applications, real-time services to connected and autonomous vehicles as (e.g., HD maps, accidents alerts, and real-time ...
E MPOWERED with advanced computation units, autonomous sensing platforms and various wireless access capabilities, connected and autonomous vehicles evolve over time and become tightly coupled and closely ...
Social-Aware Incentive Mechanism for Vehicular Crowdsensing by Deep Reinforcement Learning Y. Zhao and C. H. ...
doi:10.1109/tits.2021.3066837
fatcat:z26pc73kbvb5lhdcjcrylfbzbm
Emotion-awareness for intelligent vehicle assistants
2018
Proceedings of the 1st International Workshop on Software Engineering for AI in Autonomous Systems - SEFAIS '18
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learning, and distributed (edge) computing delivering cognitive services. ...
EVA1 is describing a new class of emotion-aware autonomous systems delivering intelligent personal assistant functionalities. ...
Hence, novel dialog system design will have to explore techniques for policy optimization based on deep reinforcement learning, taking into account user's emotions as implicit reinforcement signals that ...
doi:10.1145/3194085.3194094
fatcat:stdxabm2jjevbijg4vboixszze
Table of Contents
2022
IEEE Transactions on Vehicular Technology
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Ma Adaptive Speed Planning of Connected and Automated Vehicles Using Multi-Light Trained Deep Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Filev 3609 An Integrated Decision-Making Framework for Highway Autonomous Driving Using Combined Learning and Rule-Based Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
doi:10.1109/tvt.2022.3164386
fatcat:g3v2t2enmbe3nfqkcuebwc4rte
Table of Contents
2022
IEEE Internet of Things Journal
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Alves 3889 Trajectory Design for UAV-Based Internet of Things Data Collection: A Deep Reinforcement Learning Approach . ...
Liu 3559 Intelligent Cruise Guidance and Vehicle Resource Management With Deep Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
doi:10.1109/jiot.2022.3149670
fatcat:tvaebi6psvgevhjhwfvdw5fii4
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