Live Traffic Video Multicasting Services in UAV-Assisted Intelligent Transport Systems: A Multiactor Attention Critic Approach

Live traffic video is vitally important for vehicles in future intelligent transport systems (ITSs). Due to the limitation of onboard sensors, vehicles may not be able to obtain a full view of the traffic situations which endangers safety for autonomous driving vehicles. In this article, we propose...

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Veröffentlicht in:IEEE internet of things journal 2023-11, Vol.10 (22), p.19740-19752
Hauptverfasser: Fu, Fang, Xue, Bin, Cai, Lin, Yang, Laurence T., Zhang, Zhicai, Luo, Jia, Wang, Chenmeng
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container_end_page 19752
container_issue 22
container_start_page 19740
container_title IEEE internet of things journal
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creator Fu, Fang
Xue, Bin
Cai, Lin
Yang, Laurence T.
Zhang, Zhicai
Luo, Jia
Wang, Chenmeng
description Live traffic video is vitally important for vehicles in future intelligent transport systems (ITSs). Due to the limitation of onboard sensors, vehicles may not be able to obtain a full view of the traffic situations which endangers safety for autonomous driving vehicles. In this article, we propose a traffic video multicasting scheme by using video splitting and group splitting techniques for unmanned aerial vehicles (UAVs)-assisted ITS, in which UAVs are considered as the eyes in the sky to capture real-time traffic videos. We aim to maximize the long-term video quality received by vehicles by jointly optimizing vehicle grouping and spectrum allocation. Considering the interactions among UAVs, the above optimization problem is formulated as a multiagent coordination problem in the form of a Markov game (MG). The MG is subsequently solved by leveraging a state-of-the-art multiagent deep reinforcement learning (MADRL) algorithm, namely, multiactor attention critic (MAAC), in which an attention mechanism is utilized to pay attention to other agents to make the learning process more effective and scalable. Extensive simulation results show that the MAAC-based algorithm has better performance in terms of video quality and spectrum efficiency compared with the baseline methods.
doi_str_mv 10.1109/JIOT.2023.3282936
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source IEEE Electronic Library (IEL)
subjects Algorithms
Deep learning
Intelligent transportation systems
Machine learning
Multiagent systems
Multicasting
Optimization
Spectrum allocation
Splitting
Unmanned aerial vehicles
title Live Traffic Video Multicasting Services in UAV-Assisted Intelligent Transport Systems: A Multiactor Attention Critic Approach
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