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REVIEW 4 major objections 5 minor 55 references

A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This survey claims that AI and machine learning, especially generative learning, have advanced enough to materially improve 6G Vehicle-to-Everything (V2X) communication, and that the field now needs—and receives—a systematic up-to-date…

desk verdict A readable but mis-titled survey: the 'comprehensive' claim isn't supported, yet it's a useful entry point for newcomers to AI/ML in 6G-V2X. read the letter →

arxiv 2506.09512 v1 pith:7F2X7EDR submitted 2025-06-11 eess.SY cs.LGcs.SY

Donglin Wang , Anjie Qiu , Qiuheng Zhou , Hans D. Schotten This is my paper
classification eess.SYcs.LGcs.SY
keywords 6GV2Xmachinelearningdeepreinforcementgenerativefederatedintelligenttransportationsystems
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This survey claims that the fast-moving field of AI for 6G Vehicle-to-Everything (V2X) communication had, until now, lacked a systematic up-to-date summary, and it sets out to provide one. It reviews work from roughly 2023 to 2025, organizing the landscape into deep learning, reinforcement learning, generative learning (including large language models), and federated learning. It then shows how each family has been applied to intelligent resource allocation, beamforming in millimeter-wave and terahertz bands, intelligent traffic management, and security. A sympathetic reader would take the paper's contribution to be a reliable map of the current state of the art and of the open challenges, especially the emerging role of generative AI.

What carries the argument

The organizing device is a four-by-four taxonomy: four AI and machine-learning model families—Deep Learning, Reinforcement Learning, Generative Learning (GANs, VAEs, LLMs), and Federated Learning—mapped onto four 6G-V2X application areas—intelligent resource management, AI-powered beamforming in mmWave and THz bands, intelligent traffic management, and security management. The taxonomy is summarized in Table I, which associates representative references with each cell. The survey uses this grid to structure its review and to argue that each model family has a distinct role: deep reinforcement learning for dynamic decision-making, deep learning for channel estimation, generative models for producing realistic content and safety messages, and federated learning for privacy-preserving distributed training.

What would settle it

A reader could compile a list of 2023-2025 studies on AI for 6G-V2X using a defined keyword query across standard scholarly databases; if many relevant works meeting the same criteria are absent from this survey, or if checking the cited papers shows their reported results are misstated, the claim of a comprehensive, reliable review would be falsified.

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Extended reading notes

Core claim

The paper's central claim is that recent advances in AI and machine learning—particularly generative learning—have progressed to the point where they can materially improve the performance, adaptability, and intelligence of 6G-V2X systems, and that this progress is best understood through a taxonomy of four model families applied to four application domains. The survey assembles representative studies from 2023 to 2025 and reports their stated gains, for example a 20.69% improvement in computation offloading and resource allocation from a federated learning framework, a 94.2% privacy protection score, and up to 97% intrusion-detection accuracy. It concludes that integrating AI with 6G-V2X is the direction of future mobility, while identifying real-time processing, data privacy, non-IID data, edge resource limits, scalability, and missing standards as the main obstacles.

Load-bearing premise

The survey's usefulness rests on the assumption that the papers it selected are representative of the 2023-2025 6G-V2X AI landscape and that its summaries of them are faithful, since no systematic search or inclusion criteria are described.

Editorial extensions

If this is right

  • If the survey's map is accurate, a researcher entering 6G-V2X can use it to identify the dominant AI approach for a given application area and the representative papers behind it.
  • The survey's emphasis on 2023 to 2025 suggests that deep reinforcement learning and federated learning have become the default tools for resource allocation and beamforming, rather than classical optimization alone.
  • Generative learning and large language models appear as the newest and least settled direction; the survey implies they will increasingly handle message generation, scenario simulation, and natural-language interaction in vehicular networks.
  • The listed challenges—latency, privacy, non-IID data, edge resource limits, and missing standards—define the near-term engineering agenda for AI-driven 6G-V2X deployment.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the survey does not describe a systematic search protocol or inclusion criteria, its comprehensiveness is a claim about selected representative studies rather than a guarantee of full coverage; a reader should treat the map as an orientation, not a census.
  • If generative learning is indeed the fastest-moving branch, one testable prediction is that the share of 6G-V2X papers using LLMs or other generative models will grow faster than the share using classical deep learning or reinforcement learning over the next two to three years.
  • The security results the survey cites are mostly simulation-based; a natural extension would be to benchmark the same AI models on a common real-world V2X security dataset to see whether the reported accuracy and privacy scores transfer.
  • The survey's taxonomy could be operationalized: a future systematic review could tag each paper by model family and application area, producing a quantitative map that would test whether the four-by-four grid adequately covers the field.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This manuscript is a survey of recent AI/ML techniques applied to 6G-V2X communication. After introducing the broader AI/ML landscape, it organizes the literature into four model families (ML, DL, generative learning, and federated learning) and four application areas (resource management, mmWave/THz beamforming, intelligent traffic management, and security management). The paper's central claim, stated in the abstract and Section I, is that it 'comprehensively reviews' recent advances with emphasis on 2023–2025, and it concludes with a discussion of challenges and future directions. There are no experiments; the contribution is intended as an organized overview of selected recent papers.

Significance. If the survey delivered on its claim, it would be a useful entry point for researchers and engineers seeking an overview of AI/ML for 6G-V2X, particularly because the topic is timely and the paper touches on emerging directions such as LLM integration and federated learning. The paper identifies several relevant recent papers and gives plausible summaries of their contributions, and the challenges section correctly names non-IID data, privacy, latency, and edge resource constraints. However, the survey's significance is currently limited by its non-reproducible selection of papers, the small number of primary works covered, and the absence of comparative or critical synthesis. The abstract's emphasis on generative learning is also not matched by the application sections, which discuss only one LLM-based application in detail.

major comments (4)
  1. [I (Introduction)] The abstract and Section I claim a 'comprehensive' review of AI/ML for 6G-V2X with emphasis on 2023–2025, but the paper provides no reproducible selection methodology: there is no search strategy, no list of databases, no inclusion/exclusion criteria, and no definition of the 'past two years' window. Section I states only that the selection focuses on models that 'demonstrate high performance, adaptability, and effectiveness' without saying how these qualities were assessed. Because the central value of a survey depends on representative coverage, this omission is load-bearing rather than cosmetic.
  2. [Table I and Sections III.A–III.D] The claim of emphasis on 2023–2025 is not consistently honored. Table I includes [38] (Prathiba et al., IEEE Transactions on Network Science and Engineering, 2021) and [41] (Moon et al., Journal of Communications and Networks, 2020) as representative entries for resource management and beamforming, and Section III.A also relies on [33] from 2017. If older works are included as background, the survey should explicitly say so; as written, the temporal claim in the abstract and Section I is contradicted by the table's own contents.
  3. [References [25], [31], [47], [52], [53]] Several cited works are incomplete to the point of being unverifiable: [31] has no author list, [47] has no authors and no venue, [52] has no authors, and [53] has no authors or venue. This matters because Sections II.D and III.D rely on these references for specific factual claims, including the robustness properties of decentralized federated learning and the FedVPS scheme. Without full bibliographic data, a reader cannot check whether the summaries are accurate or whether the cited work actually appears in the scholarly record.
  4. [III (AI for 6G-V2X Applications)] Section III is an annotated list of roughly twenty papers rather than the 'systematic summary' promised in the abstract. Each subsection describes selected papers individually but does not compare their methods, evaluation setups, or reported gains, nor does it explain how the chosen papers relate to the wider literature. In addition, the abstract highlights generative learning as a particularly promising direction, yet the application sections contain no detailed treatment of GANs, VAEs, or diffusion models for 6G-V2X, and the only generative method discussed at the application level is the LLM-based resource allocation in [36]. This mismatch further weakens the claim of a comprehensive and balanced review.
minor comments (5)
  1. [Throughout] There are repeated typographical and formatting errors, including 'CA Vs' in the abstract, 'COMMUNCATION' in the Section II heading, 'Artifical' in reference [13], 'V AEs' in Section II.C, and 'disruption,s' in Section IV. These should be corrected in a thorough copyedit.
  2. [Section III.D and references [51], [54]] References [51] and [54] are the same paper (Osorio et al., 'Towards 6G-enabled Internet of Vehicles: Security and Privacy,' IEEE Open Journal of the Communications Society, 2022) but are cited as if they were distinct sources in the security discussion and in the challenges section.
  3. [Section III.D] The in-text attribution 'Asha et al. [47]' cannot be verified against the reference list, since [47] contains no author names. Similarly, '[52] Hangdong et al.' has no author list in the bibliography; these attributions should be reconciled with complete references.
  4. [Section III.A] The sentence 'In this paper [34], this paper introduces a GNN and DRL method' is grammatically redundant and should be rewritten, for example as '[34] introduces a GNN and DRL method.'
  5. [Section I] The introduction mentions that earlier surveys are outdated and that certain emerging 6G-V2X applications are not covered, but it does not specify which emerging applications are newly covered here; a brief comparison with prior surveys would help clarify the claimed contribution.

Circularity Check

0 steps flagged · score 1.0 of 10

No circular derivation: the paper is a survey with no fitted predictions or imported-uniqueness claims, and its sole self-citation (Ref. [45]) is a descriptive, non-load-bearing example.

full rationale

This is a review paper with no derivation chain: it contains no equations, fits no parameters, and makes no quantity predictions, so none of the circularity patterns (self-definition, fitted input renamed as prediction, imported uniqueness theorem, ansatz smuggled via citation, renaming of a known result) apply. The only self-citation is Ref. [45] (A. Qiu, P. A. Sathish, D. Wang, and H. D. Schotten, 'Advanced traffic demand generation in sumo: ML-based prediction of flow rate based on real-world measured datasets,' VTC2024-Spring), which overlaps with the present authors (Qiu, Wang, Schotten). It appears in Section III.C as one descriptive example among several in the Intelligent Traffic Management discussion: 'In this paper [45], a new scheme is proposed to generate large-scale traffic demands, where a real-world dataset is fed into an ML model to predict traffic flows.' The survey's organization, taxonomy, and claimed contributions do not depend on this citation, and removing it would not change any conclusion, so it is not load-bearing. The central claim—'This survey comprehensively reviews recent advances in AI and ML models applied to 6G-V2X communication' (Abstract)—is a scope/reporting claim about the surveyed literature, not a result derived from that literature, so it cannot reduce to its own inputs. Quality concerns that a reader should weigh separately under correctness risk (not circularity) include the non-reproducible selection methodology and incomplete bibliographic entries for Refs. [47] and [52], which make verification of the secondary summaries harder; these are reporting defects, not circular reasoning. Overall the derivation chain is absent by construction of the genre, and no circular step can be exhibited with quotation and reduction.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

This survey introduces no free parameters or invented entities. It relies on the domain assumption that the cited literature accurately reports results and that the authors' selection is representative, neither of which is verified in the paper.

assumptions (2)
  • domain assumption The selected papers are representative of the state of the art in AI for 6G-V2X.
    Section I states the selection focuses on models that demonstrate high performance, adaptability, and effectiveness, but no selection criteria are given.
  • domain assumption 6G networks will provide the ultra-reliable, low-latency, high-capacity connectivity described in the introduction.
    The survey's framing depends on this projected capability of 6G, which is taken from the cited 6G literature rather than demonstrated in this paper.

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Cite this review

Pith. "Pith review of A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications." pith.science (2026). https://pith.science/paper/7F2X7EDR

@misc{pith2026250609512,
  author       = {Pith},
  title        = {Pith review of: A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7F2X7EDR}},
  note         = {Machine review of arXiv:2506.09512}
}
read the original abstract

The rapid advancement of Vehicle-to-Everything (V2X) communication is transforming Intelligent Transportation Systems (ITS), with 6G networks expected to provide ultra-reliable, low-latency, and high-capacity connectivity for Connected and Autonomous Vehicles (CAVs). Artificial Intelligence (AI) and Machine Learning (ML) have emerged as key enablers in optimizing V2X communication by enhancing network management, predictive analytics, security, and cooperative driving due to their outstanding performance across various domains, such as natural language processing and computer vision. This survey comprehensively reviews recent advances in AI and ML models applied to 6G-V2X communication. It focuses on state-of-the-art techniques, including Deep Learning (DL), Reinforcement Learning (RL), Generative Learning (GL), and Federated Learning (FL), with particular emphasis on developments from the past two years. Notably, AI, especially GL, has shown remarkable progress and emerging potential in enhancing the performance, adaptability, and intelligence of 6G-V2X systems. Despite these advances, a systematic summary of recent research efforts in this area remains lacking, which this survey aims to address. We analyze their roles in 6G-V2X applications, such as intelligent resource allocation, beamforming, intelligent traffic management, and security management. Furthermore, we explore the technical challenges, including computational complexity, data privacy, and real-time decision-making constraints, while identifying future research directions for AI-driven 6G-V2X development. This study aims to provide valuable insights for researchers, engineers, and policymakers working towards realizing intelligent, AI-powered V2X ecosystems in 6G communication.

Figures

Figures reproduced from arXiv: 2506.09512 by the authors.

Figure 1
Figure 1. AI Hierarchy A. Machine Learning (ML) ML, as a central subfield of AI, focuses on how these agents can improve their perception based on experience or data [13]. ML algorithms emphasize the ability of machines to improve their performance on a task over time without being explicitly programmed for each specific task. Recently, ML models have progressed rapidly from traditional su￾pervised and unsupervised techniques… view at source ↗

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Works this paper leans on

55 extracted references · 50 canonical work pages

  1. [45]

    Advanced traffic demand generation in sumo: Ml-based prediction of flow rate based on real-world measured datasets,

    A. Qiu, P. A. Sathish, D. Wang, and H. D. Schotten, “Advanced traffic demand generation in sumo: Ml-based prediction of flow rate based on real-world measured datasets,” in2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring). IEEE, 2024, pp. 1–7

    work page 2024

  2. [31]

    Decentralized identity management and privacy-enhanced federated learning for automotive systems: A novel framework,

    “Decentralized identity management and privacy-enhanced federated learning for automotive systems: A novel framework,” 2024

    work page 2024

  3. [47]

    Harnessing 6g technology for seamless autonomous vehicle commu- nication and performance enhancement,

    “Harnessing 6g technology for seamless autonomous vehicle commu- nication and performance enhancement,” pp. 99–104, 2024

    work page 2024

  4. [52]

    Fedvps: Federated learning for privacy and security of internet of vehicles on non-iid data,

    “Fedvps: Federated learning for privacy and security of internet of vehicles on non-iid data,” 2023, pp. 178–183

    work page 2023

  5. [53]

    Research on network security and privacy protection technology of autonomous vehicle,

    “Research on network security and privacy protection technology of autonomous vehicle,”International Journal of Computer Science and Information Technology, 2024

    work page 2024

  6. [10]

    Artificial intelli- gence for vehicle-to-everything: A survey,

    W. Tong, A. Hussain, W. X. Bo, and S. Maharjan, “Artificial intelli- gence for vehicle-to-everything: A survey,”IEEE Access, vol. 7, pp. 10 823–10 843, 2019

    work page 2019

  7. [11]

    Artificial intelligence and machine learning as key enablers for v2x communications: A comprehensive survey,

    M. Christopoulou, S. Barmpounakis, H. Koumaras, and A. Kaloxylos, “Artificial intelligence and machine learning as key enablers for v2x communications: A comprehensive survey,”Vehicular Communica- tions, vol. 39, p. 100569, 2023

    work page 2023

  8. [38]

    Federated learning empowered computation offloading and resource management in 6g-v2x,

    S. B. Prathiba, G. Raja, S. Anbalagan, K. Dev, S. Gurumoorthy, and A. P. Sankaran, “Federated learning empowered computation offloading and resource management in 6g-v2x,”IEEE Transactions on Network Science and Engineering, vol. 9, no. 5, pp. 3234–3243, 2021

    work page 2021

  9. [41]

    Deep learning-based channel esti- mation and tracking for millimeter-wave vehicular communications,

    S. Moon, H. Kim, and I. Hwang, “Deep learning-based channel esti- mation and tracking for millimeter-wave vehicular communications,” Journal of Communications and Networks, vol. 22, no. 3, pp. 177– 184, 2020

    work page 2020

  10. [33]

    Integrated networking, caching, and computing for connected vehicles: A deep reinforcement learning approach,

    Y . He, N. Zhao, and H. Yin, “Integrated networking, caching, and computing for connected vehicles: A deep reinforcement learning approach,”IEEE transactions on vehicular technology, vol. 67, no. 1, pp. 44–55, 2017

    work page 2017

  11. [36]

    Resource allocation in large language model integrated 6g vehicular networks,

    C. Liu and J. Zhao, “Resource allocation in large language model integrated 6g vehicular networks,” in2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring). IEEE, 2024, pp. 1–6

    work page 2024

Show all 55 references
  1. [1]

    6g for vehicle-to-everything (v2x) communications: Enabling technologies, challenges, and oppor- tunities,

    M. Noor-A-Rahim, Z. Liu, H. Lee, M. O. Khyam, J. He, D. Pesch, K. Moessner, W. Saad, and H. V . Poor, “6g for vehicle-to-everything (v2x) communications: Enabling technologies, challenges, and oppor- tunities,”Proceedings of the IEEE, 2022

    2022

  2. [2]

    Study on new radio (nr) to support non-terrestrial networks,

    “Study on new radio (nr) to support non-terrestrial networks,” 3rd Generation Partnership Project (3GPP), Tech. Rep. 38.811, March 2022, version 15.4.0, Release 15

    2022

  3. [3]

    Performance evaluation of the ieee 802.11 p wave communication standard,

    S. Eichler, “Performance evaluation of the ieee 802.11 p wave communication standard,” in2007 IEEE 66th Vehicular Technology Conference. IEEE, 2007, pp. 2199–2203

    2007

  4. [4]

    Study on LTE-based vehicle-to-everything (V2X) services,

    “Study on LTE-based vehicle-to-everything (V2X) services,” 3rd Generation Partnership Project (3GPP), Tech. Rep. 36.885, jun 2016, version 14.0.0, Release 14. [Online]. Available: https://portal.3gpp.org/desktopmodules/Specifications /Specification- Details.aspx?specificationId=2934

    2016

  5. [5]

    Study on NR vehicle-to-everything (V2X); (Re- lease 16),

    “Study on NR vehicle-to-everything (V2X); (Re- lease 16),” 3rd Generation Partnership Project (3GPP), Tech. Rep. 38.885, Jun 2019. [Online]. Available: https://portal.3gpp.org/desktopmodules/Specifications/ Specification- Details.aspx?specificationId=3498

    2019

  6. [6]

    Summary for RAN Rel-18 package,

    “Summary for RAN Rel-18 package,” 3rd Generation Partnership Project (3GPP), Tech. Rep. RP-213468, dec 2021, release 18. [Online]. Available: https://www.3gpp.org/specifications- technologies/releases/release-18

    2021

  7. [7]

    Toward a 6g ai-native air interface,

    N. W. Paper, “Toward a 6g ai-native air interface,” Online, 2023, accessed October 2023. [Online]. Available: https://onestore.nokia.com/asset/210299

    2023

  8. [8]

    6g research visions white paper series,

    G. Flagship, “6g research visions white paper series,” Online, 2023, accessed October 2023. [Online]. Available: https://www.6gflagship.com/white-papers/

    2023

  9. [9]

    Res6edge: An edge-ai enabled resource sharing scheme for c-v2x communications towards 6g,

    J. Sanghvi, P. Bhattacharya, S. Tanwar, R. Gupta, N. Kumar, and M. Guizani, “Res6edge: An edge-ai enabled resource sharing scheme for c-v2x communications towards 6g,” in2021 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2021, pp. 149–154

    2021

  10. [12]

    Application of artificial intelligence in traffic control system of non- autonomous vehicles at signalized road intersection

    O. Olayode, L. Tartibu, and M. Okwu, “Application of artificial intelligence in traffic control system of non- autonomous vehicles at signalized road intersection.”Procedia CIRP, vol. 91, pp. 194–200, 2020, enhancing design through the 4th Industrial Revolution Thinking. [Onli...

    2020

  11. [13]

    Artifical intelligence: New paradigm in deep space exploration,

    A. A. Omar, M. M. Farag, and R. A. Alhamad, “Artifical intelligence: New paradigm in deep space exploration,” in2021 14th International Conference on Developments in eSystems Engineering (DeSE), 2021, pp. 438–442

    2021

  12. [14]

    Machine learning technologies for secure vehicular communication in internet of vehicles: recent advances and applications,

    E. S. Ali, M. K. Hasan, R. Hassan, R. A. Saeed, M. B. Hassan, S. Is- lam, N. S. Nafi, and S. Bevinakoppa, “Machine learning technologies for secure vehicular communication in internet of vehicles: recent advances and applications,”Security and Communication Networks, vol. 2021...

    2021

  13. [15]

    Deep reinforcement learning based dynamic reputation policy in 5g based vehicular communication networks,

    S. Gyawali, Y . Qian, and R. Q. Hu, “Deep reinforcement learning based dynamic reputation policy in 5g based vehicular communication networks,”IEEE Transactions on Vehicular Technology, vol. 70, no. 6, pp. 6136–6146, 2021

    2021

  14. [16]

    Machine learning in vehicular networking: An overview,

    K. Tan, D. Bremner, J. Le Kernec, L. Zhang, and M. Imran, “Machine learning in vehicular networking: An overview,”Digital Communica- tions and Networks, vol. 8, no. 1, pp. 18–24, 2022

    2022

  15. [17]

    C. C. Aggarwalet al.,Neural networks and deep learning. Springer, 2018, vol. 10, no. 978

    2018

  16. [18]

    Evolving artificial neural networks,

    X. Yao, “Evolving artificial neural networks,”Proceedings of the IEEE, vol. 87, no. 9, pp. 1423–1447, 1999

    1999

  17. [19]

    Deep reinforcement learning for energy-efficient 6g v2x networks,

    F. Marzuk, A. Vejar, and P. Chołda, “Deep reinforcement learning for energy-efficient 6g v2x networks,”Electronics, vol. 14, no. 6, p. 1148, 2025

    2025

  18. [20]

    Survey of deep reinforcement learning for motion planning of autonomous vehicles,

    S. Aradi, “Survey of deep reinforcement learning for motion planning of autonomous vehicles,”IEEE Transactions on Intelligent Trans- portation Systems, vol. 23, no. 2, pp. 740–759, 2020

    2020

  19. [21]

    A review on generative adversarial networks: Algorithms, theory, and applications,

    J. Gui, Z. Sun, Y . Wen, D. Tao, and J. Ye, “A review on generative adversarial networks: Algorithms, theory, and applications,”IEEE transactions on knowledge and data engineering, vol. 35, no. 4, pp. 3313–3332, 2021

    2021

  20. [22]

    Tutorial on variational autoencoders,

    C. Doersch, “Tutorial on variational autoencoders,”arXiv preprint arXiv:1606.05908, 2016

    2016 arXiv

  21. [23]

    Generative ai-enabled vehicular networks: Fundamentals, framework, and case study,

    R. Zhang, K. Xiong, H. Du, D. Niyato, J. Kang, X. Shen, and H. V . Poor, “Generative ai-enabled vehicular networks: Fundamentals, framework, and case study,”IEEE Network, 2024

    2024

  22. [24]

    A survey of large language models,

    W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y . Hou, Y . Min, B. Zhang, J. Zhang, Z. Donget al., “A survey of large language models,”arXiv preprint arXiv:2303.18223, vol. 1, no. 2, 2023

    2023 arXiv

  23. [25]

    Safedrive: Knowledge- and data-driven risk-sensitive decision- making for autonomous vehicles with large language models,

    “Safedrive: Knowledge- and data-driven risk-sensitive decision- making for autonomous vehicles with large language models,” 2024. [Online]. Available: http://arxiv.org/pdf/2412.13238

    2024 arXiv

  24. [26]

    Gpt-4 technical report,

    J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkatet al., “Gpt-4 technical report,”arXiv preprint arXiv:2303.08774, 2023

    2023 arXiv

  25. [27]

    6g wireless systems: Vision, requirements, challenges, insights, and opportunities,

    H. Tataria, M. Shafi, A. F. Molisch, M. Dohler, H. Sj ¨oland, and F. Tufvesson, “6g wireless systems: Vision, requirements, challenges, insights, and opportunities,”Proceedings of the IEEE, vol. 109, no. 7, pp. 1166–1199, 2021

    2021

  26. [28]

    Connectgpt: Connect large language models with connected and automated vehicles,

    K. Tong and S. Solmaz, “Connectgpt: Connect large language models with connected and automated vehicles,” in2024 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2024, pp. 581–588

    2024

  27. [29]

    Survey of llms and ai agents in v2x: Simulation, analysis & architectures,

    S. Panyam, A. Donvir, G. Paliwal, and P. Gujar, “Survey of llms and ai agents in v2x: Simulation, analysis & architectures,” in2025 Systems of Signals Generating and Processing in the Field of on Board Communications. IEEE, 2025, pp. 1–11

    2025

  28. [30]

    Distributed federated learning for ultra-reliable low-latency vehicular communi- cations,

    S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, “Distributed federated learning for ultra-reliable low-latency vehicular communi- cations,”IEEE Transactions on Communications, vol. 68, no. 2, pp. 1146–1159, 2019

    2019

  29. [32]

    A survey on federated learning in intelligent transportation systems,

    R. Zhang, J. Mao, H. Wang, B. Li, X. Cheng, and L. Yang, “A survey on federated learning in intelligent transportation systems,” IEEE Transactions on Intelligent Vehicles, 2024

    2024

  30. [34]

    Graph neural networks and deep reinforcement learning based resource allocation for v2x communications,

    M. Ji, Q. Wu, P. Fan, N. Cheng, W. Chen, J. Wang, and K. B. Letaief, “Graph neural networks and deep reinforcement learning based resource allocation for v2x communications,”IEEE Internet of Things Journal, 2024

    2024

  31. [35]

    Event-triggered reinforcement learning based joint resource allocation for ultra-reliable low-latency v2x communi- cations,

    N. Khan and S. Coleri, “Event-triggered reinforcement learning based joint resource allocation for ultra-reliable low-latency v2x communi- cations,”IEEE Transactions on Vehicular Technology, 2024

    2024

  32. [37]

    Federated learning-based resource allocation for v2x communications,

    S. Bhardwaj, D.-H. Kim, and D.-S. Kim, “Federated learning-based resource allocation for v2x communications,”IEEE Transactions on Intelligent Transportation Systems, 2024

    2024

  33. [39]

    6g v2x technologies and orchestrated sensing for autonomous driving,

    M. Mizmizi, M. Brambilla, D. Tagliaferri, C. Mazzucco, M. Deb- bah, T. Mach, R. Simeone, S. Mandelli, V . Frascolla, R. Lombardi et al., “6g v2x technologies and orchestrated sensing for autonomous driving,”arXiv preprint arXiv:2106.16146, 2021

    2021 arXiv

  34. [40]

    Multiple-input multiple- output (mimo) radar: Performance issues,

    K. Forsythe, D. Bliss, and G. Fawcett, “Multiple-input multiple- output (mimo) radar: Performance issues,” inConference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004., vol. 1. IEEE, 2004, pp. 310–315

    2004

  35. [42]

    Federated learning-based joint radar- communication mmwave beamtracking with imperfect csi for v2x communications,

    S. Bhardwaj and D.-S. Kim, “Federated learning-based joint radar- communication mmwave beamtracking with imperfect csi for v2x communications,” in2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN). IEEE, 2023, pp. 201– 206

    2023

  36. [43]

    Federated learning mm wave beamform- ing for v2x communications with imperfect csi and doppler shift,

    S. Bhardwaj and D. S. Kim, “Federated learning mm wave beamform- ing for v2x communications with imperfect csi and doppler shift,” in 2024 Fifteenth International Conference on Ubiquitous and Future Networks (ICUFN). IEEE, 2024, pp. 410–415

    2024

  37. [44]

    Modern- izing transit: Intelligent traffic and transportation management with artificial intelligence in the era of 5g and 6g,

    M. Khalid, M. Awais, C. Jisi, M. Ahmad, and B.-h. Roh, “Modern- izing transit: Intelligent traffic and transportation management with artificial intelligence in the era of 5g and 6g,”The Intersection of 6G, AI/Machine Learning, and Embedded Systems: Pioneering Intelligent Wire...

    2025

  38. [46]

    Intelligent road network management supported by 6g and deep reinforcement learning,

    S. Zhou, X. Chen, C. Li, W. Chang, F. Wei, and L. Yang, “Intelligent road network management supported by 6g and deep reinforcement learning,”IEEE Transactions on Intelligent Transportation Systems, 2024

    2024

  39. [48]

    Bfledge: Blockchain based federated edge learning scheme in v2x underlying 6g communications,

    V . A. Patel, P. Bhattacharya, S. Tanwar, N. K. Jadav, and R. Gupta, “Bfledge: Blockchain based federated edge learning scheme in v2x underlying 6g communications,” in2022 12th International Confer- ence on Cloud Computing, Data Science & Engineering (Confluence), 2022, pp. 146–152

    2022

  40. [49]

    Ai-empowered secure data communication in v2x environment with 6g network,

    A. R. Nair, N. K. Jadav, R. Gupta, and S. Tanwar, “Ai-empowered secure data communication in v2x environment with 6g network,” in IEEE INFOCOM 2022 - IEEE Conference on Computer Communi- cations Workshops (INFOCOM WKSHPS), 2022, pp. 1–6

    2022

  41. [50]

    Ar- tificial intelligence powered internet of vehicles: securing connected vehicles in 6g,

    D. Raja, Z. Abas, C. Akula, Y . Kumar, G. Kumar, and V . Eswari, “Ar- tificial intelligence powered internet of vehicles: securing connected vehicles in 6g,”Indonesian Journal of Electrical Engineering and Computer Science, vol. 35, p. 213, 07 2024

    2024

  42. [51]

    Towards 6g-enabled internet of vehicles: Security and privacy,

    “Towards 6g-enabled internet of vehicles: Security and privacy,”IEEE open journal of the Communications Society, vol. 3, pp. 82–105, 2022. [Online]. Available: https://ieeexplore.ieee.org/ielx7/8782661/9702748/09681822.pdf

    2022

  43. [54]

    Towards 6g-enabled internet of vehicles: Security and privacy,

    D. P. M. Osorio, I. Ahmad, J. D. V . S ´anchez, A. Gurtov, J. Schol- liers, M. Kutila, and P. Porambage, “Towards 6g-enabled internet of vehicles: Security and privacy,”IEEE Open Journal of the Commu- nications Society, vol. 3, pp. 82–105, 2022

    2022

  44. [55]

    Towards 6g v2x sidelink: Survey of resource allocation-mathematical formulations, challenges, and proposed solu- tions,

    P. Rajalakshmiet al., “Towards 6g v2x sidelink: Survey of resource allocation-mathematical formulations, challenges, and proposed solu- tions,”IEEE Open Journal of Vehicular Technology, 2024

    2024

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.