Vol. 3 No. 2 (2023): Journal of AI-Assisted Scientific Discovery
Articles

AI-Driven Approaches for Autonomous Vehicle Communication and Networking

Dr. Masayuki Mori
Associate Professor of Robotics, Kyushu Institute of Technology, Japan
Cover

Published 23-06-2024

Keywords

  • Autonomous Vehicle,
  • AVs

How to Cite

[1]
Dr. Masayuki Mori, “AI-Driven Approaches for Autonomous Vehicle Communication and Networking”, Journal of AI-Assisted Scientific Discovery, vol. 3, no. 2, pp. 1–12, Jun. 2024, Accessed: Nov. 13, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/34

Abstract

In this New Era of the sixth generation (6G) of wireless technologies, the required incompatibilities and the desired solutions between connected and autonomous vehicles (CAV) with multidimensional security and resource management possibilities in the stretched vehicular communication infrastructure making full use of broader smarter world (robotic) mobile smart things due to multilayer interconnected (layer-less) collective intelligence, cellular internet node particularity, spatial groupiness, cognitive radio, block chain, AI/ML platforms are pivotal. Refinements of latent challenges specific lambdas for nanonetworking for a cluster of six Gs using block-hing based enroute computable, aggregation supported trust management with secure multistakeholder driven smart (SMS-Pki-LD) context evaluation with CATHEX multidonor-correlator are diversified innovative knowledge. More mature, simplified, intelligent and global 6G is needed to fit much more closely with the colourful requirements of a predominantly enroute automotives. Networking technology is inadequate to achieve the ambitious aims of Autonomous Vehicle, connected Autos and smart things in cellular internet domain in this sixth generation. Application driven services in this big data smart world (a group of seven Gs) demands for connectivity that are secure, reliable, flexible, bernoulli wit h Intelligence and massive mobility [1].

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References

  1. S. A. Abdel Hakeem, H. H. Hussein, and H. W. Kim, "Security Requirements and Challenges of 6G Technologies and Applications," 2022. ncbi.nlm.nih.gov
  2. A. Hozouri, A. Mirzaei, S. RazaghZadeh, and D. Yousefi, "An overview of VANET vehicular networks," 2023. [PDF]
  3. S. Zhang, J. Chen, F. Lyu, N. Cheng et al., "Vehicular Communication Networks in Automated Driving Era," 2018. [PDF]
  4. S. Ye, S. Liu, and F. Wang, "A Multiscenario Intelligent QoS Routing Algorithm for Vehicle Network," 2022. ncbi.nlm.nih.gov
  5. Tatineni, Sumanth. "Customer Authentication in Mobile Banking-MLOps Practices and AI-Driven Biometric Authentication Systems." Journal of Economics & Management Research. SRC/JESMR-266. DOI: doi. org/10.47363/JESMR/2022 (3) 201 (2022): 2-5.
  6. Vemori, Vamsi. "Evolutionary Landscape of Battery Technology and its Impact on Smart Traffic Management Systems for Electric Vehicles in Urban Environments: A Critical Analysis." Advances in Deep Learning Techniques 1.1 (2021): 23-57.
  7. Shaik, Mahammad, Srinivasan Venkataramanan, and Ashok Kumar Reddy Sadhu. "Fortifying the Expanding Internet of Things Landscape: A Zero Trust Network Architecture Approach for Enhanced Security and Mitigating Resource Constraints." Journal of Science & Technology 1.1 (2020): 170-192.
  8. B. Peshavaria, S. Kavaiya, and D. K. Patel, "An approach to implement Reinforcement Learning for Heterogeneous Vehicular Networks," 2022. [PDF]
  9. S. Ribouh, R. Sadli, Y. Elhillali, A. Rivenq et al., "Vehicular Environment Identification Based on Channel State Information and Deep Learning," 2022. ncbi.nlm.nih.gov
  10. S. Paiva, M. Abdul Ahad, G. Tripathi, N. Feroz et al., "Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges," 2021. ncbi.nlm.nih.gov
  11. A. Biswas and H. C. Wang, "Autonomous Vehicles Enabled by the Integration of IoT, Edge Intelligence, 5G, and Blockchain," 2023. ncbi.nlm.nih.gov
  12. B. Sliwa and C. Wietfeld, "Towards Data-driven Simulation of End-to-end Network Performance Indicators," 2019. [PDF]
  13. L. Liang, H. Ye, and G. Ye Li, "Toward Intelligent Vehicular Networks: A Machine Learning Framework," 2018. [PDF]
  14. H. Peng, Q. Ye, and X. Shen, "SDN-Based Resource Management for Autonomous Vehicular Networks: A Multi-Access Edge Computing Approach," 2018. [PDF]
  15. B. Yang, X. Cao, K. Xiong, C. Yuen et al., "Edge Intelligence for Autonomous Driving in 6G Wireless System: Design Challenges and Solutions," 2020. [PDF]
  16. D. Katare, D. Perino, J. Nurmi, M. Warnier et al., "A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services," 2023. [PDF]
  17. V. Kumar Kukkala, S. Vignesh Thiruloga, and S. Pasricha, "Roadmap for Cybersecurity in Autonomous Vehicles," 2022. [PDF]
  18. H. Cao, W. Zou, Y. Wang, T. Song et al., "Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey," 2022. [PDF]
  19. S. N. Saadatmand, "Finding the ground states of symmetric infinite-dimensional Hamiltonians: explicit constrained optimizations of tensor networks," 2019. [PDF]
  20. J. Xu, S. Hu, J. Yu, X. Liu et al., "Mixed Precision of Quantization of Transformer Language Models for Speech Recognition," 2021. [PDF]
  21. D. Rossi and L. Zhang, "Landing AI on Networks: An equipment vendor viewpoint on Autonomous Driving Networks," 2022. [PDF]
  22. S. Hakak, T. Reddy Gadekallu, S. Priya Ramu, P. M et al., "Autonomous Vehicles in 5G and Beyond: A Survey," 2022. [PDF]