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Vehicle Artificial Intelligence System Based on Intelligent Image Analysis and 5G Network


Artificial Intelligence is a medium for machine intelligence that offers tremendous opportunities for the intelligent industrial revolution. Smart transport, computer networks, and networked intelligent cities benefit from the rapid development of networking technologies. It has opened up new possibilities for traffic safety, comfort, and quality solutions. Data-driven approaches are refined using artificial intelligence, a widely used technique in various scientific fields. The new 5G network infrastructure challenges the existing networking situation by addressing the failings of 4G. These emerging technologies give intelligent cities and autonomous networks an additional means of being completely connected, including in high-mobility or densely populated areas, with massive simultaneous connecting and the ubiquity of the web. In this paper, an artificial intelligence-based Vehicle to everything (AI-V2X) system has been used. The proposed method can collect knowledge from various sources, increase driver awareness, and anticipate possible collisions, thus increasing driving comfort, security, and performance. Combining high-speed, robust, low latency networking and AI technology, the complementary between the real world and digital information in Industry 4.0 is transformed into an intelligent vehicle. AI-V2X aims to explore the possible contribution of the new AI approaches to an autonomous vehicle search. This discussion comprehensively illustrates the effects of 5G technologies on Smart Cities, Smart Transportation Networks – including Autonomous and Semi-Auto-Communications, Technological, Economic and Legal challenges. Finally, AI-V2X has open issues and concerns in research that must be resolved to realize AI’s responsibility to enhance V2X systems fully. The results are obtained various analysis of vehicles image recognition of 5G networking as follows: improvement of transportation with 5G ratio is 84.2%, vehicle image monitoring on traffic ratio is 88.2%, development of V2X communication is 85.36%, increasing driving comfort ratio is 82.15% and reduction of road congestion on traffic ratio is 91.84%.

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  1. 1.

    N. Islam, M. M. Rashid, F. Pasandideh, B. Ray, S. Moore and R. Kadel, A review of applications and communication technologies for Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) based sustainable smart farming, Sustainability, Vol. 13, No. 4, pp. 1821, 2021.

    Article  Google Scholar 

  2. 2.

    G. Manogaran, P. M. Shakeel, R. V. Priyan, N. Chilamkurti and A. Srivastava, Ant colony optimization-induced route optimization for enhancing driving range of electric vehicles, International Journal of Communication Systems, 2019.

    Article  Google Scholar 

  3. 3.

    U. Ahmad, H. Song, A. Bilal, M. Alazab and A. Jolfaei, Securing smart vehicles from relay attacks using machine learning, The Journal of Supercomputing, Vol. 76, No. 4, pp. 2665–2682, 2020.

    Article  Google Scholar 

  4. 4.

    T. D. Ngo, T. T. Bui, T. M. Pham, H. T. Thai, G. L. Nguyen, and T. N. Nguyen, Image deconvolution for optical small satellite with deep learning and real-time GPU acceleration, Journal of Real-Time Image Processing, PP. 1–14, 2021.

  5. 5.

    K. Kaur, S. Garg, G. Kaddoum, S. H. Ahmed, F. Gagnon and M. Atiquzzaman, Demand-response Management using a fleet of electric vehicles: an opportunistic-SDN-based edge-cloud framework for smart grids, IEEE Network, Vol. 33, No. 5, pp. 46–53, 2019.

    Article  Google Scholar 

  6. 6.

    J. Gao, H. Wang, and H. Shen, Machine learning based workload prediction in cloud computing. In 2020 29th international conference on computer communications and networks (ICCCN). IEEE, pp. 1–9, 2020, August.

  7. 7.

    T. G. Nguyen, T. V. Phan, D. T. Hoang, T. N. Nguyen and C. So-In, Efficient SDN-Based Traffic Monitoring in IoT Networks with Double Deep Q-Network, in: International Conference on Computational Data and Social Networks. pp. 26–38, Springer, Cham, 2020.

    Chapter  Google Scholar 

  8. 8.

    M. Gheisari, H. E. Najafabadi, J. A. Alzubi, J. Gao, G. Wang, A. A. Abbasi and A. Castiglione, OBPP: An ontology-based framework for privacy-preserving in IoT-based smart city, Future Generation Computer Systems, Vol. 123, pp. 1–13, 2021.

    Article  Google Scholar 

  9. 9.

    M. Gupta, F. M. Awaysheh, J. Benson, M. Al Azab, F. Patwa, and R. Sandhu, An Attribute-Based Access Control for Cloud-Enabled Industrial Smart Vehicles, IEEE Transactions on Industrial Informatics, 2020.

  10. 10.

    E. E. González, F. D. Morales, R. Coral and R. M. Toasa, Fifth-Generation Networks and Vehicle-to-Everything Communications, in: International Conference on Information Technology & Systems. pp. 350–360, Springer, Cham, 2021.

    Chapter  Google Scholar 

  11. 11.

    A. Daniel, K. Subburathinam, B. A. Muthu, N. Rajkumar, S. Kadry, R. K. Mahendran and S. Pandian, Procuring cooperative intelligence in autonomous vehicles for object detection through data fusion approach, IET Intelligent Transport Systems, Vol. 14, No. 11, pp. 1410–1417, 2020.

    Article  Google Scholar 

  12. 12.

    K. R. Malik, M. Ahmad, S. Khalid, H. Ahmad, F. Al-Turjman and S. Jabbar, Image and command hybrid model for vehicle control using Internet of Vehicles, Transactions on Emerging Telecommunications Technologies, Vol. 31, No. 5, pp. e3774, 2020.

    Article  Google Scholar 

  13. 13.

    S. Yang, Z. Zhang, R. Cao, M. Wang, H. Cheng, L. Zhang, ... and X. Liu, Implementation for a cloud battery management system based on the CHAIN framework, Energy and AI, 100088, 2021.

  14. 14.

    P. M. Kumar, G. Manogaran, R. Sundarasekar, N. Chilamkurti and R. Varatharajan, Ant colony optimization algorithm with Internet of vehicles for intelligent traffic control system, Computer Networks, Vol. 144, pp. 154–162, 2018.

    Article  Google Scholar 

  15. 15.

    N. Kumar, J. J. Rodrigues and N. Chilamkurti, Bayesian coalition game as-a-service for content distribution in Internet of vehicles, IEEE Internet of Things Journal, Vol. 1, No. 6, pp. 544–555, 2014.

    Article  Google Scholar 

  16. 16.

    A. Sharma and P. K. Singh, UAV-based framework for effective data analysis of forest fire detection using 5G networks: An effective approach towards smart cities solutions, International Journal of Communication Systems, 2021.

    Article  Google Scholar 

  17. 17.

    S. Wang, C. Fan, C. H. Hsu, Q. Sun and F. Yang, A vertical handoff method via self-selection decision tree for Internet of vehicles, IEEE Systems Journal, Vol. 10, No. 3, pp. 1183–1192, 2014.

    Article  Google Scholar 

  18. 18.

    K. Yu, L. Lin, M. Alazab, L. Tan, and B. Gu, Deep learning-based traffic safety solution for a mixture of autonomous and manual vehicles in a 5G-enabled intelligent transportation system, IEEE Transactions on Intelligent Transportation Systems, 2020.

  19. 19.

    M. Attaran, The impact of 5G on the evolution of intelligent automation and industry digitization. Journal of Ambient Intelligence and Humanized Computing, pp. 1–17, 2021.

  20. 20.

    A. Sharif, J. P. Li, M. A. Saleem, G. Manogran, S. Kadry, A. Basit and M. A. Khan, A dynamic clustering technique based on deep reinforcement learning for Internet of vehicles, Journal of Intelligent Manufacturing, Vol. 32, No. 3, pp. 757–768, 2021.

    Article  Google Scholar 

  21. 21.

    A. Musaddiq, R. Ali, R. Bajracharya, Y. A. Qadri, F. Al-Turjman and S. W. Kim, Trends, Issues, and Challenges in the Domain of IoT-Based Vehicular Cloud Network, in: Unmanned Aerial Vehicles in Smart Cities. pp. 49–64, Springer, Cham, 2020.

    Chapter  Google Scholar 

  22. 22.

    R. Gupta, A. Kumari and S. Tanwar, Fusion of blockchain and artificial intelligence for secure drone networking underlying 5G communications, Transactions on Emerging Telecommunications Technologies, Vol. 32, No. 1, pp. e4176, 2021.

    Article  Google Scholar 

  23. 23.

    J. Zhao, X. Xi, Q. Na, S. Wang, S. N. Kadry and P. M. Kumar, The technological innovation of hybrid and plug-in electric vehicles for environment carbon pollution control, Environmental Impact Assessment Review, Vol. 86, pp. 106506, 2021.

    Article  Google Scholar 

  24. 24.

    S. Wijethilaka, and M. Liyanage, Survey on network slicing for Internet of things realization in 5g networks, IEEE Communications Surveys & Tutorials, 2021.

  25. 25.

    M. Elhoseny, and A. E. Hassanien (eds)., Emerging Technologies for Connected Internet of Vehicles and Intelligent Transportation System Networks: Emerging Technologies for Connected and Smart Vehicles, Vol. 242, Springer.

  26. 26.

    G. Li, Development of cold chain logistics transportation system based on 5G network and Internet of things system, Microprocessors and Microsystems, Vol. 80, pp. 103565, 2021.

    Article  Google Scholar 

  27. 27.

    G. Manogaran, V. Saravanan, and C. H. Hsu, Information-Centric Content Management Framework for Software Defined Internet of Vehicles Towards Application Specific Services. IEEE Transactions on Intelligent Transportation Systems, 2021.

  28. 28.

    V. Stehel, C. Bradley, P. Suler and S. Bilan, Cyber-Physical System-based Real-Time Monitoring, Industrial Big Data Analytics, and Smart Factory Performance in Sustainable Manufacturing Internet of Things, Econ. Manag. Financ. Mark, Vol. 16, pp. 42–51, 2021.

    Article  Google Scholar 

  29. 29.

    Z. Qadir, F. Ullah, H. S. Munawar, and F. Al-Turjman, Addressing disasters in smart cities through UAVs path planning and 5G communications: A systematic review. Computer Communications, 2021.

  30. 30.

    G. Manogaran, P. M. Shakeel, V. Priyan, N. Chilamkurti, A. Srivastava, Ant colony optimization‐induced route optimization for enhancing driving range of electric vehicles, International Journal of Communication Systems, e3964, 2020

  31. 31.

    I. Rasheed, L. Zhang and F. Hu, A privacy preserving scheme for vehicle-to-everything communications using 5G mobile edge computing, Computer Networks, Vol. 176, pp. 107283, 2020.

    Article  Google Scholar 

  32. 32.

    S. Wan, R. Gu, T. Umer, K. Salah, and X. Xu, Toward offloading Internet of vehicles applications in 5G networks, IEEE Transactions on Intelligent Transportation Systems, 2020

  33. 33.

    Z. Ning, K. Zhang, X. Wang, M. S. Obaidat, L. Guo, X. Hu, ... and R. Y. Kwok, Joint computing and caching in 5G-envisioned Internet of vehicles: A deep reinforcement learning-based traffic control system, IEEE Transactions on Intelligent Transportation Systems, 2020

  34. 34.

    H. Lu, Y. Zhang, Y. Li, C. Jiang, and H. Abbas, User-oriented virtual mobile network resource management for vehicle communications, IEEE Transactions on Intelligent Transportation Systems, 2020.

  35. 35.

    M. Khayyat, A. Alshahrani, S. Alharbi, I. Elgendy, A. Paramonov and A. Koucheryavy, Multilevel service-provisioning-based autonomous vehicle applications, Sustainability, Vol. 12, No. 6, pp. 2497, 2020.

    Article  Google Scholar 

  36. 36.

    A. A. Ahmed and A. A. Alzahrani, A comprehensive survey on handover management for vehicular ad hoc network based on 5G mobile networks technology, Transactions on Emerging Telecommunications Technologies, Vol. 30, No. 3, pp. e3546, 2019.

    Article  Google Scholar 

  37. 37.

    B. Pawłowicz, M. Salach and B. Trybus, Smart city traffic monitoring system based on 5G cellular network, RFID and machine learning, in: KKIO Software Engineering Conference. pp. 151–165, Springer, Cham, 2018.

    Google Scholar 

  38. 38.

    T. Li, M. Zhao and K. K. L. Wong, Machine learning based code dissemination by selection of reliability mobile vehicles in 5G networks, Computer Communications, Vol. 152, pp. 109–118, 2020.

    Article  Google Scholar 

  39. 39.

    W. Tong, A. Hussain, W. X. Bo and S. Maharjan, Artificial intelligence for vehicle-to-everything: A survey, IEEE Access, Vol. 7, pp. 10823–10843, 2019.

    Article  Google Scholar 

  40. 40.

    S. K. Tayyaba, H. A. Khattak, A. Almogren, M. A. Shah, I. U. Din, I. Alkhalifa and M. Guizani, 5G vehicular network resource management for improving radio access through machine learning, IEEE Access, Vol. 8, pp. 6792–6800, 2020.

    Article  Google Scholar 

  41. 41.

    C. C. Ho, B. H. Huang, M. T. Wu, and T. Y. Wu, Optimized Base Station Allocation for Platooning Vehicles Underway by Using Deep Learning Algorithm Based on 5G-V2X, In 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE). IEEE, 1–2, 2019, October.

  42. 42.

    C. R. Storck and F. Duarte-Figueiredo, A Survey of 5G Technology Evolution, Standards, and Infrastructure Associated With Vehicle-to-Everything Communications by Internet of Vehicles, IEEE Access, Vol. 8, pp. 117593–117614, 2020.

    Article  Google Scholar 

  43. 43.

    S. Zhang and Q. Cheng, Data analysis and management system design of contaminated site based on intelligent data acquisition vehicle and 5G communication, International Journal of Communication Systems, Vol. 34, No. 6, pp. e4555, 2021.

    Article  Google Scholar 

  44. 44.

    S. K. Bhoi, K. K. Jena, S. K. Panda, H. V. Long, R. Kumar, P. Subbulakshmi and H. B. Jebreen, An Internet of Things assisted Unmanned Aerial Vehicle based artificial intelligence model for rice pest detection, Microprocessors and Microsystems, Vol. 80, pp. 103607, 2021.

    Article  Google Scholar 

  45. 45.

    M. F. Jwaid and H. K. S. Juboori, Vehicles for Open-Pit Mining with Smart Scheduling System for Transportation Based on 5G, Turkish Journal of Computer and Mathematics Education (TURCOMAT), Vol. 12, No. 5, pp. 827–835, 2021.

    Article  Google Scholar 

  46. 46.

  47. 47.

  48. 48.

  49. 49.

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Liu, B., Han, C., Liu, X. et al. Vehicle Artificial Intelligence System Based on Intelligent Image Analysis and 5G Network. Int J Wireless Inf Networks (2021).

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  • Artificial Intelligence
  • Vehicle to everything
  • 5G technologies