Intelligent Transportation Systems Using Vehicle-to-Everything (V2X) Communication

  • Authors

    • Jacques Arsac Professor of Computer Science, University of Paris, France Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-2024PII4K6Y

    Published 12-04-2024

  • Intelligent Transportation Systems (ITS), Vehicle-To-Everything (V2X), Vehicle-To-Vehicle (V2V), Vehicle-To-Infrastructure (V2I), Internet Of Things (Iot), Artificial Intelligence (AI), Edge Computing, 5G Communication, Autonomous Vehicles, Smart Mobility

    Issue

    Vol. 7 No. 2 (2024)

    Section

    Articles

  • Abstract

    Intelligent Transportation Systems (ITS) have become a transformative solution to address the emerging challenges such as traffic congestion, road accidents, environmental pollution and inefficient mobility management in urban transportation. The Vehicle-to-Everything (V2X) communication has greatly improved the potential of ITS by allowing for the seamless and real-time exchange of information between vehicles, road-side infrastructure, pedestrians, cloud platforms and communication networks. V2X communication is defined as Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Pedestrian (V2P), Vehicle-to-Network (V2N) and Vehicle-to-Grid (V2G) communication, all of which will result in an intelligent transportation ecosystem that will support autonomous driving, cooperative traffic management and smart mobility services. The fifth-generation (5G) and emerging sixth-generation (6G) wireless communication technologies, along with Artificial Intelligence (AI), Edge Computing, IoT and Cloud Computing, have driven the deployment of very reliable, low latency V2X-enabled transportation systems even further. This paper provides a thorough review of current Intelligent Transportation Systems (ITS) technologies, highlights research gaps and introduces an artificial intelligence (AI) based communication framework to enhance transportation systems and road safety. The proposed framework brings in intelligent decision-making algorithms and edge-enabled V2X communication to enable dynamic traffic control, accident prevention, route optimization, and emergency response coordination. The study also covers communication protocols, system architecture, performance metrics, and challenges in implementing V2X. The results show that intelligent V2X communication greatly contributes to traffic efficiency, communication latency, and driving safety, as well as sustainable smart city transportation infrastructures.

  • References

    [1] H. Hartenstein and K. P. Laberteaux, VANET: Vehicular Applications and Inter-Networking Technologies. Hoboken, NJ, USA: Wiley, 2010.

    [2] K. Abboud, H. A. Omar, and W. Zhuang, "Interworking of DSRC and cellular network technologies for V2X communications: A survey," IEEE Transactions on Vehicular Technology, vol. 65, no. 12, pp. 9457–9470, Dec. 2016.

    [3] J. B. Kenney, "Dedicated Short-Range Communications (DSRC) standards in the United States," Proceedings of the IEEE, vol. 99, no. 7, pp. 1162–1182, Jul. 2011.

    [4] H. Ye, G. Y. Li, and B. H. Juang, "Deep reinforcement learning for resource allocation in V2X communications," IEEE Transactions on Vehicular Technology, vol. 68, no. 4, pp. 3163–3173, Apr. 2019.

    [5] Y. L. Morgan, "Notes on DSRC and WAVE standards suite: Its architecture, design, and characteristics," IEEE Communications Surveys & Tutorials, vol. 12, no. 4, pp. 504–518, Fourth Quarter 2010.

    [6] N. Lu, N. Cheng, N. Zhang, X. Shen, and J. W. Mark, "Connected vehicles: Solutions and challenges," IEEE Internet of Things Journal, vol. 1, no. 4, pp. 289–299, Aug. 2014.

    [7] M. Amoozadeh, A. Raghuramu, C. N. Chuah, D. Ghosal, H. Zhang, J. Rowe, and K. Levitt, "Security vulnerabilities of connected vehicle streams and their impact on cooperative driving," IEEE Communications Magazine, vol. 53, no. 6, pp. 126–132, Jun. 2015.

    [8] M. Chen, Y. Hao, K. Hwang, L. Wang, and L. Wang, "Disease prediction by machine learning over big data from healthcare communities," IEEE Access, vol. 5, pp. 8869–8879, 2017.

    [9] X. Hou, Y. Li, M. Chen, D. Wu, D. Jin, and S. Chen, "Vehicular fog computing: A viewpoint of vehicles as the infrastructures," IEEE Transactions on Vehicular Technology, vol. 65, no. 6, pp. 3860–3873, Jun. 2016.

    [10] S. Wang, Y. Zhang, and Y. Zhang, "A survey on mobile edge networks: Convergence of computing, caching, and communications," IEEE Access, vol. 5, pp. 6757–6779, 2017.

    [11] Q. Yang, Y. Liu, T. Chen, and Y. Tong, "Federated machine learning: Concept and applications," ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 2, pp. 1–19, Jan. 2019.

    [12] M. Grieves and J. Vickers, "Digital Twin: Mitigating unpredictable, undesirable emergent behavior in complex systems," in Transdisciplinary Perspectives on Complex Systems. Cham, Switzerland: Springer, 2017, pp. 85–113.

    [13] A. Festag, "Cooperative Intelligent Transport Systems standards in Europe," IEEE Communications Magazine, vol. 52, no. 12, pp. 166–172, Dec. 2014.

    [14] C. Campolo, A. Molinaro, A. O. Berthet, and A. Vinel, "From today's VANETs to tomorrow's planning and the bets for the day after," Vehicular Communications, vol. 2, no. 3, pp. 158–171, Jul. 2015.

    [15] M. A. Khan and K. Salah, "IoT security: Review, blockchain solutions, and open challenges," Future Generation Computer Systems, vol. 82, pp. 395–411, May 2018.

    [16] Gajula, S. (2023). A review of anomaly identification in finance frauds using machine learning system. International Journal of Current Engineering and Technology, 13(6), 568–575. https://ijcet.evegenis.org/index.php/ijcet/article/view/820

  • Downloads

Similar Articles

61-70 of 112

You may also start an advanced similarity search for this article.