6G-Enabled Autonomous Vehicle Networks: Theoretical Analysis of Traffic Optimization and Signal Elimination

This paper proposes a theoretical framework for optimizing traffic flow in autonomous vehicle (AV) networks using 6G communication systems. We propose a novel technique to eliminate conventional traffic signals through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. The article demonstrates traffic flow optimization, density, and safety improvements through real-time management and decision-making. The theoretical foundation involves the combination of multi-agent deep reinforcement learning, coupled with complex analytical models across the partition managing intersections, thus forming the basis of proposed innovative city advancements. From the theoretical analysis, the proposed approach shows a relative improvement of 40-50% in intersection waiting time, 50-70% in accident probability, and 35% in carbon footprint. The above improvements are obtained by applying ultra-low latency 6G communication with the sub-millisecond response and accommodating up to 10000 vehicles per square kilometre. In addition, an economic evaluation revealed that such a system would generate a return on investment by 6.7 years, making this system a technical and financial system for enhancing an intelligent city.

@article{John2025,
  title     = {6G-Enabled Autonomous Vehicle Networks: Theoretical Analysis of Traffic Optimization and Signal Elimination},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Daniel Benniah John},
  doi       = {10.14569/IJACSA.2025.0160201},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160201}
}