Autonomous Vehicle Communication with Parrot-Optimized Orientation-Aware Graph Neural Network for Next-Generation IoT Connectivity

Effective communication between autonomous vehicles is essential for ensuring their safe and efficient operation. This research introduces Parr-OrAGNet, a novel approach that addresses the limitations of traditional communication methods in IoT automotive networks. Parr-OrAGNet integrates Macro-Micro Depth Normalization (MaMiDN) for accurate data normalization, Orientation-Aware Graph Neural Network (OrAGNet) for enhanced neural network modeling, and Parrot Optimizer (ParrOpt) for optimization. This combined approach effectively reduces packet loss, communication overhead, and latency. The evaluation results demonstrate significant improvements in communication efficiency, with a 97% reduction in packet loss and a 95% decrease in communication overhead. Additionally, Parr-OrAGNet achieves exceptional performance metrics in terms of accuracy, precision, recall, and computational efficiency. This research contributes to the advancement of autonomous vehicle communication by establishing a new benchmark for performance and scalability. Parr-OrAGNet offers a promising solution for addressing the challenges of IoT automotive networks and paving the way for the realization of fully autonomous driving.