The use of Integration of H Level Communication with the Extended Identity Power Process to Have Intelligent Vehicles: An Empirical Review

The arrival of sixth-generation (6 G) wireless technology has ushered in a new era of connectivity, promising innovative services such as the Internet of Things (IoT) and, more importantly, when it is stimulating research into sixthgeneration networks (6G). However, the widespread integration of artificial intelligence (AI) into intelligent vehicles faces significant challenges. These challenges include limited resources, lack of high-quality labelled data, and lack of AIoptimized architecture. Edge Intelligence is emerging as a promising solution that aims to seamlessly integrate artificial intelligence with intelligent vehicles. This paper describes the key requirements and trends driving the understanding of 6 G networks, with an emphasis on self-learning techniques to develop intelligent vehicles. This study suggests a framework focused on automated learning based on onboard awareness, with the goal of reducing human intervention by automating data processing and model development. This architecture uses a Generative Adversarial Network (GAN) to aggregate labelled data for Al training, ensuring scalability, adaptability and efficiency. A case study in a connected traffic network demonstrates the effectiveness of the architecture for intelligent vehicles. The proposed architecture not only addresses the challenges of 6 G networks but also aligns with their requirements for efficiency, adaptability, and human-centricity By leveraging self-learning mechanisms, it paves the way for autonomous adaptation and evolution in dynamic network environments.