Deep-Learning-Based Intelligent Intervehicle Distance Control for 6G-Enabled Cooperative Autonomous Driving

Research on the sixth-generation cellular networks (6G) is gaining huge momentum to achieve ubiquitous wireless connectivity. Connected autonomous vehicles (CAVs) a critical vertical application for 6G, holding great potentials of improving road safety, and energy efficiency. However, stringent service requirements CAV applications reliability, latency, high speed communications will present big challenges 6G networks. New channel access algorithms intelligent control schemes connected are needed 6G-supported CAV. In this article, we investigated cooperative driving, which an advanced driving mode through information sharing coordination. First, quantify delay upper bounds vehicle-to-vehicle (V2V) with hybrid communication technologies. A deep learning neural network developed trained fast computation in real-time operations. Then, strategy designed intervehicle distance driving. Furthermore, propose Markov chain-based algorithm predict parameters system states, also safe mapping method enable smooth vehicular changes. The proposed implemented AirSim platform. Simulation results show that effective robust stable greatly improve capacity,