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Research on the sixth generation cellular networks (6G) is gaining hugemomentum to achieve ubiquitous wireless connectivity. Connected autonomousdriving (CAV) is a critical vertical envisioned for 6G, holding greatpotentials of improving road safety, road and energy efficiency. However thestringent service requirements of CAV applications on reliability, latency andhigh speed communications will present big challenges to 6G networks. Newchannel access algorithms and intelligent control schemes for connectedvehicles are needed for 6G supported CAV. In this paper, we investigated 6Gsupported cooperative driving, which is an advanced driving mode throughinformation sharing and driving coordination. Firstly we quantify the delayupper bounds of 6G vehicle to vehicle (V2V) communications with hybridcommunication and channel access technologies. A deep learning neural networkis developed and trained for fast computation of the delay bounds in real timeoperations. Then, an intelligent strategy is designed to control theinter-vehicle distance for cooperative autonomous driving. Furthermore, wepropose a Markov Chain based algorithm to predict the parameters of the systemstates, and also a safe distance mapping method to enable smooth vehicularspeed changes. The proposed algorithms are implemented in the AirSim autonomousdriving platform. Simulation results show that the proposed algorithms areeffective and robust with safe and stable cooperative autonomous driving, whichgreatly improve the road safety, capacity and efficiency.

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