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