Collaborative Driving: Learning-Aided Joint Topology Formulation and Beamforming

Currently, autonomous vehicles are able to drive more naturally based on the driving policies learned from millions of driving miles in real environments. However, to further improve the automation level of vehicles is a challenging task, especially in the case of multivehicle cooperation. In recent heated discussions of 6G, millimeter-wave (mm-wave and terahertz (THz) bands are deemed to play important roles in new radio communication architectures and algorithms. To enable reliable autonomous driving in 6G, in this article, we envision collaborative autonomous driving, a new framework that jointly controls driving topology and formulate vehicular networks in the mm-wave/THz bands. As a swarm-intelligence system, the collaborative driving scheme goes beyond existing autonomous driving patterns based on single-vehicle intelligence in terms of safety and efficiency. With efficient data sharing, the proposed framework is able to achieve cooperative sensing and load balancing so that improve sensing efficiency with saved computational resources. To deal with the new challenges in the collaborative driving framework, we further illustrate two promising approaches for mm-wave/THz-based vehicle-to-vehicle (V2V) communications. Finally, we discuss several potential open research problems for the proposed collaborative driving scheme.