Collaborative Driving: Learning- Aided Joint Topology Formulation and Beamforming
Yao Zhang, Changle Li, Tom H. Luan, Chau Yuen Yuchuan Fu

TL;DR
This paper proposes a collaborative autonomous driving framework utilizing mmWave/THz communications for joint topology control and beamforming, enhancing safety and efficiency through cooperative sensing and load balancing.
Contribution
It introduces a novel framework for collaborative autonomous driving that integrates joint topology formulation and beamforming in mmWave/THz bands, advancing multi-vehicle cooperation.
Findings
Achieves improved sensing efficiency through cooperative data sharing.
Proposes two approaches for mmWave/THz V2V communications.
Discusses open research challenges in collaborative driving.
Abstract
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 multi-vehicle cooperation. In recent heated discussions of 6G, millimeter-wave (mmWave) 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 paper, we envision collaborative autonomous driving, a new framework that jointly controls driving topology and formulate vehicular networks in the mmWave/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…
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Taxonomy
TopicsVehicular Ad Hoc Networks (VANETs) · Millimeter-Wave Propagation and Modeling · Privacy-Preserving Technologies in Data
