论文标题

协作驾驶:学习辅助联合拓扑配方和波束成绩

Collaborative Driving: Learning- Aided Joint Topology Formulation and Beamforming

论文作者

Zhang, Yao, Li, Changle, Luan, Tom H., Fu, Chau Yuen Yuchuan

论文摘要

目前,自动驾驶汽车能够根据在实际环境中从数百万英里处学到的驾驶政策自然地驾驶。但是,要进一步提高车辆的自动化水平是一项具有挑战性的任务,尤其是在多车合作的情况下。在最近对6G,毫米波(MMWave)和Terahertz(THZ)乐队的热烈讨论中,被认为在新的无线电通信体系结构和算法中起着重要作用。为了在6G中启用可靠的自动驾驶,我们设想合作自动驾驶,这是一个新框架,共同控制驾驶拓扑并在MMWave/THZ频段中建立车辆网络。作为一个群情报系统,就安全性和效率而言,协作驾驶计划超出了基于单车情报的现有自动驾驶模式。通过有效的数据共享,所提出的框架能够实现合作感测和负载平衡,从而通过节省的计算资源提高感应效率。为了应对协作驾驶框架中的新挑战,我们进一步说明了基于MMWave/THZ的车辆到车辆(V2V)通信的两种有希望的方法。最后,我们讨论了针对拟议的协作驾驶计划的一些潜在的开放研究问题。

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 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 mmWave/THz-based vehicle-to-vehicle (V2V) communications. Finally, we discuss several potential open research problems for the proposed collaborative driving scheme.

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