Connected and autonomous vehicles is a disruptive technology that has the potential to transform the current transportation system by reducing traffic accidents and enhancing driving safety. One major challenge of building such a system is how to realize effective and efficient cooperative perception among vehicles, which enables them to share local (raw or processed) perception data with each other or roadside infrastructures through wireless communications. As machine learning techniques become prevalent in autonomous vehicles, particularly in their perception subsystem, we articulate the possibility to design a machine-learning-enabled cooperative perception system for connected autonomous vehicles. Not only are the research challenges in designing cooperative perception presented, but we also focus on how to reduce communication and data processing latency in order to meet the stringent time requirements posed by autonomous driving applications. The article outlines the research challenges and opportunities in designing cooperative perception for autonomous vehicles, leveraging the recent research outcomes from machine learning, feature map quantification, millimeter-wave communications, and vehicular edge computing.

中文翻译：

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支持机器学习的联网自动驾驶车辆的协作感知：挑战和机遇


联网和自动驾驶汽车是一项颠覆性技术，有可能通过减少交通事故和提高驾驶安全来改变当前的交通系统。构建这样一个系统的一个主要挑战是如何实现车辆之间有效且高效的协作感知，使它们能够通过无线通信与彼此或路边基础设施共享本地（原始或处理后的）感知数据。随着机器学习技术在自动驾驶汽车中变得普遍，特别是在其感知子系统中，我们阐明了为联网自动驾驶汽车设计支持机器学习的协作感知系统的可能性。不仅提出了设计协作感知的研究挑战，而且我们还关注如何减少通信和数据处理延迟，以满足自动驾驶应用提出的严格时间要求。本文利用机器学习、特征图量化、毫米波通信和车辆边缘计算的最新研究成果，概述了自动驾驶汽车合作感知设计的研究挑战和机遇。