Machine-Learning-Enabled Cooperative Perception for Connected Autonomous Vehicles: Challenges and Opportunities
Machine-Learning-Enabled Cooperative Perception for Connected Autonomous Vehicles: Challenges and Opportunities
复制标题
DOI：
10.1109/mnet.011.2000560
复制
发表时间：
2021-05
期刊：
影响因子：
9.3
通讯作者：
Qing Yang;Song Fu;Honggang Wang;Hua Fang
中科院分区：
文献类型：
--
作者：
Qing Yang;Song Fu;Honggang Wang;Hua Fang
互联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性来改变当前的交通系统。构建这样的系统的一个主要挑战是如何实现车辆之间有效和高效的协作感知，这使得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）感知数据。随着机器学习技术在自动驾驶汽车中的普及，特别是在其感知子系统中，我们阐述了为联网的自动驾驶汽车设计一个支持机器学习的协作感知系统的可能性。不仅是在设计合作感知的研究挑战，但我们也专注于如何减少通信和数据处理延迟，以满足自动驾驶应用程序所带来的严格的时间要求。本文概述了在设计自动驾驶汽车的协作感知方面的研究挑战和机遇，利用机器学习，特征地图量化，毫米波通信和车辆边缘计算的最新研究成果。
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.