School of Computer Science and Information Engineering, Hohhot Minzu College, Hohhot 010051, China.
Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo 182-8585, Japan.
Sensors (Basel). 2022 Sep 15;22(18):6995. doi: 10.3390/s22186995.
Internet of Vehicles (IoV) technology has been attracting great interest from both academia and industry due to its huge potential impact on improving driving experiences and enabling better transportation systems. While a large number of interesting IoV applications are expected, it is more challenging to design an efficient IoV system compared with conventional Internet of Things (IoT) applications due to the mobility of vehicles and complex road conditions. We discuss existing studies about enabling collaborative intelligence in IoV systems by focusing on collaborative communications, collaborative computing, and collaborative machine learning approaches. Based on comparison and discussion about the advantages and disadvantages of recent studies, we point out open research issues and future research directions.
车联网（IoV）技术因其在改善驾驶体验和实现更好的交通系统方面的巨大潜力，引起了学术界和工业界的极大关注。虽然预计会有大量有趣的 IoV 应用，但与传统的物联网（IoT）应用相比，设计一个高效的 IoV 系统更具挑战性，这是由于车辆的移动性和复杂的道路条件所致。我们通过关注协作通信、协作计算和协作机器学习方法，讨论了现有的关于在 IoV 系统中实现协作智能的研究。基于对近期研究的优缺点的比较和讨论，我们指出了开放的研究问题和未来的研究方向。
