Distributed Deep Learning Based on Edge Computing Over Internet of Vehicles: Overview, Applications, and Challenges

Recently, the internet of vehicles (IoVs), mobile edge computing (MEC), and deep learning have attracted many research attentions in the applications of autonomous driving. MEC can help to reduce the network load and transmission delay by offloading the computing tasks to the powerful edge servers while deep learning can effectively improve the accuracy of obstacle detection to enhance the stability and safety of automatic driving. In this article, we first present a comprehensive overview of distributed deep learning based on edge computing over IoVs. Then, the related key techniques, including the distributed characteristics of IoVs, mobile edge collaborative computing architecture for high quality of service (QoS) requirements in terms of vehicle transmission delay and energy consumption, distributed deep learning, and its applications for vehicular networking, are discussed. Finally, the article identifies several important open challenges, opportunities, and potential research directions to provide a reference for readers in this field.