Overview of deep learning and mobile edge computing in autonomous driving

Abstract

In recent years, mobile edge computing and deep learning have attracted strong industry attention in the application scenario of autonomous driving. Mobile edge computing reduces the transmission delay of autonomous driving information by offloading computational tasks to edge servers to reduce the network load; deep learning can effectively improve the accuracy of obstacle detection, thereby enhancing the stability and safety of autonomous driving. This paper first introduces the basic concept and reference architecture of MEC and the commonly used model algorithms in deep learning, and then summarizes the applications of MEC and deep learning in autonomous driving from three aspects: target detection, path planning, and collision avoidance, and finally discusses and outlooks the problems and challenges in current research.