earticle

영어

As autonomous driving and connected car technology advance, various deep learning applications for autonomous vehicles and complex traffic situations are increasing. Autonomous vehicles must collect and process vast amounts of sensor data to support various deep learning applications, but vehicles have limited computing resources to perform complex deep learning operations. Therefore, edge computing is a promising solution to complement the limitations of autonomous vehicles. In this paper, we design edge computing for efficient task processing in an autonomous driving environment using a driving simulator. Also, we propose a task scheduling and offloading method which determines the target server to offload a task according to the characteristics of the task and the computing resources. The effectiveness of the proposed method is verified through experimental evaluation in an autonomous driving environment, supporting multiple deep learning services that we established by using a driving simulator.