Earticle

초록

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.

목차

Abstract
I. INTRODUCTION
II. METHODS
A. Design of Edge Computing
B. Task Scheduling
C. Taks Offloading
III. EXPERIMENTAL RESULTS
A. Measurement of Task Scheduling Time
B. Evaluation of Task Offloading
IV. CONCLUSIONS AND FUTURE WORKS
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Jihye Jang [ Department of Electrical and Computer Engineering Inha University ]
• Deok-Hwan Kim [ Department of Electronic Engineering Inha University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004