Research Trends Focused on End-to-End Learning Technologies for Autonomous Vehicles 


Vol. 49,  No. 11, pp. 1614-1630, Nov.  2024
10.7840/kics.2024.49.11.1614


PDF
  Abstract

As autonomous driving technology is researched as a core technology in the future automotive industry, recent advancements in related sensors, components, platforms, and artificial intelligence technologies have led to rapid progress in this field. This paper focuses on end-to-end learning technology, which has recently emerged as a major issue in autonomous driving technology. It explores the approach of replacing individual perception, decision-making, and control modules with a single integrated neural network. First, the paper examines the structure and limitations of traditional modular autonomous driving systems, which consist of separate perception, decision-making, and control modules. Then, it analyzes the structure of end-to-end learning autonomous driving systems, highlighting key technologies such as imitation learning and reinforcement learning, training methods, input and output formats, and evaluation methods and metrics. Finally, the paper reviews current technical issues and related structures of end-to-end learning, presenting various research cases and experimental results.

  Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


  Related Articles
  Cite this article

[IEEE Style]

J. Choi, Y. Park, W. Jun, S. Lee, "Research Trends Focused on End-to-End Learning Technologies for Autonomous Vehicles," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 11, pp. 1614-1630, 2024. DOI: 10.7840/kics.2024.49.11.1614.

[ACM Style]

Jeong-hwan Choi, Yechan Park, Woomin Jun, and Sungjin Lee. 2024. Research Trends Focused on End-to-End Learning Technologies for Autonomous Vehicles. The Journal of Korean Institute of Communications and Information Sciences, 49, 11, (2024), 1614-1630. DOI: 10.7840/kics.2024.49.11.1614.

[KICS Style]

Jeong-hwan Choi, Yechan Park, Woomin Jun, Sungjin Lee, "Research Trends Focused on End-to-End Learning Technologies for Autonomous Vehicles," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 11, pp. 1614-1630, 11. 2024. (https://doi.org/10.7840/kics.2024.49.11.1614)
Vol. 49, No. 11 Index

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)






pISSN : 1226 - 4717
eISSN : 2287 - 3880