TY  - JOUR
T1  - Research Trends Focused on End-to-End Learning Technologies for Autonomous Vehicles
AU  - Choi, Jeong-hwan 
AU  - Park, Yechan 
AU  - Jun, Woomin 
AU  - Lee, Sungjin 
JO  - The Journal of Korean Institute of Communications and Information Sciences
PY  - 2024
DA  - 2024/1/1
DO  - 10.7840/kics.2024.49.11.1614
KW  - Autonomous Driving
KW  - End-to-End Learning
KW  - Modular Architecture
KW  - Perception
KW  - Planning
KW  - Control
AB  - 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.