Earticle

초록

영어

Autonomous driving algorithm aims to safely control the vehicle through recognition of the surrounding environment and relevant decision and control based on deep learning algorithms. However, the deep learning algorithms require appropriate datasets but many difficulties exist in generating proper datasets for critical scenarios, e.g. accident avoidance. This paper presents a method for generating datasets for critical scenarios via a game engine. The video game engine used in this paper offers an open world environment, providing realistic graphics. A variety of non-player characters in the game equipped with a high level of Artificial Intelligence (AI) are ideally suited for the reproduction of various unexpected situations in reality by the interaction between the AIs. Using this, this paper creates a high-level critical scenario dataset and presents an augmentation method for the generated dataset.

목차

Abstract
I. INTRODUCTION
II. THE PROPOSED APPROACH
A. Data Acquisition
B. Scenario Logging Algorithm
C. Critical Scenario Generation
D. Dataset Augmentation
III. EXPERIMENTS
IV. CONCLUSION
REFERENCES

키워드

저자

• Ji-Ung Im [ Department of Electrical and Computer Engineering Inha University Incheon, Korea ]
• Sang-Hoon Ahn [ Department of Electrical and Computer Engineering Inha University Incheon, Korea ]
• Jin-Gyu Ahn [ Department of Electrical and Computer Engineering Inha University Incheon, Korea ]
• Jong-Hoon Won [ School of Electronics Engineering Inha University Incheon, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004