Earticle

초록

영어

In order to overcome the limitations on human accidents and reduce experimental costs during autonomous driving, studies using virtual simulator is conducted. In addition, by using open source-based frameworks and middleware, we develop a module that can be applied immediately to a real vehicle using a model developed for autonomous driving that has been studied in a virtual environment. In this paper, we use autonomous driving environment using a game engine-based CARLA simulator, and propose a model predictive control module by interlocking open-source ROS (Robot Operating System) and Autoware with the simulator. In addition, ROS manages vehicle information and sensor data and transmits them to the Autoware. In order to control autonomous driving, the control end part is implemented using MPC (Model Predictive Control) among Autoware APIs, which provides necessary functions such as 3D map generation and NDT matching algorithm for autonomous driving, location identification, object recognition, and vehicle control.

목차

요약
1. 서론
2. 관련연구
3. 제안한 방법
3.1. CARLA 시뮬레이터
3.2. ROS
3.3. AUTOWARE
3.4. 프로그램간 차량 정보 연동 과정
3.5 차량제어를 위한 모델 예측 제어
4. 실험
4.1. 실험 환경
4.2. 실험결과
5. 결론
사사문구
참고문헌

키워드

저자

• 이흥구 [ Heung-Gu Lee | Department of Electrical and Computer Engineering, Inha University, Incheon, South Korea ]
• 김덕환 [ Deok-Hwan Kim | Department of Electrical and Computer Engineering, Inha University, Incheon, South Korea ] 교신저자

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004