Journal of Internet of Things and Convergence (사물인터넷융복합논문지)

The Korea Internet of Things Society (한국사물인터넷학회)
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End to End Autonomous Driving System using Out-layer Removal

Out-layer를 제거한 End to End 자율주행 시스템

  • Seung-Hyeok Jeong (Department of ICT Convergence System Engineering, Chonnam University ) ;
  • Dong-Ho Yun (Korea Institute of Industrial Technology ) ;
  • Sung-Hun Hong (Department of ICT Convergence System Engineering, Chonnam University)
  • 정승혁 (전남대학교 ICT융합시스템공학과 ) ;
  • 윤동호 (한국생산기술연구원 ) ;
  • 홍성훈 (전남대학교 ICT융합시스템공학과)
  • Received : 2022.12.21
  • Accepted : 2023.01.23
  • Published : 2023.02.28
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Abstract

In this paper, we propose an autonomous driving system using an end-to-end model to improve lane departure and misrecognition of traffic lights in a vision sensor-based system. End-to-end learning can be extended to a variety of environmental conditions. Driving data is collected using a model car based on a vision sensor. Using the collected data, it is composed of existing data and data with outlayers removed. A class was formed with camera image data as input data and speed and steering data as output data, and data learning was performed using an end-to-end model. The reliability of the trained model was verified. Apply the learned end-to-end model to the model car to predict the steering angle with image data. As a result of the learning of the model car, it can be seen that the model with the outlayer removed is improved than the existing model.

본 논문에서는 비전 센서 기반 시스템의 차선 이탈과 신호등 오인식 등을 개선하기 위해 End to End 모델을 활용한 자율주행 시스템을 제안한다. End to End 학습은 다양한 환경 조건에 대해 확장을 할 수 있다. 비전 센서 기반 모형 자동차를 이용하여 주행 데이터를 수집한다. 수집한 데이터를 이용하여 기존의 데이터와 아웃레이어를 제거한 데이터로 구성한다. 입력 데이터인 카메라 이미지 데이터, 출력 데이터인 속도와 조향 데이터로 클래스를 구성하고 End to End 모델을 활용하여 데이터 학습을 수행하였다. 학습된 모델의 신뢰성을 확인했다. 모형 자동차에 학습한 End to End 모델을 적용하여 이미지 데이터로 조향각을 예측한다. 모형 자동차의 학습 결과, 아웃레이어를 제거한 모델이 기존 모델보다 향상된 것을 볼 수 있다.

Keywords

Acknowledgement

본 논문은 2023년도 한국생산기술연구원 기관주요사업 "스마트 모빌리티 핵심 요소기술 개발(1/3))(kitech JA-23-0011)" 의 지원을 받아 수행된 것임

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