자율주행을 위한 공간적 경향성이 고려된 End-to-End Neural Network 설계
- Title
- 자율주행을 위한 공간적 경향성이 고려된 End-to-End Neural Network 설계
- Other Titles
- End-to-End Neural Network Design with Spatial Dependencies for Autonomous Driving
- Author
- 정정주
- Keywords
- 인공 지능; 엔드투엔드 신경망; 컨볼루션 신경망; 공간적 경향성; 자율 주행; Artificial Intelligence; End-to-End Neural Network; Convolutional Neural Network; CNN; Spatial Dependency; Autonomous Driving
- Issue Date
- 2019-05
- Publisher
- 한국자동차공학회
- Citation
- 2019 한국자동차공학회 춘계학술대회 , Page. 614-619
- Abstract
- Recently, autonomous driving and advanced driver assistance system (ADAS) have been actively researched in the automotive field. It is very important to consider the improvement of perception ability about a forward driving scene. However, the sensor"s capability maintains high quality when the driving condition is only ideal. There are so many road conditions and an environment in real driving situations. In this paper, we propose a steering wheel angle prediction model using a deep convolutional end-to-end neural network for various driving environments and road shapes. The image data for training and validation is UDACITY Challenge dataset and the experiment was performed through computational simulation.
- URI
- http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE08747811https://repository.hanyang.ac.kr/handle/20.500.11754/111636
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > ELECTRICAL AND BIOMEDICAL ENGINEERING(전기·생체공학부) > Articles
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