(A) platform-independent hybrid control architecture for robust autonomous driving = 자율 주행을 위한 플랫폼 독립적 하이브리드 제어 아키텍쳐

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In this thesis, a platform-independent hybrid control architecture that generates control commands for lateral and longitudinal autonomous driving is proposed. Based on the deep neural network for the basic lane keeping autonomous driving, the End-to-End learning method which learns the nonlinear relation from the driving environment information to the final control command is applied to make robust driving in various driving environments. In addition, ICP matching algorithm is applied between driven trajectory using real time perception and global plan from start to destination, so it can compensate localization errors occurring during driving. Also, in order to solve the dependence on the platform which is a limitation of End-to-End learning approach, image transformation and vehicle modeling are performed. The performance of proposed architecture is verified through experiments in a real road environment.
Advisors
Shim, Hyunchulresearcher심현철researcher
Description
한국과학기술원 :미래자동차학제전공,
Publisher
한국과학기술원
Issue Date
2018
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 미래자동차학제전공, 2018.2,[v, 52 p. :]

Keywords

Autonomous Vehicle▼aEnd-to-End learning▼aICP Matching Algorithm▼aPlatform-Independent▼aHybrid control; 자율 주행 자동차▼aEnd-to-End 학습▼aICP 알고리듬▼a플랫폼 독립적▼a하이브리드 제어

URI
http://hdl.handle.net/10203/266113
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=733799&flag=dissertation
Appears in Collection
PD-Theses_Master(석사논문)
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