DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 김경수 | - |
dc.contributor.author | Cho, Minsu | - |
dc.contributor.author | 조민수 | - |
dc.date.accessioned | 2024-08-08T19:30:53Z | - |
dc.date.available | 2024-08-08T19:30:53Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1098111&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/321956 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 기계공학과, 2024.2,[viii, 161 p. :] | - |
dc.description.abstract | Nonlinear model predictive control (NMPC) is an efficient and proven method for optimization-based autonomous vehicle motion planning. In safety-critical control systems, controllers should address inequality-constrained optimization problems. In this work, we design a single unified constraint using an grid map. A single discrete barrier state is added to the system model to transform a constrained optimization problem into an unconstrained optimization problem. This approach can simplify complex motion planning problems and reduce computational cost. In addition, we defined a new safety metric for collision avoidance with dynamic objects and measured the risk using Conditional-Value-at-Risk (CVaR) to account for uncertainty. For safe motion planning that minimizes collision risk, we design a CVaR map, and design a single constraint that allows the vehicle to drive only in cells with risk below a predefined threshold. Finally, we propose a risk-aware motion planning method by applying it to the grid map-based integrated motion planning method. We verify the performance of the proposed method and the conventional MPC based motion planning method through real-time simulations in CarMaker and ROS environments. In the pop-up obstacle avoidance scenario and overtaking scenario in a dynamic environment, the proposed risk-aware motion planning method shows advantages in computation time and driving stability. Finally, the performance of the proposed method is experimented and verified on a real autonomous vehicle. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 자율주행차량 네비게이션▼a안전필수 모델예측제어▼a격자지도 기반 통합 모션플래닝▼a위험인지 모션플래닝 | - |
dc.subject | Autonomous navigation▼aSafety-critical MPC▼aGrid-based integrated motion planning▼aRisk-aware motion planning | - |
dc.title | Risk-aware motion planning with CVaR maps for collision avoidance in dynamic environments | - |
dc.title.alternative | 동적 환경에서 충돌 회피를 위한 CVaR 지도 기반 자율주행차량의 위험 인지 모션플래닝 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :기계공학과, | - |
dc.contributor.alternativeauthor | Kim, Kyung-Soo | - |
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