Emergency collision avoidance/mitigation system: motion planning and control of autonomous vehicle긴급 충돌 회피/경감 시스템: 자율주행 자동차의 주행 계획 및 제어

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Despite globally overwhelming research and development efforts towards autonomous vehicle technologies, the number of collisions and driver interventions of autonomous vehicles tested in California seems to be reaching a plateau. One of the main reasons for this is the lack of defensive driving functionality; i.e. emergency collision avoidance when other human drivers make mistakes. In this study, a Collision Avoidance/Mitigation System (CAMS) is proposed to rapidly evaluate risks associated with all surrounding vehicles and then maneuver the vehicle into a safer region when faced with critically dangerous situations. The CAMS consists of predicting future situations, collision risk assessment, collision avoidance trajectory planning, and path tracking control.A risk assessment module, namely Predictive Occupancy Map (POM), is proposed in order to compute potential risks associated with surrounding vehicles based on relative position, velocity, and acceleration. In order to avoid dangerous situations, collision avoidance trajectory planning that considers the situation of the surrounding space is necessary. The safe trajectory planning method, which uses model predictive planning (MPP), is proposed. A model-based Linear Quadratic Gaussian (LQG) Control with adaptive Q-matrix is proposed to efficiently and systematically design the path tracking controller for any given target vehicle while effectively handling the noise and error problems arise from the localization and path planning algorithms. The performance limits and trade-offs between three performance criteria (lane tracking, stability robustness, and passenger comfort) are first investigated by exploring the entire design space of three prominent controllers. i.e.,PID, LQG, and $H_∞$. Through this study, the collision avoidance/mitigation system that avoid threat of surrounding vehicle is developed. The performance of the CAMS is validated based on scenario simulations. The performance of tracking controller is validated base on KAIST autonomous vehicle. It is expected that autonomous vehicles can drive safely without severe accident.
Advisors
Kum, Dongsukresearcher금동석researcher
Description
한국과학기술원 :조천식녹색교통대학원,
Publisher
한국과학기술원
Issue Date
2020
Identifier
325007
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 조천식녹색교통대학원, 2020.2,[ix, 134 p. :]

Keywords

Autonomous vehicle; Advanced driver assist system (ADAS); Collision avoidance; Risk assessment; Robust control; Motion planning; 자율주행 자동차; 운전자 지원 시스템; 충돌 회피; 위험도 평가; 강건 제어; 주행 계획

URI
http://hdl.handle.net/10203/284556
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=947941&flag=dissertation
Appears in Collection
GT-Theses_Ph.D.(박사논문)
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