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Autonomous System Safety: Towards Targeted and Scalable Approaches for Validation and Behaviour Analysis
Autonomous System Safety: Towards Targeted and Scalable Approaches for Validation and Behaviour Analysis
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260209102850
- ISBN
- 9798291563953
- DDC
- 621.3
- 저자명
- Du, Peter Boyu.
- 서명/저자
- Autonomous System Safety: Towards Targeted and Scalable Approaches for Validation and Behaviour Analysis
- 발행사항
- [Sl] : University of Illinois at Urbana-Champaign, 2023
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2023
- 형태사항
- 148 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-02, Section: B.
- 주기사항
- Advisor: Driggs-Campbell, Katherine.
- 학위논문주기
- Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2023.
- 초록/해제
- 요약Autonomous systems are rapidly making their way into the physical domain, where they have to interact with human users in unstructured and safety-critical scenarios. The complexity of the jobs autonomy is tasked with is reflected in the diversity of components that make up the software and hardware stacks of autonomous systems. This diversity enables the performance we see, but also poses a challenge when trying to analyze the safety and behaviour properties of the systems - preventing the feasibility of a one-size-fits-all approach. In this dissertation, we approach the problem of safety analysis through the development of targeted and scalable methodologies that can cater to various forms of autonomous system design.In the first part of this work, we introduce methods for the validation of deterministic and stochastic white box models. Using reachability analysis, we develop a real-time safety monitor for automated systems interacting with human agents. The monitor incorporates human motion prediction with data driven reachability to provide a statement of safety assurance with formal guarantees in an online manner. In the stochastic setting, we develop safety analysis methods through examining the exit-time distribution of dynamics governed by stochastic differential equations (SDEs). Given some appropriately defined SDE, the moments of exit-times from a safe set are computed through an infinite dimensional convex optimization, where the constraints consist of linear and PSD matrices, resulting in a semidefinite program (SDP).In the latter sections, we present methods for the validation and behaviour analysis of black and semi-black box systems. In particular, the problem of failure search is posed as a sequential decision making process, enabling the use of efficient reinforcement learning (RL) methods to uncover the failure space of the autonomy under test. Using similar adaptive search techniques, we address the comparison of autonomous agent policies through contrastive summaries. The structured search of behaviour summaries demonstrates both computational efficiency and high interpretability.
- 일반주제명
- Electrical engineering
- 일반주제명
- Computer engineering
- 일반주제명
- Computer science
- 키워드
- Safety
- 키워드
- Validation
- 키워드
- Machine learning
- 키워드
- Robotics
- 기타저자
- University of Illinois at Urbana-Champaign Electrical & Computer Eng
- 기본자료저록
- Dissertations Abstracts International. 87-02B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■0820 ▼a621.3
■1001 ▼aDu, Peter Boyu.
■24510▼aAutonomous System Safety: Towards Targeted and Scalable Approaches for Validation and Behaviour Analysis
■260 ▼a[Sl]▼bUniversity of Illinois at Urbana-Champaign▼c2023
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2023
■300 ▼a148 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-02, Section: B.
■500 ▼aAdvisor: Driggs-Campbell, Katherine.
■5021 ▼aThesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2023.
■520 ▼aAutonomous systems are rapidly making their way into the physical domain, where they have to interact with human users in unstructured and safety-critical scenarios. The complexity of the jobs autonomy is tasked with is reflected in the diversity of components that make up the software and hardware stacks of autonomous systems. This diversity enables the performance we see, but also poses a challenge when trying to analyze the safety and behaviour properties of the systems - preventing the feasibility of a one-size-fits-all approach. In this dissertation, we approach the problem of safety analysis through the development of targeted and scalable methodologies that can cater to various forms of autonomous system design.In the first part of this work, we introduce methods for the validation of deterministic and stochastic white box models. Using reachability analysis, we develop a real-time safety monitor for automated systems interacting with human agents. The monitor incorporates human motion prediction with data driven reachability to provide a statement of safety assurance with formal guarantees in an online manner. In the stochastic setting, we develop safety analysis methods through examining the exit-time distribution of dynamics governed by stochastic differential equations (SDEs). Given some appropriately defined SDE, the moments of exit-times from a safe set are computed through an infinite dimensional convex optimization, where the constraints consist of linear and PSD matrices, resulting in a semidefinite program (SDP).In the latter sections, we present methods for the validation and behaviour analysis of black and semi-black box systems. In particular, the problem of failure search is posed as a sequential decision making process, enabling the use of efficient reinforcement learning (RL) methods to uncover the failure space of the autonomy under test. Using similar adaptive search techniques, we address the comparison of autonomous agent policies through contrastive summaries. The structured search of behaviour summaries demonstrates both computational efficiency and high interpretability.
■590 ▼aSchool code: 0090.
■650 4▼aElectrical engineering
■650 4▼aComputer engineering
■650 4▼aComputer science
■653 ▼aAutonomous systems
■653 ▼aSafety
■653 ▼aValidation
■653 ▼aMachine learning
■653 ▼aRobotics
■690 ▼a0544
■690 ▼a0984
■690 ▼a0464
■71020▼aUniversity of Illinois at Urbana-Champaign▼bElectrical & Computer Eng.
■7730 ▼tDissertations Abstracts International▼g87-02B.
■790 ▼a0090
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17365893▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
