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Constrained Connected Automated Vehicle Trajectory Planning: A Spatial Dynamics Perspective
Constrained Connected Automated Vehicle Trajectory Planning: A Spatial Dynamics Perspective
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152819
- ISBN
- 9798384012870
- DDC
- 385
- 저자명
- Yi, Ran.
- 서명/저자
- Constrained Connected Automated Vehicle Trajectory Planning: A Spatial Dynamics Perspective
- 발행사항
- [Sl] : The University of Wisconsin - Madison, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 108 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
- 주기사항
- Advisor: Ran, Bin.
- 학위논문주기
- Thesis (Ph.D.)--The University of Wisconsin - Madison, 2024.
- 초록/해제
- 요약This dissertation introduces a comprehensive trajectory optimization method for connected automated vehicles (CAVs) operating on curved roads, augmented by infrastructure support. We offer detailed strategies for car-following and lane-changing, crafted specifically for intricate road structures. Specifically, this paper systematically formulates trajectory optimization in a spatial domain and on a curvilinear coordinate. This unique approach allows for a dynamic formulation that can adeptly accommodate spatially diverse road geometries, traffic regulations, road obstacles, and the dynamics of leading vehicles. The acquisition of this intricate data is facilitated through both vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication channels. Our proposed strategies - encompassing trajectory optimization, car-following, and lane-changing - are underpinned by three foundational segments: i) An initial mathematical validation, confirming the controllability of our system and thereby ensuring its operational feasibility; ii) The employment of a multi-objective model predictive control (MPC) framework, devised to refine trajectories in a rolling horizon manner. This setup guarantees simultaneous adherence to collision avoidance, traffic regulations, and vehicular kinematic constraints; iii) To corroborate the efficacy of our approach, we undertook numerical simulations across a spectrum of scenarios. The derived results indicate that our method is adept at sculpting smooth vehicular trajectories, adeptly navigating around obstacles, and consistently complying with traffic regulations across varying circumstances. Notably, the method exhibits resilience against variations in road geometries and other potential disruptions. In essence, this paper presents a holistic solution for CAVs maneuvering on complex road topographies, ensuring safety, compliance, and efficiency in their operations.
- 일반주제명
- Transportation
- 일반주제명
- Computer engineering
- 일반주제명
- Urban planning
- 일반주제명
- Automotive engineering
- 키워드
- Car-following
- 키워드
- Spatial domain
- 기타저자
- The University of Wisconsin - Madison Civil & Environmental Engr
- 기본자료저록
- Dissertations Abstracts International. 86-02B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008250123s2024 us c eng d■001000017163998
■00520250211152819
■006m o d
■007cr#unu||||||||
■020 ▼a9798384012870
■035 ▼a(MiAaPQ)AAI31559110
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a385
■1001 ▼aYi, Ran.
■24510▼aConstrained Connected Automated Vehicle Trajectory Planning: A Spatial Dynamics Perspective
■260 ▼a[Sl]▼bThe University of Wisconsin - Madison▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a108 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-02, Section: B.
■500 ▼aAdvisor: Ran, Bin.
■5021 ▼aThesis (Ph.D.)--The University of Wisconsin - Madison, 2024.
■520 ▼aThis dissertation introduces a comprehensive trajectory optimization method for connected automated vehicles (CAVs) operating on curved roads, augmented by infrastructure support. We offer detailed strategies for car-following and lane-changing, crafted specifically for intricate road structures. Specifically, this paper systematically formulates trajectory optimization in a spatial domain and on a curvilinear coordinate. This unique approach allows for a dynamic formulation that can adeptly accommodate spatially diverse road geometries, traffic regulations, road obstacles, and the dynamics of leading vehicles. The acquisition of this intricate data is facilitated through both vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication channels. Our proposed strategies - encompassing trajectory optimization, car-following, and lane-changing - are underpinned by three foundational segments: i) An initial mathematical validation, confirming the controllability of our system and thereby ensuring its operational feasibility; ii) The employment of a multi-objective model predictive control (MPC) framework, devised to refine trajectories in a rolling horizon manner. This setup guarantees simultaneous adherence to collision avoidance, traffic regulations, and vehicular kinematic constraints; iii) To corroborate the efficacy of our approach, we undertook numerical simulations across a spectrum of scenarios. The derived results indicate that our method is adept at sculpting smooth vehicular trajectories, adeptly navigating around obstacles, and consistently complying with traffic regulations across varying circumstances. Notably, the method exhibits resilience against variations in road geometries and other potential disruptions. In essence, this paper presents a holistic solution for CAVs maneuvering on complex road topographies, ensuring safety, compliance, and efficiency in their operations.
■590 ▼aSchool code: 0262.
■650 4▼aTransportation
■650 4▼aComputer engineering
■650 4▼aUrban planning
■650 4▼aAutomotive engineering
■653 ▼aCar-following
■653 ▼aConnected automated vehicles
■653 ▼aMandatory lane-changing
■653 ▼aModel predictive control
■653 ▼aSpatial domain
■653 ▼aTrajectory optimization
■690 ▼a0709
■690 ▼a0464
■690 ▼a0999
■690 ▼a0540
■71020▼aThe University of Wisconsin - Madison▼bCivil & Environmental Engr.
■7730 ▼tDissertations Abstracts International▼g86-02B.
■790 ▼a0262
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17163998▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
