자료유형  

 학위논문

Control Number  

0017163998

International Standard Book Number  

9798384012870

Dewey Decimal Classification Number  

385

Main Entry-Personal Name  

Yi, Ran.

Publication, Distribution, etc. (Imprint  

[S.l.] : The University of Wisconsin - Madison., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

108 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-02, Section: B.

General Note  

Advisor: Ran, Bin.

Dissertation Note  

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2024.

Summary, Etc.  

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. 

Subject Added Entry-Topical Term  

Transportation.

Subject Added Entry-Topical Term  

Computer engineering.

Subject Added Entry-Topical Term  

Urban planning.

Subject Added Entry-Topical Term  

Automotive engineering.

Index Term-Uncontrolled  

Car-following

Index Term-Uncontrolled  

Connected automated vehicles

Index Term-Uncontrolled  

Mandatory lane-changing

Index Term-Uncontrolled  

Model predictive control

Index Term-Uncontrolled  

Spatial domain

Index Term-Uncontrolled  

Trajectory optimization

Added Entry-Corporate Name  

The University of Wisconsin - Madison Civil & Environmental Engr

Host Item Entry  

Dissertations Abstracts International. 86-02B.

Electronic Location and Access  

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Control Number  

joongbu:654121