자료유형  

 학위논문

Control Number  

0016935354

International Standard Book Number  

9798380607384

Dewey Decimal Classification Number  

624

Main Entry-Personal Name  

Xu, Runsheng.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of California, Los Angeles., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(216 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 85-04, Section: B.

General Note  

Advisor: Ma, Jiaqi.

Dissertation Note  

Thesis (Ph.D.)--University of California, Los Angeles, 2023.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약Automated driving technology has emerged in recent years due to its potential to revolutionize transportation, bringing enhanced safety and efficiency. However, large-scale deployment is restricted by challenges inherent to single-vehicle systems, including occlusions, interactions with diverse traffic elements, and complicated decision-making. This dissertation advances the realm of Cooperative Driving Automation (CDA) as a solution, focusing on simulation frameworks and cooperative perception algorithms design.The research starts with introducing OpenCDA, a comprehensive simulation framework for CDA system prototyping, and OPV2V, the first large-scale simulated cooperative perception dataset. These tools address the need for a simulated environment to prototype and validate CDA algorithms, bridging existing gaps in cooperative perception advancement.Built upon OpenCDA and OPV2V, I present two state-of-the-art cooperative perception algorithms. The first, a cooperative 3D LiDAR detection framework, employs a Vision Transformer architecture to tackle challenges like sensor heterogeneity, localization error, and bandwidth constraints. The second, CoBEVT, is a pioneering multi-agent, multi-camera perception framework that uses economical RGB cameras to generate Bird-eye-view map predictions, offering a cost-effective solution.The final segment of the research emphasizes real-world deployment. I present V2V4Real, the first real-world dataset for V2V perception, detailing its comprehensive benchmarks and introducing novel tasks. Further, I delve into strategies to optimally train cooperative perception models using simulated data, introducing a novel module, the Homogeneous Training Augmenter, which demonstrates the efficacy of simulation in real-world applications.In essence, this thesis provides significant contributions to the domain of CDA, offering tools, datasets, and algorithms that pave the way for the broader, real-world implementation of cooperative automated driving.

Subject Added Entry-Topical Term  

Civil engineering.

Subject Added Entry-Topical Term  

Transportation.

Subject Added Entry-Topical Term  

Remote sensing.

Index Term-Uncontrolled  

Cooperative Driving Automation

Index Term-Uncontrolled  

Single-vehicle systems

Index Term-Uncontrolled  

Automated driving

Index Term-Uncontrolled  

Perception algorithms

Index Term-Uncontrolled  

Datasets

Added Entry-Corporate Name  

University of California, Los Angeles Civil and Environmental Engineering 0300

Host Item Entry  

Dissertations Abstracts International. 85-04B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:639540