자료유형  

 학위논문

Control Number  

0016935683

International Standard Book Number  

9798380373890

Dewey Decimal Classification Number  

621.3

Main Entry-Personal Name  

Zhong, Yuanxin.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of Michigan., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(129 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 85-03, Section: B.

General Note  

Advisor: Liu, Henry.

Dissertation Note  

Thesis (Ph.D.)--University of Michigan, 2023.

Restrictions on Access Note  

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

Restrictions on Access Note  

This item must not be added to any third party search indexes.

Summary, Etc.  

요약The perception system is a key component of AVs, as it relies on onboard sensors to gather information. However, the system faces challenges due to occlusions that obstruct visibility. Safely navigating in highly occluded scenarios remains a significant obstacle for AVs. In this dissertation, we present a systematic approach to address this issue by preparing AVs for fully occluded areas on the road.To effectively model the occluded areas, we introduce a joint object detection and semantic segmentation algorithm. This helps in acquiring critical environmental information with increased efficiency, enabling AVs to make decisions in the presence of occlusions. In tandem, we propose a semantic 3D mapping framework that efficiently identifies occlusions, which feeds into a layered 2D map, essential for planning, and contains the occlusion data. The experiments in SemanticKITTI dataset demonstrated that the proposed perception algorithms can generate a semantic grid map of the environment and identify the occluded grids efficiently and effectively.To tackle the occlusion problem and produce a safe plan for the AV, an occlusion-aware object management system is introduced to generate virtual road users for the planning algorithm, and near-optimal trajectories are solved using a sampling-based method while taking the presence of virtual objects into account. The experiments in a 2D toy driving environment showed the proposed planner can achieve better safety against baseline approaches while maintaining a reasonable passing speed in several challenging testing scenarios.Furthermore, the effectiveness of the proposed perception and planning framework is validated in both the Carla simulator and the physical Mcity testing facility, demonstrating the effectiveness of the proposed architecture and its superior safety performance over baseline approaches. Besides, a modular AV stack is described to guide the integration of the proposed perception and planning framework in the experiments.Validation experiment results show that the proposed framework results in a reduced crash rate in comparison to several baselines, including the renowned open-source AV framework, Autoware. Notably, these outcomes were realized without needing a High-Definition (HD) map with road geometry definitions.

Subject Added Entry-Topical Term  

Computer engineering.

Subject Added Entry-Topical Term  

Mechanical engineering.

Subject Added Entry-Topical Term  

Robotics.

Index Term-Uncontrolled  

Automated vehicle

Index Term-Uncontrolled  

Perception system

Index Term-Uncontrolled  

Motion planning

Index Term-Uncontrolled  

Risk assessment

Index Term-Uncontrolled  

Planning algorithm

Added Entry-Corporate Name  

University of Michigan Mechanical Engineering

Host Item Entry  

Dissertations Abstracts International. 85-03B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:642026