Autonomous Vehicle Perception Quality Assessment

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dc.contributor.authorZhang, Ceen
dc.contributor.committeechairEskandarian, Azimen
dc.contributor.committeememberAbaid, Nicoleen
dc.contributor.committeememberSouthward, Steve C.en
dc.contributor.committeememberAsbeck, Alan Thomasen
dc.contributor.committeememberAhmadian, Mehdien
dc.contributor.departmentMechanical Engineeringen
dc.date.accessioned2023-06-30T08:01:58Zen
dc.date.available2023-06-30T08:01:58Zen
dc.date.issued2023-06-29en
dc.description.abstractIn recent years, the rapid development of autonomous vehicles (AVs) has necessitated the need for high-quality perception systems. Perception is a fundamental requirement for AVs, with cameras and LiDARs being commonly used sensors for environmental understanding and localization. However, there is a research gap in assessing the quality of AVs perception systems. To address this gap, this dissertation proposes a novel paradigm for evaluating AVs perception quality by studying the perception quality of cameras and LiDARs sensors. Our proposed paradigm aims to provide a comprehensive assessment of the quality of perception systems used in AVs.To achieve our research goals, we first validate the concept of surrounding environmental complexity through subjective experiments that rate complexity scores. In this study, we propose a neural network to classify complexity. Subsequently, we study image-based perception quality assessment by using image saliency and 2D object detection algorithms to create an image-based quality index. We then develop a neural network model to regress the proposed quality index score. Furthermore, we extend our research to LiDAR-based point cloud quality assessment by using the image-based saliency map as guidance to generate a point cloud quality index score. We then develop a neural network model to regress the score. Finally, we validate the proposed perception quality index with a novel designed AVs perception algorithm. In conclusion, this dissertation makes a significant contribution to the field of AVs perception by proposing a new paradigm for assessing perception quality. Our research findings can be used to improve the overall performance and safety of AVs, which has significant implications for the transportation industry and society as a whole.en
dc.description.abstractgeneralThis dissertation delves into the fundamentals of autonomous vehicles (AVs), which is perception, with the aim of developing a new paradigm for evaluating the quality of perception algorithms. AVs are the dream of humanity, and perception is the fundamental requirement for achieving their full potential. Our research proposes a new approach to assessing the quality of perception algorithms, which can have significant implications for the performance and safety of AVs. By studying the perception algorithm quality, we aim to identify areas for improvement, leading to better AV performance and enhancing user trust. Our findings highlight the importance of perception in the development of AVs and demonstrate the need for continuous evaluation and improvement of the perception algorithms used in AVs.en
dc.description.degreeDoctor of Philosophyen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:37464en
dc.identifier.urihttp://hdl.handle.net/10919/115594en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectAutonomous Vehicleen
dc.subjectComputer Visionen
dc.subjectDeep Learningen
dc.subjectNeural Networken
dc.subjectImage Quality Assessmenten
dc.subjectPoint Cloud Quality Assessmenten
dc.titleAutonomous Vehicle Perception Quality Assessmenten
dc.typeDissertationen
thesis.degree.disciplineMechanical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.nameDoctor of Philosophyen

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