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It reviews sensor fusion foundations, then develops novel fusion architectures that adaptively integrate context via gating and multi-branch models. The work proposes methods for robust and efficient autonomous vehicle perception using HydraFusion, and further extends to improved motion planning through CASTNet for multimodal motion prediction and scene understanding. Experiments evaluate datasets, accuracy, and computational efficiency.","UC Irvine  \nUC Irvine Electronic Theses and Dissertations  \nTitle  \nMulti-Sensor Data Fusion and Machine Learning for Adaptive Autonomous Systems  \nPermalink  \n[https://escholarship.org/uc/item/1w8631sp](https://escholarship.org/uc/item/1w8631sp)  \nAuthor  \nMortlock, Trier  \nPublication Date  \n2024  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution License, available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \n[Peer reviewed|Thesis/dissertation](Peer reviewed|Thesis/dissertation)  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA,  \nIRVINE  \nMulti-Sensor Data Fusion and Machine Learning for Adaptive Autonomous Systems  \nDISSERTATION  \nsubmitted in partial satisfaction of the requirements for the degree of  \nDOCTOR OF PHILOSOPHY  \nin Mechanical and Aerospace Engineering  \nby  \nTrier Mortlock  \nDissertation Committee:  \nProfessor Mohammad Abdullah Al Faruque, Chair Professor Faryar Jabbari  \nProfessor Pramod P. Khargonekar  \nChapter 4 © 2022 ACM/IEEE ICCPS Chapter 5 © 2024 ACM TCPS Chapter 6 © 2024 IEEE TII  \nChapter 7 © 2024 IEEE T-ITS Portion of Chapter 8 © 2021 IEEE TETCAll other materials © 2024 Trier Mortlock  \nDEDICATION  \nTo my parents, for all their love and support. To my family and friends, for providing motivation and perspective. To Helen, for her love and for giving me purpose.  \nTABLE OF CONTENTS  \nPage  \nLIST OF FIGURES vii  \nLIST OF TABLES x  \nACKNOWLEDGMENTS xii  \nVITA xiii  \nABSTRACT OF THE DISSERTATION xvi  \n1 Introduction 1  \n1.1 Multi-Sensor Data Fusion ............................ 1  \n1.1.1 History ................................... 1  \n1.1.2 Basics ................................... 3  \n1.1.3 Motivation ................................. 5  \n1.1.4 Machine Learning ............................. 6  \n1.2 Research Scope—Adaptive Autonomous Systems ............... 7  \n1.3 Dissertation Structure .............................. 9  \n2 How to Fuse: Sensor Fusion in the Age of Artificial Intelligence 12  \n2.1 Introduction .................................... 12  \n2.2 Learning Objectives ................................ 13  \n2.2.1 Discriminative ............................... 13  \n2.2.2 Generative ................................. 14  \n2.2.3 Hybrid ................................... 15  \n2.3 Fusion Frameworks ................................ 15  \n2.3.1 Early Fusion ................................ 16  \n2.3.2 Late Fusion ................................ 16  \n2.3.3 Intermediate Fusion ............................ 17  \n3 A Novel Machine Learning Architecture for Multi-Sensor Data Fusion 18  \n3.1 Overview ...................................... 18  \n3.2 Problem Statement ................................ 20  \n3.3 Related Work ................................... 21  \n3.3.1 Selective and Context-Informed Sensor Fusion ............. 21  \n3.3.2 Multi-Branch Architectures ....................... 22  \n3.4 Proposed Framework ............................... 22  \n3.4.1 Workflow ................................. 23  \n3.4.2 Stem Model ................................ 23  \n3.4.3 Context-Aware Gating Model ...................... 24  \n3.4.4 Branch Models .............................. 25  \n3.4.5 Fusion Model ............................... 25  \n3.4.6 Why Fuse: Benefits of AdaMFusion ................... 26  \n4 Why Fuse: Robust and Efficient Autonomous Vehicle Perception 27  \n4.1 Autonomous Vehicle Perception ......................... 28  \n4.1.1 Motivational Example .......................... 31  \n4.2 Related Works on Perception .......................... 34  \n4.2.1 Sensor Fusion ............................... 34  \n4.2.2 Fusion in Object Detection Methods .................. 36  \n4.2.3 Multi-Branch Deep Learning Architectures ............... 36  \n4.3 Perception Problem Formulation ......................... 37  \n4.4 HydraFusion Methodology ..........","cbCaifSS6jzA47NB","https://ap.wps.com/l/cbCaifSS6jzA47NB","pdf",23827939,2,1,224,"English","en",105,"# Introduction\n## Multi-Sensor Data Fusion\n## Research Scope—Adaptive Autonomous Systems\n# How to Fuse: Sensor Fusion in the Age of Artificial Intelligence\n## Learning Objectives\n## Fusion Frameworks\n# A Novel Machine Learning Architecture for Multi-Sensor Data Fusion\n## Proposed Framework\n# Why Fuse: Robust and Efficient Autonomous Vehicle Perception\n## HydraFusion Methodology\n# Why Fuse: Improved Motion Planning and Scene Understanding\n## CASTNet Methodology","[{\"question\":\"What is the central problem of the dissertation?\",\"answer\":\"How to fuse multi-sensor data with machine learning in a way that supports adaptive autonomous systems for perception and planning tasks.\"},{\"question\":\"What fusion approach is introduced for autonomous vehicle perception?\",\"answer\":\"HydraFusion uses context identification with a gating module plus multi-branch architectures, then fuses the branches for object detection.\"},{\"question\":\"How does the dissertation improve motion-related understanding or planning?\",\"answer\":\"It introduces CASTNet methodology for multimodal motion prediction, using an ensemble of motion predictors and fusion of multi-modal predictions.\"}]","Multi-Sensor Data Fusion and Machine Learning for Adaptive Autonomous Systems - 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