{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129482"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129482","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Perception and sensor fusion in environments with uncertainty using Fuzzy Inference Systems","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01","abstract_has_math":false,"creators":["Sang, I-Chen"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Systems & Entrepreneurial Engr","degree_department":null,"school":null,"contributors":["Norris, William R","Sreenivas, Ramavarapu S","Hsiao-Wecksler, Elizabeth T","Beck, Carolyn L"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-01-10","date_published":"2025-01-10","updated_at":"2026-07-22T22:25:05Z","subjects":["adaptive","lane detection","AUV","autonomous vehicle","image processing","parameter-tuning","navigation","drivable region detection","CNN","adverse weather"],"languages":["en","eng"],"rights":["Copyright 2025 I-Chen Sang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129482","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Norris, William R","Sreenivas, Ramavarapu S","Hsiao-Wecksler, Elizabeth T","Beck, Carolyn L"]},{"key":"dc:creator","label":"Author","values":["Sang, I-Chen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-01-10","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Systems & Entrepreneurial Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["adaptive","lane detection","AUV","autonomous vehicle","image processing","parameter-tuning","navigation","drivable region detection","CNN","adverse weather"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 I-Chen Sang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129482"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01","The student, I-Chen Sang, accepted the attached license on 2024-12-12 at 16:27.","The student, I-Chen Sang, submitted this Dissertation for approval on 2024-12-12 at 16:40.","This Dissertation was approved for publication on 2025-01-10 at 16:02.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21588 on 2025-10-19 at 19:13:48","The escalating significance of autonomous vehicles in the realm of engineering underscores the pressing need for robust systems. Variations in weather conditions pose formidable challenges to on-road systems, while underwater vehicles contend with fluctuating sea currents and variable illumination levels, profoundly impacting their performance. Fuzzy Inference Systems (FIS), also referred to as expert systems, are widely employed in control applications. Their inherent probabilistic nature equips them to stabilize controllers and mitigate errors amidst noise. However, their application to perception, image, and point cloud data is still in its early stages. Thus, this dissertation concentrates on enhancing the perception and sensor fusion capabilities of autonomous vehicles within uncertain environments, leveraging FIS. This work encompasses three principal studies. Initially, a FIS was seamlessly integrated into an adaptive image-sonar sensor fusion framework, steering an Autonomous Underwater Vehicle through pipeline following/inspection tasks. Subsequently, FIS was integrated with image perception frameworks, fine-tuning intrinsic parameters in image processing algorithms to bolster lane detection in on-road vehicles facing adverse weather conditions. Lastly, FIS was deployed in a pixel-wise image-LiDAR sensor fusion framework, generating drivable region detection outcomes for on-road vehicles navigating snowy and rainy conditions. This dissertation presents three major contributions stemming from the fusion of FIS and perception algorithms. First, integrating FIS into sensor fusion navigation frameworks enhances the system noise tolerance. Second, embedding FIS within the parameter-tuning mechanism of image-processing algorithms broadens the scope of applications for perception algorithms. Finally, leveraging FIS and integration with sensor fusion-based drivable region detection marks a significant advancement in autonomous vehicle navigation under challenging environmental conditions."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Perception and sensor fusion in environments with uncertainty using Fuzzy Inference Systems"]}]}],"canonical_facts":{"dc:contributor":["Norris, William R","Sreenivas, Ramavarapu S","Hsiao-Wecksler, Elizabeth T","Beck, Carolyn L"],"dc:creator":["Sang, I-Chen"],"dc:date":["2025-01-10","2025-05"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01","The student, I-Chen Sang, accepted the attached license on 2024-12-12 at 16:27.","The student, I-Chen Sang, submitted this Dissertation for approval on 2024-12-12 at 16:40.","This Dissertation was approved for publication on 2025-01-10 at 16:02.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21588 on 2025-10-19 at 19:13:48","The escalating significance of autonomous vehicles in the realm of engineering underscores the pressing need for robust systems. Variations in weather conditions pose formidable challenges to on-road systems, while underwater vehicles contend with fluctuating sea currents and variable illumination levels, profoundly impacting their performance. Fuzzy Inference Systems (FIS), also referred to as expert systems, are widely employed in control applications. Their inherent probabilistic nature equips them to stabilize controllers and mitigate errors amidst noise. However, their application to perception, image, and point cloud data is still in its early stages. Thus, this dissertation concentrates on enhancing the perception and sensor fusion capabilities of autonomous vehicles within uncertain environments, leveraging FIS. This work encompasses three principal studies. Initially, a FIS was seamlessly integrated into an adaptive image-sonar sensor fusion framework, steering an Autonomous Underwater Vehicle through pipeline following/inspection tasks. Subsequently, FIS was integrated with image perception frameworks, fine-tuning intrinsic parameters in image processing algorithms to bolster lane detection in on-road vehicles facing adverse weather conditions. Lastly, FIS was deployed in a pixel-wise image-LiDAR sensor fusion framework, generating drivable region detection outcomes for on-road vehicles navigating snowy and rainy conditions. This dissertation presents three major contributions stemming from the fusion of FIS and perception algorithms. First, integrating FIS into sensor fusion navigation frameworks enhances the system noise tolerance. Second, embedding FIS within the parameter-tuning mechanism of image-processing algorithms broadens the scope of applications for perception algorithms. Finally, leveraging FIS and integration with sensor fusion-based drivable region detection marks a significant advancement in autonomous vehicle navigation under challenging environmental conditions."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129482"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 I-Chen Sang"],"dc:subject":["adaptive","lane detection","AUV","autonomous vehicle","image processing","parameter-tuning","navigation","drivable region detection","CNN","adverse weather"],"dc:title":["Perception and sensor fusion in environments with uncertainty using Fuzzy Inference Systems"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Systems & Entrepreneurial Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}