{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129560"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129560","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Addressing Behavior Model Inaccuracies for Safe Motion Control in Uncertain Dynamic Environments","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":["Sung, Minjun"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Hovakimyan, Naira"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-04-24","date_published":"2025-04-24","updated_at":"2026-07-22T22:25:05Z","subjects":["Motion Control","Robust/Adaptive Control","Planning under Uncertainty","Collision Avoidance"],"languages":["en","eng"],"rights":["Copyright 2025 Minjun Sung"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129560","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hovakimyan, Naira"]},{"key":"dc:creator","label":"Author","values":["Sung, Minjun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-04-24","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["Motion Control","Robust/Adaptive Control","Planning under Uncertainty","Collision Avoidance"]}]},{"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 Minjun Sung"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129560"]}]},{"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, Minjun Sung, accepted the attached license on 2025-04-23 at 16:08.","The student, Minjun Sung, submitted this Thesis for approval on 2025-04-23 at 16:16.","This Thesis was approved for publication on 2025-04-24 at 16:58.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21903 on 2025-10-19 at 19:15:25","Uncertainties in the environment and inaccuracies in behavior models critically affect the safety and reliability of autonomous systems in dynamic environment. These inaccuracies compromise the estimation of a dynamic obstacle’s state, leading to biased estimates and shifts in the predicted trajectory distributions. Such prediction errors, if unaddressed, may result in violations of safety constraints and degraded control performance. To address these challenges, we propose a novel framework called SIED-MPC (Simultaneous Input-Estimation and Distributionally robust Model Predictive Control), which unifies Simultaneous State and Input Estimation (SSIE) with Distributionally Robust Model Predictive Control (DR-MPC) through an adaptive model confidence evaluation scheme. Unlike conventional estimation techniques that assume access to accurate behavior models or treat prediction as an isolated module, our SSIE formulation jointly estimates both the obstacle’s state and the input gap—the discrepancy between predicted and actual control inputs—thus correcting for behavior model errors in real-time. This input gap serves as a quantitative proxy for model confidence, which is used to dynamically adjust the size of the ambiguity set in the DR-MPC formulation via a Wasserstein-based uncertainty radius. By integrating this feedback-driven adaptivity into the control pipeline, SIED-MPC systematically accounts for both estimation bias and distributional shift, ensuring safe operation with minimal conservatism. The proposed framework is evaluated in realistic autonomous driving simulations using CARLA. Our method demonstrates superior collision avoidance performance, lower constraint violation rates, and improved computational efficiency."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Addressing Behavior Model Inaccuracies for Safe Motion Control in Uncertain Dynamic Environments"]}]}],"canonical_facts":{"dc:contributor":["Hovakimyan, Naira"],"dc:creator":["Sung, Minjun"],"dc:date":["2025-04-24","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, Minjun Sung, accepted the attached license on 2025-04-23 at 16:08.","The student, Minjun Sung, submitted this Thesis for approval on 2025-04-23 at 16:16.","This Thesis was approved for publication on 2025-04-24 at 16:58.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21903 on 2025-10-19 at 19:15:25","Uncertainties in the environment and inaccuracies in behavior models critically affect the safety and reliability of autonomous systems in dynamic environment. These inaccuracies compromise the estimation of a dynamic obstacle’s state, leading to biased estimates and shifts in the predicted trajectory distributions. Such prediction errors, if unaddressed, may result in violations of safety constraints and degraded control performance. To address these challenges, we propose a novel framework called SIED-MPC (Simultaneous Input-Estimation and Distributionally robust Model Predictive Control), which unifies Simultaneous State and Input Estimation (SSIE) with Distributionally Robust Model Predictive Control (DR-MPC) through an adaptive model confidence evaluation scheme. Unlike conventional estimation techniques that assume access to accurate behavior models or treat prediction as an isolated module, our SSIE formulation jointly estimates both the obstacle’s state and the input gap—the discrepancy between predicted and actual control inputs—thus correcting for behavior model errors in real-time. This input gap serves as a quantitative proxy for model confidence, which is used to dynamically adjust the size of the ambiguity set in the DR-MPC formulation via a Wasserstein-based uncertainty radius. By integrating this feedback-driven adaptivity into the control pipeline, SIED-MPC systematically accounts for both estimation bias and distributional shift, ensuring safe operation with minimal conservatism. The proposed framework is evaluated in realistic autonomous driving simulations using CARLA. Our method demonstrates superior collision avoidance performance, lower constraint violation rates, and improved computational efficiency."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129560"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Minjun Sung"],"dc:subject":["Motion Control","Robust/Adaptive Control","Planning under Uncertainty","Collision Avoidance"],"dc:title":["Addressing Behavior Model Inaccuracies for Safe Motion Control in Uncertain Dynamic Environments"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}