{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/116110"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/116110","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Hybrid Friction Estimation based on Intelligent Tires and Vehicle Dynamics","abstract":"The control systems installed in modern vehicles lack crucial information regarding the interaction between the tires and the road surface. This knowledge gap significantly impacts the safety and control of the vehicle. Thus, to address this issue, this research introduces a novel fusion approach to estimate friction at the tire-road contact interface. This hybrid fusion friction estimation algorithm employs techniques like signal processing and machine learning, backed up by information from various vehicle and tire dynamics models, to develop algorithms that estimate the level of friction between the tire and the road. This fusion approach enables more precise estimations of the friction coefficient in both normal driving situations and scenarios involving sudden changes in speed or road conditions. Therefore, this research aids in enhancing vehicle safety and control by providing improved information about such tire-road interactions.","abstract_html":"The control systems installed in modern vehicles lack crucial information regarding the interaction between the tires and the road surface. This knowledge gap significantly impacts the safety and control of the vehicle. Thus, to address this issue, this research introduces a novel fusion approach to estimate friction at the tire-road contact interface. This hybrid fusion friction estimation algorithm employs techniques like signal processing and machine learning, backed up by information from various vehicle and tire dynamics models, to develop algorithms that estimate the level of friction between the tire and the road. This fusion approach enables more precise estimations of the friction coefficient in both normal driving situations and scenarios involving sudden changes in speed or road conditions. Therefore, this research aids in enhancing vehicle safety and control by providing improved information about such tire-road interactions.","abstract_has_math":false,"creators":["Gupta, Utkarsh"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Mechanical Engineering","degree_department":"Mechanical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Taheri, Saied"],"committee_members":["Sandu, Corina","Lee, Hojong","Ferris, John B."],"year":2023,"date_issued":"2023-08-24","date_published":"2023-08-24","updated_at":"2026-07-22T22:20:12Z","subjects":["Intelligent Tires","Vehicle Dynamics","Tire-Road Interactions","Road Surface","Coefficient of Friction","Brush Tire Model","Tire Slip Estimation"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:38366"],"render_values":[{"text":"vt_gsexam:38366","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/116110","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Taheri, Saied"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Sandu, Corina","Lee, Hojong","Ferris, John B."]},{"key":"dc:contributor.department","label":"Department","values":["Mechanical Engineering"]},{"key":"dc:creator","label":"Author","values":["Gupta, Utkarsh"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-08-25T08:00:39Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-08-25T08:00:39Z"]},{"key":"dc:date.issued","label":"Date","values":["2023-08-24"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Intelligent Tires","Vehicle Dynamics","Tire-Road Interactions","Road Surface","Coefficient of Friction","Brush Tire Model","Tire Slip Estimation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:38366"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/116110"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["The control systems installed in modern vehicles lack crucial information regarding the interaction between the tires and the road surface. This knowledge gap significantly impacts the safety and control of the vehicle. Thus, to address this issue, this research introduces a novel fusion approach to estimate friction at the tire-road contact interface. This hybrid fusion friction estimation algorithm employs techniques like signal processing and machine learning, backed up by information from various vehicle and tire dynamics models, to develop algorithms that estimate the level of friction between the tire and the road. This fusion approach enables more precise estimations of the friction coefficient in both normal driving situations and scenarios involving sudden changes in speed or road conditions. 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This knowledge gap significantly impacts the safety and control of the vehicle. Thus, to address this issue, this research introduces a novel fusion approach to estimate friction at the tire-road contact interface. This hybrid fusion friction estimation algorithm employs techniques like signal processing and machine learning, backed up by information from various vehicle and tire dynamics models, to develop algorithms that estimate the level of friction between the tire and the road. This fusion approach enables more precise estimations of the friction coefficient in both normal driving situations and scenarios involving sudden changes in speed or road conditions. 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