{"id":{"repo_id":"ohiolink","oai_identifier":"oai:etd.ohiolink.edu:osu1365952195"},"canonical_url":"https://search.dev.ndltd.org/etd/ohiolink/oai:etd.ohiolink.edu:osu1365952195","repository":{"repo_id":"ohiolink","name":"OhioLINK","base_url":"https://etd.ohiolink.edu/acprod/odb_etd/ws/oai/oai"},"display":{"title":"Estimation of Driver Behavior for Autonomous Vehicle Applications","abstract":"Cyber-physical systems (CPS) refer to the co-joining of environmental and computational elements of a system. One CPS application area is in autonomous vehicles. Autonomous (or self-driving) vehicles are likely to be an upcoming revolution in personal and commercial transportation. While there are many outstanding public policy questions, this technology promises to improve our quality of life by providing transportation that is safe and efficient. A likely technology adoption path includes a period in which human driven and autonomous vehicles will need to coexist. In such an environment, referred to as a Mixed Urban Environment, autonomous vehicles may only be able to obtain information from human driven vehicles through on board sensors or vehicle-to-vehicle communication. From this information, an autonomous vehicle will need to determine the likely behavior of the human driven vehicle, a task which is referred to as driver behavior estimation. This task requires a qualitative-quantitative architecture capable of explaining the driver/vehicle coupling being observed. A vehicle's ability to determine other vehicle's likely behavior also has applications to driver safety and collision avoidance systems. In essence, a vehicle must be able to estimate the behavior of another vehicle, and determine its course of action. This thesis proposes an architecture for driver behavior estimation through the unified development of two theoretical concepts, namely: Graphical models, and Hybrid State Systems. Hybrid State Systems (HSS) provide the qualitative relationship between driver/vehicle couplings through a two layer model. Pattern recognition techniques in conjunction with Hidden Markov Models (HMMs), a type of graphical model, provide the quantitative relation between HSS layers. The estimation of current driver state is based on easy-to-measure continuous observations. The proposed system uses machine-learning concepts and requires extensive data collection, which is discussed. This thesis further provides an extension of the proposed system that includes external factors such as roadway type conditions in the decision making process. Results are provided for driver behavior estimation and system extension. A discussion of some of the public policy questions behind autonomous vehicles is also provided.","abstract_html":"Cyber-physical systems (CPS) refer to the co-joining of environmental and computational elements of a system. One CPS application area is in autonomous vehicles. Autonomous (or self-driving) vehicles are likely to be an upcoming revolution in personal and commercial transportation. While there are many outstanding public policy questions, this technology promises to improve our quality of life by providing transportation that is safe and efficient. A likely technology adoption path includes a period in which human driven and autonomous vehicles will need to coexist. In such an environment, referred to as a Mixed Urban Environment, autonomous vehicles may only be able to obtain information from human driven vehicles through on board sensors or vehicle-to-vehicle communication. From this information, an autonomous vehicle will need to determine the likely behavior of the human driven vehicle, a task which is referred to as driver behavior estimation. This task requires a qualitative-quantitative architecture capable of explaining the driver/vehicle coupling being observed. A vehicle's ability to determine other vehicle's likely behavior also has applications to driver safety and collision avoidance systems. In essence, a vehicle must be able to estimate the behavior of another vehicle, and determine its course of action. This thesis proposes an architecture for driver behavior estimation through the unified development of two theoretical concepts, namely: Graphical models, and Hybrid State Systems. Hybrid State Systems (HSS) provide the qualitative relationship between driver/vehicle couplings through a two layer model. Pattern recognition techniques in conjunction with Hidden Markov Models (HMMs), a type of graphical model, provide the quantitative relation between HSS layers. The estimation of current driver state is based on easy-to-measure continuous observations. The proposed system uses machine-learning concepts and requires extensive data collection, which is discussed. This thesis further provides an extension of the proposed system that includes external factors such as roadway type conditions in the decision making process. Results are provided for driver behavior estimation and system extension. A discussion of some of the public policy questions behind autonomous vehicles is also provided.","abstract_has_math":false,"creators":["Gadepally, Vijay Narasimha"],"institution":"The Ohio State University","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":["Krishnamurthy, Ashok"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-07-23","date_published":"2013-07-23","updated_at":"2026-07-24T03:37:31Z","subjects":["Electrical Engineering","Computer Engineering","Autonomous Vehicles","Self Driving Cars","Policy","Driver Behavior","Hidden Markov Models"],"languages":["English"],"rights":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://rave.ohiolink.edu/etdc/view?acc_num=osu1365952195","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Krishnamurthy, Ashok"]},{"key":"dc:creator","label":"Author","values":["Gadepally, Vijay Narasimha"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2013-07-23"]},{"key":"dc:publisher","label":"Institution","values":["The Ohio State University / OhioLINK"]},{"key":"dc:type","label":"Dc Type","values":["Electronic Thesis or Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer 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":["The Ohio State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical Engineering","Computer Engineering","Autonomous Vehicles","Self Driving Cars","Policy","Driver Behavior","Hidden Markov Models"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:rights","label":"Dc Rights","values":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://rave.ohiolink.edu/etdc/view?acc_num=osu1365952195"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Cyber-physical systems (CPS) refer to the co-joining of environmental and computational elements of a system. One CPS application area is in autonomous vehicles. Autonomous (or self-driving) vehicles are likely to be an upcoming revolution in personal and commercial transportation. While there are many outstanding public policy questions, this technology promises to improve our quality of life by providing transportation that is safe and efficient. A likely technology adoption path includes a period in which human driven and autonomous vehicles will need to coexist. In such an environment, referred to as a Mixed Urban Environment, autonomous vehicles may only be able to obtain information from human driven vehicles through on board sensors or vehicle-to-vehicle communication. From this information, an autonomous vehicle will need to determine the likely behavior of the human driven vehicle, a task which is referred to as driver behavior estimation. This task requires a qualitative-quantitative architecture capable of explaining the driver/vehicle coupling being observed. A vehicle's ability to determine other vehicle's likely behavior also has applications to driver safety and collision avoidance systems. In essence, a vehicle must be able to estimate the behavior of another vehicle, and determine its course of action. This thesis proposes an architecture for driver behavior estimation through the unified development of two theoretical concepts, namely: Graphical models, and Hybrid State Systems. Hybrid State Systems (HSS) provide the qualitative relationship between driver/vehicle couplings through a two layer model. Pattern recognition techniques in conjunction with Hidden Markov Models (HMMs), a type of graphical model, provide the quantitative relation between HSS layers. The estimation of current driver state is based on easy-to-measure continuous observations. The proposed system uses machine-learning concepts and requires extensive data collection, which is discussed. This thesis further provides an extension of the proposed system that includes external factors such as roadway type conditions in the decision making process. Results are provided for driver behavior estimation and system extension. A discussion of some of the public policy questions behind autonomous vehicles is also provided."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf","p.182","7.01 MB"]},{"key":"dc:title","label":"Title","values":["Estimation of Driver Behavior for Autonomous Vehicle Applications"]}]}],"canonical_facts":{"dc:contributor":["Krishnamurthy, Ashok"],"dc:creator":["Gadepally, Vijay Narasimha"],"dc:date":["2013-07-23"],"dc:description":["Cyber-physical systems (CPS) refer to the co-joining of environmental and computational elements of a system. One CPS application area is in autonomous vehicles. Autonomous (or self-driving) vehicles are likely to be an upcoming revolution in personal and commercial transportation. While there are many outstanding public policy questions, this technology promises to improve our quality of life by providing transportation that is safe and efficient. A likely technology adoption path includes a period in which human driven and autonomous vehicles will need to coexist. In such an environment, referred to as a Mixed Urban Environment, autonomous vehicles may only be able to obtain information from human driven vehicles through on board sensors or vehicle-to-vehicle communication. From this information, an autonomous vehicle will need to determine the likely behavior of the human driven vehicle, a task which is referred to as driver behavior estimation. This task requires a qualitative-quantitative architecture capable of explaining the driver/vehicle coupling being observed. A vehicle's ability to determine other vehicle's likely behavior also has applications to driver safety and collision avoidance systems. In essence, a vehicle must be able to estimate the behavior of another vehicle, and determine its course of action. This thesis proposes an architecture for driver behavior estimation through the unified development of two theoretical concepts, namely: Graphical models, and Hybrid State Systems. Hybrid State Systems (HSS) provide the qualitative relationship between driver/vehicle couplings through a two layer model. Pattern recognition techniques in conjunction with Hidden Markov Models (HMMs), a type of graphical model, provide the quantitative relation between HSS layers. The estimation of current driver state is based on easy-to-measure continuous observations. The proposed system uses machine-learning concepts and requires extensive data collection, which is discussed. This thesis further provides an extension of the proposed system that includes external factors such as roadway type conditions in the decision making process. Results are provided for driver behavior estimation and system extension. A discussion of some of the public policy questions behind autonomous vehicles is also provided."],"dc:format":["application/pdf","p.182","7.01 MB"],"dc:identifier":["http://rave.ohiolink.edu/etdc/view?acc_num=osu1365952195"],"dc:language":["English"],"dc:publisher":["The Ohio State University / OhioLINK"],"dc:rights":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."],"dc:subject":["Electrical Engineering","Computer Engineering","Autonomous Vehicles","Self Driving Cars","Policy","Driver Behavior","Hidden Markov Models"],"dc:title":["Estimation of Driver Behavior for Autonomous Vehicle Applications"],"dc:type":["Electronic Thesis or Dissertation"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["The Ohio State University"]},"updated_at":"2026-07-24T03:37:31Z"}