{"id":{"repo_id":"uwo","oai_identifier":"oai:uwo.scholaris.ca:20.500.14721/32721"},"canonical_url":"https://search.dev.ndltd.org/etd/uwo/oai:uwo.scholaris.ca:20.500.14721/32721","repository":{"repo_id":"uwo","name":"Western University","base_url":"https://uwo.scholaris.ca/server/oai/request"},"display":{"title":"Driver Behavior Analysis Based on Real On-Road Driving Data in the Design of Advanced Driving Assistance Systems","abstract":"The number of vehicles on the roads increases every day. According to the National Highway Traffic Safety Administration (NHTSA), the overwhelming majority of serious crashes (over 94 percent) are caused by human error. The broad aim of this research is to develop a driver behavior model using real on-road data in the design of Advanced Driving Assistance Systems (ADASs). For several decades, these systems have been a focus of many researchers and vehicle manufacturers in order to increase vehicle and road safety and assist drivers in different driving situations. Some studies have concentrated on drivers as the main actor in most driving circumstances. The way a driver monitors the traffic environment partially indicates the level of driver awareness. As an objective, we carry out a quantitative and qualitative analysis of driver behavior to identify the relationship between a driver’s intention and his/her actions. The RoadLAB project developed an instrumented vehicle equipped with On-Board Diagnostic systems (OBD-II), a stereo imaging system, and a non-contact eye tracker system to record some synchronized driving data of the driver cephalo-ocular behavior, the vehicle itself, and traffic environment. We analyze several behavioral features of the drivers to realize the potential relevant relationship between driver behavior and the anticipation of the next driver maneuver as well as to reach a better understanding of driver behavior while in the act of driving. Moreover, we detect and classify road lanes in the urban and suburban areas as they provide contextual information. Our experimental results show that our proposed models reached the F1 score of 84% and the accuracy of 94% for driver maneuver prediction and lane type classification respectively.","abstract_html":"The number of vehicles on the roads increases every day. According to the National Highway Traffic Safety Administration (NHTSA), the overwhelming majority of serious crashes (over 94 percent) are caused by human error. The broad aim of this research is to develop a driver behavior model using real on-road data in the design of Advanced Driving Assistance Systems (ADASs). For several decades, these systems have been a focus of many researchers and vehicle manufacturers in order to increase vehicle and road safety and assist drivers in different driving situations. Some studies have concentrated on drivers as the main actor in most driving circumstances. The way a driver monitors the traffic environment partially indicates the level of driver awareness. As an objective, we carry out a quantitative and qualitative analysis of driver behavior to identify the relationship between a driver’s intention and his/her actions. The RoadLAB project developed an instrumented vehicle equipped with On-Board Diagnostic systems (OBD-II), a stereo imaging system, and a non-contact eye tracker system to record some synchronized driving data of the driver cephalo-ocular behavior, the vehicle itself, and traffic environment. We analyze several behavioral features of the drivers to realize the potential relevant relationship between driver behavior and the anticipation of the next driver maneuver as well as to reach a better understanding of driver behavior while in the act of driving. Moreover, we detect and classify road lanes in the urban and suburban areas as they provide contextual information. Our experimental results show that our proposed models reached the F1 score of 84% and the accuracy of 94% for driver maneuver prediction and lane type classification respectively.","abstract_has_math":false,"creators":["Khairdoost, Nima"],"institution":"The University of Western Ontario","degree_name":"Ph D","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Steven S. Beauchemin","Michael A. 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In citing or referring to this thesis, use the DOI or handle from this page instead. Sample citation: Author name, \"Thesis title.\" (Year). Western University Open Repository. https://doi.org/10.71858/123456."]},{"key":"dc:description.abstract","label":"Abstract","values":["The number of vehicles on the roads increases every day. According to the National Highway Traffic Safety Administration (NHTSA), the overwhelming majority of serious crashes (over 94 percent) are caused by human error. The broad aim of this research is to develop a driver behavior model using real on-road data in the design of Advanced Driving Assistance Systems (ADASs). For several decades, these systems have been a focus of many researchers and vehicle manufacturers in order to increase vehicle and road safety and assist drivers in different driving situations. Some studies have concentrated on drivers as the main actor in most driving circumstances. The way a driver monitors the traffic environment partially indicates the level of driver awareness. As an objective, we carry out a quantitative and qualitative analysis of driver behavior to identify the relationship between a driver’s intention and his/her actions. The RoadLAB project developed an instrumented vehicle equipped with On-Board Diagnostic systems (OBD-II), a stereo imaging system, and a non-contact eye tracker system to record some synchronized driving data of the driver cephalo-ocular behavior, the vehicle itself, and traffic environment. We analyze several behavioral features of the drivers to realize the potential relevant relationship between driver behavior and the anticipation of the next driver maneuver as well as to reach a better understanding of driver behavior while in the act of driving. Moreover, we detect and classify road lanes in the urban and suburban areas as they provide contextual information. Our experimental results show that our proposed models reached the F1 score of 84% and the accuracy of 94% for driver maneuver prediction and lane type classification respectively."]},{"key":"dc:title","label":"Title","values":["Driver Behavior Analysis Based on Real On-Road Driving Data in the Design of Advanced Driving Assistance Systems"]}]}],"canonical_facts":{"dc:contributor.advisor":["Steven S. Beauchemin","Michael A. Bauer"],"dc:creator":["Khairdoost, Nima"],"dc:date.accessioned":["2025-07-10T19:35:28Z"],"dc:date.available":["2025-07-10T19:35:28Z"],"dc:date.issued":["2022-11-15"],"dc:description":["The thesis cover page in the PDF document includes references to Western University’s previous institutional repository platform, known as Scholarship@Western, and links to that platform (beginning with ir.lib.uwo.ca). In citing or referring to this thesis, use the DOI or handle from this page instead. Sample citation: Author name, \"Thesis title.\" (Year). 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As an objective, we carry out a quantitative and qualitative analysis of driver behavior to identify the relationship between a driver’s intention and his/her actions. The RoadLAB project developed an instrumented vehicle equipped with On-Board Diagnostic systems (OBD-II), a stereo imaging system, and a non-contact eye tracker system to record some synchronized driving data of the driver cephalo-ocular behavior, the vehicle itself, and traffic environment. We analyze several behavioral features of the drivers to realize the potential relevant relationship between driver behavior and the anticipation of the next driver maneuver as well as to reach a better understanding of driver behavior while in the act of driving. Moreover, we detect and classify road lanes in the urban and suburban areas as they provide contextual information. 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