{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/79815"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/79815","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Exploring the Driver's Situation Awareness in a Dynamic Traffic Environment","abstract":"Safe driving requires effective monitoring, representing, and updating of the changing situation. This dissertation aims to study drivers' situation awareness of roadway traffic in the dynamic driving environment, in particular, their awareness of vehicles' spatial information and other properties. It also examines what factors influence the quality of this situation awareness. A change detection paradigm is employed in a single-monitor driving simulator: while driving, a discrete change (vehicle location, color, or identity) is occasionally made in one of the vehicles on the road ahead during a 150 ms blank-out period. The detection of a change indicates the awareness of that information, thus giving an indication of the driver's situation awareness. Experiment 1 explores the use of this method, and the results show that with a brief visual disruption, detection of all changes is greatly reduced, especially for location displacements. Experiment 2 replicates and extends the initial results, and also examines how the driving task itself influences the detection level. The results show that only for a very large location displacement (60%) can participants detect changes relatively well (comparable to that of color or identity change). Overall, the autonomous driving improves the detection performance. Finally, Experiment 3 investigates how this situation awareness is affected by secondary tasks of different types. The results show the negative effect of secondary tasks, with the spatial task producing greater interference than the verbal task. Moreover, both secondary tasks interfere more with the detection of the location displacements than with the detection of the vehicle color or identity changes. Taken together, the research findings suggest that because drivers retain reliable and robust awareness of vehicles' featural properties, e.g. color and identity, this can serve as the basis for monitoring vehicles' dynamic states, such as their locations, which are proposed to be encoded qualitatively in a few categories or distance zones. This situation awareness is influenced by vehicle environmental factors, such as whether the vehicle is moving or parked, or at near or far distance, as well as by the level of the driver's cognitive load. Real world implications are also discussed.","abstract_html":"Safe driving requires effective monitoring, representing, and updating of the changing situation. This dissertation aims to study drivers' situation awareness of roadway traffic in the dynamic driving environment, in particular, their awareness of vehicles' spatial information and other properties. It also examines what factors influence the quality of this situation awareness. A change detection paradigm is employed in a single-monitor driving simulator: while driving, a discrete change (vehicle location, color, or identity) is occasionally made in one of the vehicles on the road ahead during a 150 ms blank-out period. The detection of a change indicates the awareness of that information, thus giving an indication of the driver's situation awareness. Experiment 1 explores the use of this method, and the results show that with a brief visual disruption, detection of all changes is greatly reduced, especially for location displacements. Experiment 2 replicates and extends the initial results, and also examines how the driving task itself influences the detection level. The results show that only for a very large location displacement (60%) can participants detect changes relatively well (comparable to that of color or identity change). Overall, the autonomous driving improves the detection performance. Finally, Experiment 3 investigates how this situation awareness is affected by secondary tasks of different types. The results show the negative effect of secondary tasks, with the spatial task producing greater interference than the verbal task. Moreover, both secondary tasks interfere more with the detection of the location displacements than with the detection of the vehicle color or identity changes. Taken together, the research findings suggest that because drivers retain reliable and robust awareness of vehicles' featural properties, e.g. color and identity, this can serve as the basis for monitoring vehicles' dynamic states, such as their locations, which are proposed to be encoded qualitatively in a few categories or distance zones. This situation awareness is influenced by vehicle environmental factors, such as whether the vehicle is moving or parked, or at near or far distance, as well as by the level of the driver's cognitive load. Real world implications are also discussed.","abstract_has_math":false,"creators":["Zheng, Xianjun"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Education","degree_department":null,"school":null,"contributors":["McConkie, George W."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T19:53:51Z","date_published":"2015-09-25T19:53:51Z","updated_at":"2026-07-22T22:26:13Z","subjects":["Engineering, Industrial"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3153478"],"render_values":[{"text":"(MiAaPQ)AAI3153478","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/79815","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["McConkie, George W."]},{"key":"dc:creator","label":"Author","values":["Zheng, Xianjun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T19:53:51Z","10000-01-01","2004"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Education"]},{"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 at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Engineering, Industrial"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/79815","(MiAaPQ)AAI3153478"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Safe driving requires effective monitoring, representing, and updating of the changing situation. This dissertation aims to study drivers' situation awareness of roadway traffic in the dynamic driving environment, in particular, their awareness of vehicles' spatial information and other properties. It also examines what factors influence the quality of this situation awareness. A change detection paradigm is employed in a single-monitor driving simulator: while driving, a discrete change (vehicle location, color, or identity) is occasionally made in one of the vehicles on the road ahead during a 150 ms blank-out period. The detection of a change indicates the awareness of that information, thus giving an indication of the driver's situation awareness. Experiment 1 explores the use of this method, and the results show that with a brief visual disruption, detection of all changes is greatly reduced, especially for location displacements. Experiment 2 replicates and extends the initial results, and also examines how the driving task itself influences the detection level. The results show that only for a very large location displacement (60%) can participants detect changes relatively well (comparable to that of color or identity change). Overall, the autonomous driving improves the detection performance. Finally, Experiment 3 investigates how this situation awareness is affected by secondary tasks of different types. The results show the negative effect of secondary tasks, with the spatial task producing greater interference than the verbal task. Moreover, both secondary tasks interfere more with the detection of the location displacements than with the detection of the vehicle color or identity changes. Taken together, the research findings suggest that because drivers retain reliable and robust awareness of vehicles' featural properties, e.g. color and identity, this can serve as the basis for monitoring vehicles' dynamic states, such as their locations, which are proposed to be encoded qualitatively in a few categories or distance zones. This situation awareness is influenced by vehicle environmental factors, such as whether the vehicle is moving or parked, or at near or far distance, as well as by the level of the driver's cognitive load. Real world implications are also discussed.","Made available in DSpace on 2015-09-25T19:53:51Z (GMT). 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This dissertation aims to study drivers' situation awareness of roadway traffic in the dynamic driving environment, in particular, their awareness of vehicles' spatial information and other properties. It also examines what factors influence the quality of this situation awareness. A change detection paradigm is employed in a single-monitor driving simulator: while driving, a discrete change (vehicle location, color, or identity) is occasionally made in one of the vehicles on the road ahead during a 150 ms blank-out period. The detection of a change indicates the awareness of that information, thus giving an indication of the driver's situation awareness. Experiment 1 explores the use of this method, and the results show that with a brief visual disruption, detection of all changes is greatly reduced, especially for location displacements. Experiment 2 replicates and extends the initial results, and also examines how the driving task itself influences the detection level. The results show that only for a very large location displacement (60%) can participants detect changes relatively well (comparable to that of color or identity change). Overall, the autonomous driving improves the detection performance. Finally, Experiment 3 investigates how this situation awareness is affected by secondary tasks of different types. The results show the negative effect of secondary tasks, with the spatial task producing greater interference than the verbal task. Moreover, both secondary tasks interfere more with the detection of the location displacements than with the detection of the vehicle color or identity changes. Taken together, the research findings suggest that because drivers retain reliable and robust awareness of vehicles' featural properties, e.g. color and identity, this can serve as the basis for monitoring vehicles' dynamic states, such as their locations, which are proposed to be encoded qualitatively in a few categories or distance zones. This situation awareness is influenced by vehicle environmental factors, such as whether the vehicle is moving or parked, or at near or far distance, as well as by the level of the driver's cognitive load. Real world implications are also discussed.","Made available in DSpace on 2015-09-25T19:53:51Z (GMT). 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