{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/141064"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/141064","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Scenario-Based Methodology for Predicting the Safety Benefits of Emerging Advanced Rider Assistance Systems (ARAS)","abstract":"Motorcycle crashes remain a critical public health challenge in the United States (U.S.). Despite decades of progress in automotive safety, motorcyclists continue to experience disproportionately high rates of serious and fatal injuries. The combination of inherent vulnerability, limited physical protection, and exposure to complex traffic environments led the motorcycle fatality rate per registered vehicle in the U.S. to remain almost constant over the last forty years. This increasing concern has elevated interest in emerging Advanced Rider Assistance Systems (ARAS), which aim to reduce both motorcycle crash occurrence and riders' injury severity. However, unlike more mature safety technologies available for passenger vehicles, most motorcycle-specific ARAS remain in early development stages, and their real-world effectiveness is still largely unknown. Progress toward understanding their potential benefits is further constrained by motorcycle-specific limitations, such as the limited crash data available and the absence of a standardized framework for evaluating and comparing system benefits. To address these gaps, this dissertation presents a novel framework serving as the scientific foundation for ARAS benefit assessment, using Motorcycle Autonomous Emergency Braking (MAEB) as a proving case. Central to this work is a novel scenario-based methodology—adapted from automated-driving research—that integrates national crash statistics, in-depth crash reconstructions, naturalistic riding behavior, and computer-based simulation to produce the first U.S.-based ARAS safety-benefit estimation. Collectively, this work identifies critical opportunities to improve motorcycle safety through refined ARAS design, scenario-based evaluation, and human-aware control strategies. The scenario-based methodology developed here provides a data-driven scalable framework that allows manufacturers, regulators, and researchers to estimate safety benefits, refine activation logic, and inform the development of future motorcycle safety systems. Although centered on MAEB, the approach is generalizable to a wide range of emerging ARAS technologies and represents an important step toward reducing motorcycle injuries and fatalities on U.S. roads.","abstract_html":"Motorcycle crashes remain a critical public health challenge in the United States (U.S.). Despite decades of progress in automotive safety, motorcyclists continue to experience disproportionately high rates of serious and fatal injuries. The combination of inherent vulnerability, limited physical protection, and exposure to complex traffic environments led the motorcycle fatality rate per registered vehicle in the U.S. to remain almost constant over the last forty years. This increasing concern has elevated interest in emerging Advanced Rider Assistance Systems (ARAS), which aim to reduce both motorcycle crash occurrence and riders' injury severity. However, unlike more mature safety technologies available for passenger vehicles, most motorcycle-specific ARAS remain in early development stages, and their real-world effectiveness is still largely unknown. Progress toward understanding their potential benefits is further constrained by motorcycle-specific limitations, such as the limited crash data available and the absence of a standardized framework for evaluating and comparing system benefits. To address these gaps, this dissertation presents a novel framework serving as the scientific foundation for ARAS benefit assessment, using Motorcycle Autonomous Emergency Braking (MAEB) as a proving case. Central to this work is a novel scenario-based methodology—adapted from automated-driving research—that integrates national crash statistics, in-depth crash reconstructions, naturalistic riding behavior, and computer-based simulation to produce the first U.S.-based ARAS safety-benefit estimation. Collectively, this work identifies critical opportunities to improve motorcycle safety through refined ARAS design, scenario-based evaluation, and human-aware control strategies. The scenario-based methodology developed here provides a data-driven scalable framework that allows manufacturers, regulators, and researchers to estimate safety benefits, refine activation logic, and inform the development of future motorcycle safety systems. Although centered on MAEB, the approach is generalizable to a wide range of emerging ARAS technologies and represents an important step toward reducing motorcycle injuries and fatalities on U.S. roads.","abstract_has_math":false,"creators":["Terranova, Paolo"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Engineering Mechanics","degree_department":"Engineering Science and Mechanics","school":null,"contributors":[],"advisors":[],"committee_chairs":["Perez, Miguel A."],"committee_members":["Untaroiu, Costin D.","Guo, Feng","Doerzaph, Zachary Richard","Savino, Giovanni"],"year":2026,"date_issued":"2026-01-29","date_published":"2026-01-29","updated_at":"2026-07-22T22:18:46Z","subjects":["Road safety","Scenario-based methodology","Motorcycles","Powered two-wheelers (PTWs)","Crash injury","Driver behavior","Advanced Rider Assistance Systems (ARAS)","Active Safety","Simulation"],"languages":["en"],"rights":["Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International"],"rights_urls":["http://creativecommons.org/licenses/by-nc-nd/4.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45388"],"render_values":[{"text":"vt_gsexam:45388","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/141064","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Perez, Miguel A."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Untaroiu, Costin D.","Guo, Feng","Doerzaph, Zachary Richard","Savino, Giovanni"]},{"key":"dc:contributor.department","label":"Department","values":["Engineering Science and Mechanics"]},{"key":"dc:creator","label":"Author","values":["Terranova, Paolo"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-01-30T09:00:42Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-01-30T09:00:42Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-01-29"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering Mechanics"]},{"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":["Road safety","Scenario-based methodology","Motorcycles","Powered two-wheelers (PTWs)","Crash injury","Driver behavior","Advanced Rider Assistance Systems (ARAS)","Active Safety","Simulation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://creativecommons.org/licenses/by-nc-nd/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45388"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/141064"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Motorcycle crashes remain a critical public health challenge in the United States (U.S.). Despite decades of progress in automotive safety, motorcyclists continue to experience disproportionately high rates of serious and fatal injuries. The combination of inherent vulnerability, limited physical protection, and exposure to complex traffic environments led the motorcycle fatality rate per registered vehicle in the U.S. to remain almost constant over the last forty years. This increasing concern has elevated interest in emerging Advanced Rider Assistance Systems (ARAS), which aim to reduce both motorcycle crash occurrence and riders' injury severity. However, unlike more mature safety technologies available for passenger vehicles, most motorcycle-specific ARAS remain in early development stages, and their real-world effectiveness is still largely unknown. Progress toward understanding their potential benefits is further constrained by motorcycle-specific limitations, such as the limited crash data available and the absence of a standardized framework for evaluating and comparing system benefits. To address these gaps, this dissertation presents a novel framework serving as the scientific foundation for ARAS benefit assessment, using Motorcycle Autonomous Emergency Braking (MAEB) as a proving case. Central to this work is a novel scenario-based methodology—adapted from automated-driving research—that integrates national crash statistics, in-depth crash reconstructions, naturalistic riding behavior, and computer-based simulation to produce the first U.S.-based ARAS safety-benefit estimation. Collectively, this work identifies critical opportunities to improve motorcycle safety through refined ARAS design, scenario-based evaluation, and human-aware control strategies. The scenario-based methodology developed here provides a data-driven scalable framework that allows manufacturers, regulators, and researchers to estimate safety benefits, refine activation logic, and inform the development of future motorcycle safety systems. Although centered on MAEB, the approach is generalizable to a wide range of emerging ARAS technologies and represents an important step toward reducing motorcycle injuries and fatalities on U.S. roads."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Motorcycle crashes remain a critical public health challenge in the United States (U.S.). Despite decades of progress in automotive safety, motorcyclists continue to experience disproportionately high rates of serious and fatal injuries. The combination of inherent vulnerability, limited physical protection, and exposure to complex traffic environments led the motorcycle fatality rate per registered vehicle in the U.S. to remain almost constant over the last forty years. This increasing concern has elevated interest in emerging Advanced Rider Assistance Systems (ARAS), which aim to reduce both motorcycle crash occurrence and riders' injury severity. However, unlike more mature safety technologies available for passenger vehicles, most motorcycle-specific ARAS remain in early development stages, and their real-world effectiveness is still largely unknown. Progress toward understanding their potential benefits is further constrained by motorcycle-specific limitations, such as the limited crash data available and the absence of a standardized framework for evaluating and comparing system benefits. To address these gaps, this dissertation presents a novel framework serving as the scientific foundation for ARAS benefit assessment, using Motorcycle Autonomous Emergency Braking (MAEB) as a proving case. Central to this work is a novel scenario-based methodology—adapted from automated-driving research—that integrates national crash statistics, in-depth crash reconstructions, naturalistic riding behavior, and computer-based simulation to produce the first U.S.-based ARAS safety-benefit estimation. Collectively, this work identifies critical opportunities to improve motorcycle safety through refined ARAS design, scenario-based evaluation, and human-aware control strategies. The scenario-based methodology developed here provides a data-driven scalable framework that allows manufacturers, regulators, and researchers to estimate safety benefits, refine activation logic, and inform the development of future motorcycle safety systems. Although centered on MAEB, the approach is generalizable to a wide range of emerging ARAS technologies and represents an important step toward reducing motorcycle injuries and fatalities on U.S. roads."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Doctor of Philosophy"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Scenario-Based Methodology for Predicting the Safety Benefits of Emerging Advanced Rider Assistance Systems (ARAS)"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Perez, Miguel A."],"dc:contributor.committeemember":["Untaroiu, Costin D.","Guo, Feng","Doerzaph, Zachary Richard","Savino, Giovanni"],"dc:contributor.department":["Engineering Science and Mechanics"],"dc:creator":["Terranova, Paolo"],"dc:date.accessioned":["2026-01-30T09:00:42Z"],"dc:date.available":["2026-01-30T09:00:42Z"],"dc:date.issued":["2026-01-29"],"dc:description.abstract":["Motorcycle crashes remain a critical public health challenge in the United States (U.S.). Despite decades of progress in automotive safety, motorcyclists continue to experience disproportionately high rates of serious and fatal injuries. The combination of inherent vulnerability, limited physical protection, and exposure to complex traffic environments led the motorcycle fatality rate per registered vehicle in the U.S. to remain almost constant over the last forty years. This increasing concern has elevated interest in emerging Advanced Rider Assistance Systems (ARAS), which aim to reduce both motorcycle crash occurrence and riders' injury severity. However, unlike more mature safety technologies available for passenger vehicles, most motorcycle-specific ARAS remain in early development stages, and their real-world effectiveness is still largely unknown. Progress toward understanding their potential benefits is further constrained by motorcycle-specific limitations, such as the limited crash data available and the absence of a standardized framework for evaluating and comparing system benefits. To address these gaps, this dissertation presents a novel framework serving as the scientific foundation for ARAS benefit assessment, using Motorcycle Autonomous Emergency Braking (MAEB) as a proving case. Central to this work is a novel scenario-based methodology—adapted from automated-driving research—that integrates national crash statistics, in-depth crash reconstructions, naturalistic riding behavior, and computer-based simulation to produce the first U.S.-based ARAS safety-benefit estimation. Collectively, this work identifies critical opportunities to improve motorcycle safety through refined ARAS design, scenario-based evaluation, and human-aware control strategies. The scenario-based methodology developed here provides a data-driven scalable framework that allows manufacturers, regulators, and researchers to estimate safety benefits, refine activation logic, and inform the development of future motorcycle safety systems. Although centered on MAEB, the approach is generalizable to a wide range of emerging ARAS technologies and represents an important step toward reducing motorcycle injuries and fatalities on U.S. roads."],"dc:description.abstractgeneral":["Motorcycle crashes remain a critical public health challenge in the United States (U.S.). Despite decades of progress in automotive safety, motorcyclists continue to experience disproportionately high rates of serious and fatal injuries. The combination of inherent vulnerability, limited physical protection, and exposure to complex traffic environments led the motorcycle fatality rate per registered vehicle in the U.S. to remain almost constant over the last forty years. This increasing concern has elevated interest in emerging Advanced Rider Assistance Systems (ARAS), which aim to reduce both motorcycle crash occurrence and riders' injury severity. However, unlike more mature safety technologies available for passenger vehicles, most motorcycle-specific ARAS remain in early development stages, and their real-world effectiveness is still largely unknown. Progress toward understanding their potential benefits is further constrained by motorcycle-specific limitations, such as the limited crash data available and the absence of a standardized framework for evaluating and comparing system benefits. To address these gaps, this dissertation presents a novel framework serving as the scientific foundation for ARAS benefit assessment, using Motorcycle Autonomous Emergency Braking (MAEB) as a proving case. Central to this work is a novel scenario-based methodology—adapted from automated-driving research—that integrates national crash statistics, in-depth crash reconstructions, naturalistic riding behavior, and computer-based simulation to produce the first U.S.-based ARAS safety-benefit estimation. Collectively, this work identifies critical opportunities to improve motorcycle safety through refined ARAS design, scenario-based evaluation, and human-aware control strategies. The scenario-based methodology developed here provides a data-driven scalable framework that allows manufacturers, regulators, and researchers to estimate safety benefits, refine activation logic, and inform the development of future motorcycle safety systems. Although centered on MAEB, the approach is generalizable to a wide range of emerging ARAS technologies and represents an important step toward reducing motorcycle injuries and fatalities on U.S. roads."],"dc:description.degree":["Doctor of Philosophy"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:45388"],"dc:identifier.uri":["https://hdl.handle.net/10919/141064"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International"],"dc:rights.uri":["http://creativecommons.org/licenses/by-nc-nd/4.0/"],"dc:subject":["Road safety","Scenario-based methodology","Motorcycles","Powered two-wheelers (PTWs)","Crash injury","Driver behavior","Advanced Rider Assistance Systems (ARAS)","Active Safety","Simulation"],"dc:title":["Scenario-Based Methodology for Predicting the Safety Benefits of Emerging Advanced Rider Assistance Systems (ARAS)"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Engineering Mechanics"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:18:46Z"}