{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/2039"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/2039","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Semi-autonomous driving control of multi-trailer articulated heavy vehicles for increasing highway traffic safety","abstract":"This PhD research introduces innovative semi-autonomous driving strategies for Multitrailer Articulated Heavy Vehicles (MTAHVs) to enhance highway safety and transportation efficiency. While autonomous driving has progressed, concerns about safety, reliability, and ethical issues remain, making the transition to full autonomy gradual. Unlike the extensive focus on semi-autonomous technologies for single-unit vehicles, limited attention has been given to Articulated Heavy Vehicles (AHVs), particularly MTAHVs, which have unique dynamics and a significantly higher accident risk. This study addresses the critical need for advancing semi-autonomous systems for AHVs and MTAHVs. To fill the research gap, this study proposes an innovative and cost-effective methodology for exploring semi-autonomous driving systems for MTAHVs. To this end, an MTAHV with the configuration of A-train double is selected as the subject vehicle. To design the tracking-controller and the driver, the A-train double is modeled as a single-track yaw-plane model using the Lagrange formalism and symbolically manipulated in Maple software. A Nonlinear Model Predictive Controller (NLMPC) is designed as the tracking controller via multi-objective optimization technique; a Modified Visual Driver Model (MVDM) incorporating driver time delay and Arm Neuromuscular System (NMS) dynamics is devised to represent the MTAHV driver. A comprehensive framework is introduced to bridge the gap between high-level decision-making and low-level control/driver systems for MTAHVs in highway scenarios. The framework features a multi-layer decision-maker developed using deep reinforcement learning (DRL), designed to coordinate with the NLMPC-based tracking controller or the MVDM-based driver. This collaboration enables the vehicle to navigate effectively by integrating the decision-maker's commands with the controller/driver's outputs, ensuring seamless interaction between decision-making and control systems for safe and efficient highway operation. Finally, a novel formulation for a semi-autonomous controller tailored to the unique challenges of MTAHVs is introduced. The proposed semi-autonomous system is evaluated using a benchmark for non-cooperative maneuvers, demonstrating the system’s capability to manage the complex, nonlinear dynamics of MTAHVs and effectively balance human and automated control objectives. This study provides an effective modeling-simulation based method for exploring semi-autonomous systems for MTAHV, and achieves insightful original findings, which may be used as the design guidelines for developing semiautonomous MTAHVs.","abstract_html":"This PhD research introduces innovative semi-autonomous driving strategies for Multitrailer Articulated Heavy Vehicles (MTAHVs) to enhance highway safety and transportation efficiency. While autonomous driving has progressed, concerns about safety, reliability, and ethical issues remain, making the transition to full autonomy gradual. Unlike the extensive focus on semi-autonomous technologies for single-unit vehicles, limited attention has been given to Articulated Heavy Vehicles (AHVs), particularly MTAHVs, which have unique dynamics and a significantly higher accident risk. This study addresses the critical need for advancing semi-autonomous systems for AHVs and MTAHVs. To fill the research gap, this study proposes an innovative and cost-effective methodology for exploring semi-autonomous driving systems for MTAHVs. To this end, an MTAHV with the configuration of A-train double is selected as the subject vehicle. To design the tracking-controller and the driver, the A-train double is modeled as a single-track yaw-plane model using the Lagrange formalism and symbolically manipulated in Maple software. A Nonlinear Model Predictive Controller (NLMPC) is designed as the tracking controller via multi-objective optimization technique; a Modified Visual Driver Model (MVDM) incorporating driver time delay and Arm Neuromuscular System (NMS) dynamics is devised to represent the MTAHV driver. A comprehensive framework is introduced to bridge the gap between high-level decision-making and low-level control/driver systems for MTAHVs in highway scenarios. The framework features a multi-layer decision-maker developed using deep reinforcement learning (DRL), designed to coordinate with the NLMPC-based tracking controller or the MVDM-based driver. This collaboration enables the vehicle to navigate effectively by integrating the decision-maker's commands with the controller/driver's outputs, ensuring seamless interaction between decision-making and control systems for safe and efficient highway operation. Finally, a novel formulation for a semi-autonomous controller tailored to the unique challenges of MTAHVs is introduced. The proposed semi-autonomous system is evaluated using a benchmark for non-cooperative maneuvers, demonstrating the system’s capability to manage the complex, nonlinear dynamics of MTAHVs and effectively balance human and automated control objectives. This study provides an effective modeling-simulation based method for exploring semi-autonomous systems for MTAHV, and achieves insightful original findings, which may be used as the design guidelines for developing semiautonomous MTAHVs.","abstract_has_math":false,"creators":["Ajorkar, Abbas"],"institution":"University of Ontario Institute of Technology","degree_name":"Doctor of Philosophy (PhD)","degree_level":null,"degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["He, Yuping"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12-01","date_published":"2025-12-01","updated_at":"2026-07-24T05:35:24Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/2039","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["He, Yuping"]},{"key":"dc:creator","label":"Author","values":["Ajorkar, Abbas"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-01-20T14:56:47Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-12-01"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/2039"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This PhD research introduces innovative semi-autonomous driving strategies for Multitrailer Articulated Heavy Vehicles (MTAHVs) to enhance highway safety and transportation efficiency. While autonomous driving has progressed, concerns about safety, reliability, and ethical issues remain, making the transition to full autonomy gradual. Unlike the extensive focus on semi-autonomous technologies for single-unit vehicles, limited attention has been given to Articulated Heavy Vehicles (AHVs), particularly MTAHVs, which have unique dynamics and a significantly higher accident risk. This study addresses the critical need for advancing semi-autonomous systems for AHVs and MTAHVs. To fill the research gap, this study proposes an innovative and cost-effective methodology for exploring semi-autonomous driving systems for MTAHVs. To this end, an MTAHV with the configuration of A-train double is selected as the subject vehicle. To design the tracking-controller and the driver, the A-train double is modeled as a single-track yaw-plane model using the Lagrange formalism and symbolically manipulated in Maple software. A Nonlinear Model Predictive Controller (NLMPC) is designed as the tracking controller via multi-objective optimization technique; a Modified Visual Driver Model (MVDM) incorporating driver time delay and Arm Neuromuscular System (NMS) dynamics is devised to represent the MTAHV driver. A comprehensive framework is introduced to bridge the gap between high-level decision-making and low-level control/driver systems for MTAHVs in highway scenarios. The framework features a multi-layer decision-maker developed using deep reinforcement learning (DRL), designed to coordinate with the NLMPC-based tracking controller or the MVDM-based driver. This collaboration enables the vehicle to navigate effectively by integrating the decision-maker's commands with the controller/driver's outputs, ensuring seamless interaction between decision-making and control systems for safe and efficient highway operation. Finally, a novel formulation for a semi-autonomous controller tailored to the unique challenges of MTAHVs is introduced. The proposed semi-autonomous system is evaluated using a benchmark for non-cooperative maneuvers, demonstrating the system’s capability to manage the complex, nonlinear dynamics of MTAHVs and effectively balance human and automated control objectives. This study provides an effective modeling-simulation based method for exploring semi-autonomous systems for MTAHV, and achieves insightful original findings, which may be used as the design guidelines for developing semiautonomous MTAHVs."]},{"key":"dc:title","label":"Title","values":["Semi-autonomous driving control of multi-trailer articulated heavy vehicles for increasing highway traffic safety"]}]}],"canonical_facts":{"dc:contributor.advisor":["He, Yuping"],"dc:creator":["Ajorkar, Abbas"],"dc:date.accessioned":["2026-01-20T14:56:47Z"],"dc:date.issued":["2025-12-01"],"dc:description.abstract":["This PhD research introduces innovative semi-autonomous driving strategies for Multitrailer Articulated Heavy Vehicles (MTAHVs) to enhance highway safety and transportation efficiency. While autonomous driving has progressed, concerns about safety, reliability, and ethical issues remain, making the transition to full autonomy gradual. Unlike the extensive focus on semi-autonomous technologies for single-unit vehicles, limited attention has been given to Articulated Heavy Vehicles (AHVs), particularly MTAHVs, which have unique dynamics and a significantly higher accident risk. This study addresses the critical need for advancing semi-autonomous systems for AHVs and MTAHVs. To fill the research gap, this study proposes an innovative and cost-effective methodology for exploring semi-autonomous driving systems for MTAHVs. To this end, an MTAHV with the configuration of A-train double is selected as the subject vehicle. To design the tracking-controller and the driver, the A-train double is modeled as a single-track yaw-plane model using the Lagrange formalism and symbolically manipulated in Maple software. A Nonlinear Model Predictive Controller (NLMPC) is designed as the tracking controller via multi-objective optimization technique; a Modified Visual Driver Model (MVDM) incorporating driver time delay and Arm Neuromuscular System (NMS) dynamics is devised to represent the MTAHV driver. A comprehensive framework is introduced to bridge the gap between high-level decision-making and low-level control/driver systems for MTAHVs in highway scenarios. The framework features a multi-layer decision-maker developed using deep reinforcement learning (DRL), designed to coordinate with the NLMPC-based tracking controller or the MVDM-based driver. This collaboration enables the vehicle to navigate effectively by integrating the decision-maker's commands with the controller/driver's outputs, ensuring seamless interaction between decision-making and control systems for safe and efficient highway operation. Finally, a novel formulation for a semi-autonomous controller tailored to the unique challenges of MTAHVs is introduced. The proposed semi-autonomous system is evaluated using a benchmark for non-cooperative maneuvers, demonstrating the system’s capability to manage the complex, nonlinear dynamics of MTAHVs and effectively balance human and automated control objectives. This study provides an effective modeling-simulation based method for exploring semi-autonomous systems for MTAHV, and achieves insightful original findings, which may be used as the design guidelines for developing semiautonomous MTAHVs."],"dc:identifier.uri":["https://hdl.handle.net/10155/2039"],"dc:language.iso":["en"],"dc:title":["Semi-autonomous driving control of multi-trailer articulated heavy vehicles for increasing highway traffic safety"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_name":["Doctor of Philosophy (PhD)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:24Z"}