{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1635"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1635","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Autonomous driving control strategies for multi-trailer articulated heavy vehicles with active safety system","abstract":"This study aims to develop automated driving strategies and integrated active control system for multi-trailer articulated heavy vehicles (MTAHVs) to enhance road transport efficiency, directional performance, and safety. To this end, a MTAHV with the configuration of A-train double was selected to be the subject vehicle, and the required vehicle models were generated. The corresponding nonlinear TruckSim model was employed as the virtual for co-simulations. The original contributions of the thesis in autonomous driving control of MTAHVs include: 1) a lateral preview driver model for MTAHVs was developed using the optimal preview control method; 2) a longitudinal motion-planning and control strategy using fuzzy sets was also devised; 3) an integrated control system was designed for coordinating autonomous driving and active trailer and dolly steering (ATDS) using a model predictive control (MPC) technique; and 4) a model-based predictive motion planning method was developed using the Frenet-Serret frame. The proposed lateral preview driver model may operate in two modes according to varied forward speed: i) in high-speed operations, the lateral stability is prioritized, and the high-speed and stability-oriented mode is activated; ii) while in low-speed curved path negotiations, the path-following off-tracking performance is emphasized, and the low-speed path-following mode is activated. It also takes benefits of the vehicle units’ body-fixed reference frames for lateral deviation calculations to mimic the driver’s local perception of vehicle position and reference path. If the so-called driver neuromuscular delay is set to zero, the driver model may perform as an autonomous human-like controller for vehicle lateral motion control. The devised longitudinal motion planner considers the road curvature over a preview horizon to regulate vehicle forward speed. It is featured with the predictive and compensatory throttle/brake actuations to assure all the vehicle units’ lateral stability. The MPC-based control method integrates the ATDS into the automated tractor steering and speed control, while the ATDS is activated to operate in either high-speed or low-speed mode, thereby improving the directional performance. The developed trajectory planner benefits from a model-based predictive approach to customize the generated trajectory to enhance the lateral stability in high-speed evasive maneuvers. The innovative findings of this dissertation will contribute to the advancement and development of autonomous driving control for MTAHVs.","abstract_html":"This study aims to develop automated driving strategies and integrated active control system for multi-trailer articulated heavy vehicles (MTAHVs) to enhance road transport efficiency, directional performance, and safety. To this end, a MTAHV with the configuration of A-train double was selected to be the subject vehicle, and the required vehicle models were generated. The corresponding nonlinear TruckSim model was employed as the virtual for co-simulations. The original contributions of the thesis in autonomous driving control of MTAHVs include: 1) a lateral preview driver model for MTAHVs was developed using the optimal preview control method; 2) a longitudinal motion-planning and control strategy using fuzzy sets was also devised; 3) an integrated control system was designed for coordinating autonomous driving and active trailer and dolly steering (ATDS) using a model predictive control (MPC) technique; and 4) a model-based predictive motion planning method was developed using the Frenet-Serret frame. The proposed lateral preview driver model may operate in two modes according to varied forward speed: i) in high-speed operations, the lateral stability is prioritized, and the high-speed and stability-oriented mode is activated; ii) while in low-speed curved path negotiations, the path-following off-tracking performance is emphasized, and the low-speed path-following mode is activated. It also takes benefits of the vehicle units’ body-fixed reference frames for lateral deviation calculations to mimic the driver’s local perception of vehicle position and reference path. If the so-called driver neuromuscular delay is set to zero, the driver model may perform as an autonomous human-like controller for vehicle lateral motion control. The devised longitudinal motion planner considers the road curvature over a preview horizon to regulate vehicle forward speed. It is featured with the predictive and compensatory throttle/brake actuations to assure all the vehicle units’ lateral stability. The MPC-based control method integrates the ATDS into the automated tractor steering and speed control, while the ATDS is activated to operate in either high-speed or low-speed mode, thereby improving the directional performance. The developed trajectory planner benefits from a model-based predictive approach to customize the generated trajectory to enhance the lateral stability in high-speed evasive maneuvers. The innovative findings of this dissertation will contribute to the advancement and development of autonomous driving control for MTAHVs.","abstract_has_math":false,"creators":["Rahimi, Amir"],"institution":"University of Ontario Institute of Technology","degree_name":"Doctor of Philosophy (PhD)","degree_level":null,"degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["He, Yuping"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05-01","date_published":"2023-05-01","updated_at":"2026-07-24T05:35:16Z","subjects":["Multi-trailer articulated heavy vehicles","Autonomous driving","Active trailer steering","Motion planning","Driver model"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1635","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":["Rahimi, Amir"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-06-13T18:49:36Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-06-13T18:49:36Z"]},{"key":"dc:date.issued","label":"Date","values":["2023-05-01"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer 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":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Multi-trailer articulated heavy vehicles","Autonomous driving","Active trailer steering","Motion planning","Driver model"]}]},{"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/10155/1635"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This study aims to develop automated driving strategies and integrated active control system for multi-trailer articulated heavy vehicles (MTAHVs) to enhance road transport efficiency, directional performance, and safety. To this end, a MTAHV with the configuration of A-train double was selected to be the subject vehicle, and the required vehicle models were generated. The corresponding nonlinear TruckSim model was employed as the virtual for co-simulations. The original contributions of the thesis in autonomous driving control of MTAHVs include: 1) a lateral preview driver model for MTAHVs was developed using the optimal preview control method; 2) a longitudinal motion-planning and control strategy using fuzzy sets was also devised; 3) an integrated control system was designed for coordinating autonomous driving and active trailer and dolly steering (ATDS) using a model predictive control (MPC) technique; and 4) a model-based predictive motion planning method was developed using the Frenet-Serret frame. The proposed lateral preview driver model may operate in two modes according to varied forward speed: i) in high-speed operations, the lateral stability is prioritized, and the high-speed and stability-oriented mode is activated; ii) while in low-speed curved path negotiations, the path-following off-tracking performance is emphasized, and the low-speed path-following mode is activated. It also takes benefits of the vehicle units’ body-fixed reference frames for lateral deviation calculations to mimic the driver’s local perception of vehicle position and reference path. If the so-called driver neuromuscular delay is set to zero, the driver model may perform as an autonomous human-like controller for vehicle lateral motion control. The devised longitudinal motion planner considers the road curvature over a preview horizon to regulate vehicle forward speed. It is featured with the predictive and compensatory throttle/brake actuations to assure all the vehicle units’ lateral stability. The MPC-based control method integrates the ATDS into the automated tractor steering and speed control, while the ATDS is activated to operate in either high-speed or low-speed mode, thereby improving the directional performance. The developed trajectory planner benefits from a model-based predictive approach to customize the generated trajectory to enhance the lateral stability in high-speed evasive maneuvers. The innovative findings of this dissertation will contribute to the advancement and development of autonomous driving control for MTAHVs."]},{"key":"dc:title","label":"Title","values":["Autonomous driving control strategies for multi-trailer articulated heavy vehicles with active safety system"]}]}],"canonical_facts":{"dc:contributor.advisor":["He, Yuping"],"dc:creator":["Rahimi, Amir"],"dc:date.accessioned":["2023-06-13T18:49:36Z"],"dc:date.available":["2023-06-13T18:49:36Z"],"dc:date.issued":["2023-05-01"],"dc:description.abstract":["This study aims to develop automated driving strategies and integrated active control system for multi-trailer articulated heavy vehicles (MTAHVs) to enhance road transport efficiency, directional performance, and safety. To this end, a MTAHV with the configuration of A-train double was selected to be the subject vehicle, and the required vehicle models were generated. The corresponding nonlinear TruckSim model was employed as the virtual for co-simulations. The original contributions of the thesis in autonomous driving control of MTAHVs include: 1) a lateral preview driver model for MTAHVs was developed using the optimal preview control method; 2) a longitudinal motion-planning and control strategy using fuzzy sets was also devised; 3) an integrated control system was designed for coordinating autonomous driving and active trailer and dolly steering (ATDS) using a model predictive control (MPC) technique; and 4) a model-based predictive motion planning method was developed using the Frenet-Serret frame. The proposed lateral preview driver model may operate in two modes according to varied forward speed: i) in high-speed operations, the lateral stability is prioritized, and the high-speed and stability-oriented mode is activated; ii) while in low-speed curved path negotiations, the path-following off-tracking performance is emphasized, and the low-speed path-following mode is activated. It also takes benefits of the vehicle units’ body-fixed reference frames for lateral deviation calculations to mimic the driver’s local perception of vehicle position and reference path. If the so-called driver neuromuscular delay is set to zero, the driver model may perform as an autonomous human-like controller for vehicle lateral motion control. The devised longitudinal motion planner considers the road curvature over a preview horizon to regulate vehicle forward speed. It is featured with the predictive and compensatory throttle/brake actuations to assure all the vehicle units’ lateral stability. The MPC-based control method integrates the ATDS into the automated tractor steering and speed control, while the ATDS is activated to operate in either high-speed or low-speed mode, thereby improving the directional performance. The developed trajectory planner benefits from a model-based predictive approach to customize the generated trajectory to enhance the lateral stability in high-speed evasive maneuvers. The innovative findings of this dissertation will contribute to the advancement and development of autonomous driving control for MTAHVs."],"dc:identifier.uri":["https://hdl.handle.net/10155/1635"],"dc:language.iso":["en"],"dc:subject":["Multi-trailer articulated heavy vehicles","Autonomous driving","Active trailer steering","Motion planning","Driver model"],"dc:title":["Autonomous driving control strategies for multi-trailer articulated heavy vehicles with active safety system"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_name":["Doctor of Philosophy (PhD)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:16Z"}