{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129188"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129188","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Evaluating and comparing longitudinal control strategies for autonomous vehicles","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_has_math":false,"creators":["Cho, Louis Sungwoo"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Talebpour, Alireza"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05-06","date_published":"2025-05-06","updated_at":"2026-07-22T22:25:04Z","subjects":["Spacing control","Autonomous vehicles","Genetic algorithm"],"languages":["en","eng"],"rights":["Copyright 2025 Louis Cho"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129188","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Talebpour, Alireza"]},{"key":"dc:creator","label":"Author","values":["Cho, Louis Sungwoo"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-05-06","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Spacing control","Autonomous vehicles","Genetic algorithm"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Louis Cho"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129188"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Louis Cho, accepted the attached license on 2025-05-05 at 14:24.","The student, Louis Cho, submitted this Thesis for approval on 2025-05-05 at 14:36.","This Thesis was approved for publication on 2025-05-06 at 15:20.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21716 on 2025-10-19 at 18:09:16","Maintaining appropriate inter-vehicle distances is crucial in enhancing the safety and efficiency of traffic flow for autonomous vehicles (AV). Various spacing control policies have been introduced in the literature. These policies affect traffic stability and dynamics differently, creating variations in road capacity and driving patterns in mixed traffic. This comparative study analyzes five common policies utilized in the literature: Constant Spacing Policy (CSP), Constant Time Headway (CTH), Traffic Flow Stability (TFS), Constant Safety Factor (CSF), and the Intelligent Driver Model (IDM), and compares their applicability to modeling real-world AV behavior. Accordingly, each model is calibrated using the genetic algorithm for optimizing the parameters of each spacing policy. Three distinct datasets were utilized for calibration: (1) Third Generation Simulation (TGSIM) data from I-294L1 in Chicago that contains SAE Level 1 AVs, (2) TGSIM data from I-90/94 containing SAE Level 2 AVs, and (3) data collected from Level 4 AV operations in Phoenix, AZ. Calibration and simulation results show that CSP and CSF models had the most consistent performance, achieving the lowest RMSE and highest R2 values. The CSP model achieved a good level of performance under low-density highway conditions by maintaining consistent vehicle spacing with minimal perturbations. The CSF policy was determined to be the most optimal policy for high-density traffic, effectively managing safety-critical scenarios involving abrupt acceleration and braking. The CTH model showed reliable performance though it was sensitive to speed fluctuations, resulting in higher errors during dynamic traffic scenarios particularly in stop-and-go traffic. The TFS model consistently had higher errors, reflecting its limited adaptability to congested or complex traffic environments. The IDM model demonstrated strong adaptability and realistic driving behavior across diverse conditions but required precise calibration to achieve optimal performance in high-density and unpredictable scenarios."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Evaluating and comparing longitudinal control strategies for autonomous vehicles"]}]}],"canonical_facts":{"dc:contributor":["Talebpour, Alireza"],"dc:creator":["Cho, Louis Sungwoo"],"dc:date":["2025-05-06","2025-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Louis Cho, accepted the attached license on 2025-05-05 at 14:24.","The student, Louis Cho, submitted this Thesis for approval on 2025-05-05 at 14:36.","This Thesis was approved for publication on 2025-05-06 at 15:20.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21716 on 2025-10-19 at 18:09:16","Maintaining appropriate inter-vehicle distances is crucial in enhancing the safety and efficiency of traffic flow for autonomous vehicles (AV). Various spacing control policies have been introduced in the literature. These policies affect traffic stability and dynamics differently, creating variations in road capacity and driving patterns in mixed traffic. This comparative study analyzes five common policies utilized in the literature: Constant Spacing Policy (CSP), Constant Time Headway (CTH), Traffic Flow Stability (TFS), Constant Safety Factor (CSF), and the Intelligent Driver Model (IDM), and compares their applicability to modeling real-world AV behavior. Accordingly, each model is calibrated using the genetic algorithm for optimizing the parameters of each spacing policy. Three distinct datasets were utilized for calibration: (1) Third Generation Simulation (TGSIM) data from I-294L1 in Chicago that contains SAE Level 1 AVs, (2) TGSIM data from I-90/94 containing SAE Level 2 AVs, and (3) data collected from Level 4 AV operations in Phoenix, AZ. Calibration and simulation results show that CSP and CSF models had the most consistent performance, achieving the lowest RMSE and highest R2 values. The CSP model achieved a good level of performance under low-density highway conditions by maintaining consistent vehicle spacing with minimal perturbations. The CSF policy was determined to be the most optimal policy for high-density traffic, effectively managing safety-critical scenarios involving abrupt acceleration and braking. The CTH model showed reliable performance though it was sensitive to speed fluctuations, resulting in higher errors during dynamic traffic scenarios particularly in stop-and-go traffic. The TFS model consistently had higher errors, reflecting its limited adaptability to congested or complex traffic environments. The IDM model demonstrated strong adaptability and realistic driving behavior across diverse conditions but required precise calibration to achieve optimal performance in high-density and unpredictable scenarios."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129188"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Louis Cho"],"dc:subject":["Spacing control","Autonomous vehicles","Genetic algorithm"],"dc:title":["Evaluating and comparing longitudinal control strategies for autonomous vehicles"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:04Z"}