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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/101571完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蔡欣穆 | zh_TW |
| dc.contributor.advisor | Hsin-Mu Tsai | en |
| dc.contributor.author | 林紹維 | zh_TW |
| dc.contributor.author | Shao-Wei Lin | en |
| dc.date.accessioned | 2026-02-11T16:26:57Z | - |
| dc.date.available | 2026-02-12 | - |
| dc.date.copyright | 2026-02-11 | - |
| dc.date.issued | 2026 | - |
| dc.date.submitted | 2026-02-03 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/101571 | - |
| dc.description.abstract | 高速公路的交通流經常因「幽靈塞車 (Phantom Jams)」而陷入不穩定，此現象主要源於人類駕駛的反應延遲。儘管聯網自駕車 (CAVs) 被廣泛視為解決此問題的終極方案，但受限於混合自動駕駛環境的複雜性與法律規範，其全面部署仍難以在短期內實現。為解決此過渡期缺口，本研究提出一套速度建議系統 (SAS)，透過 V2X 通訊技術賦予人類駕駛車輛 (HDVs) 前瞻性的預判能力。
鑑於現有標準模型無法重現導致交通震盪的特定人類駕駛失誤，本研究首先在 SUMO 模擬環境中開發了一套「以人為本跟車模型 (HCCFM)」。該架構明確納入了人類駕駛固有的生理與心理限制——具體整合了韋伯定律 (Weber's Law) 與非對稱風險感知——以精確重現真實的駕駛動態。 實驗結果證實，SAS 發揮了強大的「消波 (Wave-Breaker)」作用，將震盪波傳播距離減少了 94.9%，並將總延遲時間降低了 90.4%。在巨觀層面上，該系統在瓶頸場景中使道路容量提升了 21.2%。敏感度分析顯示，效率顯著提升的關鍵規模 (Critical Mass) 位於 50% 的市場滲透率。關鍵的是，穩健性測試證實即便在 50% 封包遺失率下，系統運作效率仍優於純人類駕駛；且其失效安全機制確保了即使在 100% 通訊中斷的情況下，仍能維持零碰撞紀錄。 | zh_TW |
| dc.description.abstract | Highway traffic is frequently destabilized by "phantom jams'' driven by human reaction latency. While Connected and Autonomous Vehicles (CAVs) are widely proposed as the ultimate solution, their immediate deployment is constrained by the complexities of mixed autonomy and legal regulations. To address this gap, we propose a Speed Advisory System (SAS) that empowers Human-Driven Vehicles (HDVs) with V2X-enabled foresight. Since standard models fail to replicate the specific human errors that propagate these instabilities, we first develop a Human-Centric Car-Following Model (HCCFM) within SUMO. This framework explicitly accounts for the innate physiological and psychological constraints of human drivers---specifically integrating Weber's Law and asymmetric risk perception---to accurately reproduce realistic driving dynamics. Experimental results demonstrate that the SAS acts as a strong "wave-breaker,'' reducing shockwave propagation distance by 94.9% and total delay by 90.4%. Macroscopically, the system increases the capacity by 21.2% in the bottleneck scenarios. Sensitivity analysis reveals a critical mass at 50% penetration for efficiency gains. In particular, robustness tests confirm that the system remains more efficient than human driving even under 50% packet loss, and the fail-safe mechanism ensures zero collisions even under 100% communication failure. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2026-02-11T16:26:57Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2026-02-11T16:26:57Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書：i
誌謝：iii 摘要：iv Abstract：v Contents：vii List of Figures：xi List of Tables：xiv Chapter 1: Introduction：1 1.1 Background and Motivation：1 1.2 Problem Statement：2 1.3 Proposed Solution：3 1.4 Thesis Contributions：3 1.5 Thesis Organization：5 Chapter 2: Related Work：6 2.1 Traffic Shockwave Mitigation in Mixed Autonomy：6 2.2 Car-Following Models：8 2.3 Driver Reaction Dynamics and Cognitive Load：9 2.4 Research Gaps and Thesis Positioning：10 Chapter 3: System Design：12 3.1 Overview：12 3.2 System Architecture：12 3.3 SUMO Simulation Environment：15 3.3.1 Temporal Resolution and Action Granularity：15 3.3.2 Car-Following Parameters：16 3.4 Krauss Model：16 3.5 Human-Centric Car Following Model：19 3.5.1 Delayed Perception：19 3.5.2 Imperfect Estimation：20 3.5.3 Asymmetric Risk Assessment：21 3.5.4 Final Action Determination：23 3.6 Speed Advisory System (SAS) Design：24 3.6.1 Digital State Acquisition and Latency Mitigation：24 3.6.2 Cognitive Load Reduction: From Choice Reaction Time to Simple Reaction Time：25 3.6.3 Precision-Enabled Stability and Error Elimination：26 3.6.4 Fail-Safe State Machine and Handover Protocol：28 Chapter 4: Implementation：31 4.1 Simulation Environment：31 4.2 Vehicle Model Implementation and Calibration：32 4.2.1 Calibration Methodology：32 4.2.2 Parameter Configuration：35 4.2.3 Baseline Models Configuration：36 4.2.4 Evaluation Metrics：37 4.3 Experimental Scenarios：37 4.3.1 Pulse Step Test Scenario：37 4.3.2 Virtual Bottleneck Scenario：38 4.3.3 Penetration Rate Sensitivity：40 4.3.4 Network Reliability and Fail-Safe Mechanism：42 4.3.5 Two-Lane Capacity Scenario with Lane Changing Dynamics：45 Chapter 5: Evaluation：47 5.1 Overview：47 5.2 Model Calibration and Validation：48 5.2.1 Trajectory Reproduction Capability：49 5.2.2 Probabilistic Error Analysis (KDE & CDF)：50 5.3 Microscopic String Stability of SAS：52 5.3.1 Qualitative Analysis: Space-Time Heatmaps：52 5.3.2 Quantitative Impact: Propagation Distance and Delay：54 5.3.3 Mechanism Verification: Perturbation Amplification Ratio：55 5.4 Macroscopic Capacity Analysis：56 5.4.1 Fundamental Diagram Analysis：57 5.4.2 Capacity Improvement Quantification：58 5.4.3 Mechanism of Improvement and Breakdown：59 5.4.4 Sensitivity Analysis: Impact of Reaction Time on Capacity：59 5.5 Sensitivity Analysis: Penetration Rate：61 5.5.1 Quantitative Improvements：62 5.5.2 Interpretative Summary: The Phases of Deployment：64 5.5.3 Damping Effect Analysis：65 5.5.4 Visual Verification: Shockwave Propagation：66 5.6 Impact of Network Reliability：68 5.6.1 The Peril of Blind Estimation：69 5.6.2 Robustness via Fail-Safe Mechanism：69 5.7 Two-Lane Capacity with Lane Changing Analysis：69 5.8 Discussion：71 5.8.1 Linking Microscopic Stability to Macroscopic Capacity：71 5.8.2 The Trade-off between Handover Safety and Maximum Efficiency：73 5.8.3 Phased Evolution of System Benefits：73 Chapter 6: Conclusion：75 6.1 Summary of Work：75 6.2 Key Findings：76 6.3 Limitations：77 6.4 Future Work：78 References：80 | - |
| dc.language.iso | en | - |
| dc.subject | 混合自動駕駛 | - |
| dc.subject | V2X | - |
| dc.subject | 交通震盪緩解 | - |
| dc.subject | 跟車模型 | - |
| dc.subject | SUMO模擬 | - |
| dc.subject | 速度建議系統(SAS) | - |
| dc.subject | Mixed Autonomy | - |
| dc.subject | V2X Communication | - |
| dc.subject | Speed Advisory System(SAS) | - |
| dc.subject | Car-Following Model | - |
| dc.subject | Traffic Shockwaves | - |
| dc.subject | Human-Centric Design | - |
| dc.title | 混合自動駕駛環境中 V2X 速度建議系統之成效評估：人本導向之研究 | zh_TW |
| dc.title | Impact Assessment of V2X Speed Advisory System in Mixed Autonomy Environment : Human-Centric Approach | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 114-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 林忠緯;陳柏華;林靖茹 | zh_TW |
| dc.contributor.oralexamcommittee | Chung-Wei Lin;Po-Hua Chen;Ching-Ju Lin | en |
| dc.subject.keyword | 混合自動駕駛,V2X交通震盪緩解跟車模型SUMO模擬速度建議系統(SAS) | zh_TW |
| dc.subject.keyword | Mixed Autonomy,V2X CommunicationSpeed Advisory System(SAS)Car-Following ModelTraffic ShockwavesHuman-Centric Design | en |
| dc.relation.page | 86 | - |
| dc.identifier.doi | 10.6342/NTU202600337 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2026-02-05 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | - |
| dc.date.embargo-lift | 2026-02-12 | - |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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