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請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90133
完整後設資料紀錄
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dc.contributor.advisor林忠緯zh_TW
dc.contributor.advisorChung-Wei Linen
dc.contributor.author黃紹輔zh_TW
dc.contributor.authorShao-Fu Huangen
dc.date.accessioned2023-09-22T17:33:08Z-
dc.date.available2023-11-09-
dc.date.copyright2023-09-22-
dc.date.issued2023-
dc.date.submitted2023-08-09-
dc.identifier.citationA. Uno, T. Sakaguchi, and S. Tsugawa. A merging control algorithm based on inter-vehicle communication. In Proceedings 199 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems (Cat. No.99TH8383), pages 783–787, 1999.
Gurulingesh Raravi, Vipul Shingde, Krithi Ramamritham, and Jatin Bharadia. Merge algorithms for intelligent vehicles. In S. Ramesh and Prahladavaradan Sampath, editors, Next Generation Design and Verification Methodologies for Distributed Embedded Control Systems, pages 51–65, Dordrecht, 2007. Springer Netherlands.
Tanveer Awal, Lars Kulik, and Kotagiri Ramamohanrao. Optimal traffic merging strategy for communication- and sensor-enabled vehicles. In 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), pages 1468–1474, 2013.
Huaxin Pei, Shuo Feng, Yi Zhang, and Danya Yao. A cooperative driving strategy for merging at on-ramps based on dynamic programming. IEEE Transactions on Vehicular Technology, 68(12):11646–11656, 2019.
Shang-Chien Lin, Hsiang Hsu, Yi-Ting Lin, Chung-Wei Lin, Iris Hui-Ru Jiang, and Changliu Liu. A dynamic programming approach to optimal lane merging of connected and autonomous vehicles. In 2020 IEEE Intelligent Vehicles Symposium (IV), pages 349–356, 2020.
Shang-Chien Lin, Chia-Chu Kung, Lee Lin, Chung-Wei Lin, and Iris Hui-Ru Jiang. Efficient mandatory lane changing of connected and autonomous vehicles. In 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), pages 1–7, 2021.
Chaoyi Chen, Qing Xu, Mengchi Cai, Jiawei Wang, Biao Xu, Xiangbin Wu, Jianqiang Wang, Keqiang Li, and Chunyu Qi. A graph-based conflict-free cooperation method for intelligent electric vehicles at unsignalized intersections. In 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), pages 52–57, 2021.
Huile Xu, Yi Zhang, Li Li, and Weixia Li. Cooperative driving at unsignalized intersections using tree search. IEEE Transactions on Intelligent Transportation Systems, 21(11):4563–4571, 2020.
Li Li, Fei-Yue Wang, and Yi Zhang. Cooperative driving at lane closures. In 2007 IEEE Intelligent Vehicles Symposium, pages 1156–1161, 2007.
Maksat Atagoziyev, Klaus W. Schmidt, and Ece G. Schmidt. Lane change scheduling for autonomous vehicles**this work was supported by the scientific and technological research council of turkey (tubitak) [award 115e372]. IFACPapersOnLine, 49(3):61–66, 2016.
Tanveer Awal, Manzur Murshed, and Mortuza Ali. An efficient cooperative lane-changing algorithm for sensor- and communication-enabled automated vehicles. In 2015 IEEE Intelligent Vehicles Symposium (IV), pages 1328–1333, 2015.
Vicente Milanes, Jorge Godoy, Jorge Villagra, and Joshu´e Perez. Automated on-ramp merging system for congested traffic situations. IEEE Transactions on Intelligent Transportation Systems, 12(2):500–508, 2011.
Ioannis A. Ntousakis, Ioannis K. Nikolos, and Markos Papageorgiou. Optimal vehicle trajectory planning in the context of cooperative merging on highways. Transportation Research Part C: Emerging Technologies, 71:464–488, 2016.
A. V. S. Sai Bhargav Kumar, Adarsh Modh, Mithun Babu, Bharath Gopalakrishnan, and K. Madhava Krishna. A novel lane merging framework with probabilistic risk based lane selection using time scaled collision cone. In 2018 IEEE Intelligent Vehicles Symposium (IV), pages 1406–1411, 2018.
Na’Shea Wiesner, John Sheppard, and Brian Haberman. Using particle swarm optimization to learn a lane change model for autonomous vehicle merging. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1–8, 2021.
Gil Domingues, Jo˜ao Cabral, Jo˜ao Mota, Pedro Pontes, Zafeiris Kokkinogenis, and Rosaldo J. F. Rossetti. Traffic simulation of lane-merging of autonomous vehicles in the context of platooning. In 2018 IEEE International Smart Cities Conference (ISC2), pages 1–6, 2018.
Shurong Li, Chong Wei, and Ying Wang. A ramp merging strategy for automated vehicles considering vehicle longitudinal and latitudinal dynamics. In 2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE), pages 441–445, 2020.
Zhaohui Wang, Shengmin Cui, and Tianyi Yu. Automatic lane change control for intelligent connected vehicles. In 2019 4th International Conference on Electromechanical Control Technology and Transportation (ICECTT), pages 286–289, 2019.
Mei Yang, Chunxiao Li, Wen Wu, and Chunyan Qi. A cooperative lane change strategy for improving road safety through V2V communications. In 2022 13th International Conference on Information and Communication Technology Convergence (ICTC), pages 1415–1418, 2022.
Yong-Geon Choi, Kyung-Il Lim, and Jung-Ha Kim. Lane change and path planning of autonomous vehicles using GIS. In 2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pages 163–166,2015.
Bangjun Qiao and Xiaodong Wu. Lane change control of autonomous vehicle with real-time rerouting function. In 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pages 1317–1322, 2019.
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90133-
dc.description.abstract車道合併是導致交通擁堵的主要原因之一,因為車道數目減少, 而其他車輛的行為通常是不可預測的。通過利用車輛對車輛和車輛對 基礎設施的通信,以及自動駕駛車輛的特點,車輛可以以較低的時間 成本達成共識,從而緩解交通擁堵。本文旨在對在兩對一車道合併情 景下車輛的通過順序進行調度,其中每對車輛之間的等待時間不同。 首先,我們對問題進行了形式化,提出了一種無車道變換的基於動態 規劃的算法,用全局視角對所有車輛進行調度。此外,我們引入了一 種考慮車道變換的基於動態規劃的算法,進一步縮短了最後一輛車的 預定進入時間。受到車道合併問題的啟發,我們提出了在M-N車道擴 展情景下的負載平衡調度問題。負載平衡的重要性在於,如果車道上 的負載不平衡,維護不僅會變得昂貴,而且會耗費時間。因此,我們 對問題進行了形式化,並首先提出了一種處理每輛車輛出車道決策的 混合整數線性規劃(MILP)方法。然後,我們提出了一種啟發式方法,提高了所有車輛的通過效率。車道合併的實驗結果表明,相比於 無車道變換的先來先服務(FCFS)、有車道變換的FCFS以及無車道變 換的基於動態規劃的算法,考慮車道變換的基於動態規劃的算法找到 了更優的解決方案。負載平衡問題的實驗結果表明,雖然我們的啟發 式方法可能無法找到最優解,但它仍然比FCFS方法提供了更優的解決 方案。zh_TW
dc.description.abstractLane merging is a major reason causing traffic congestion because the number of lanes decreases and the behavior of other vehicles is usually unpredictable. By taking advantage of vehicle-to-vehicle and vehicle-to-infrastructure communication, as well as the characteristics of autonomous vehicles, vehicles can reach a consensus with low time cost, and traffic congestion can be alleviated. In this thesis, we aim to schedule the passing order of vehicles in a two-to-one lane-merging scenario where the waiting times between each pair of vehicles are different. We first formulate the problem and come up with a dynamic programming (DP)-based algorithm that schedules all vehicles with a global perspective. Moreover, we introduce a DP-based algorithm that takes lane changing into consideration, further reducing the scheduled entering time of the last vehicle. Inspired by the lane-merging problem, we come up with a load-balancing scheduling problem under M-N lane-expanding scenarios. The significance of load balancing is that if the load on lanes is unbalanced, maintenance will not only be expensive but also time-consuming. Therefore, we formulate the problem and first propose a Mixed-Integer Linear Programming (MILP) approach that handles the decision of outgoing lanes for each vehicle. Then, we present a heuristic approach that reduces the scheduled entering time of the last vehicle. Experimental results for lane merging show that the DP-Based Algorithm with Lane Changing finds a better solution compared to First Come First Serve (FCFS) without Lane Changing, FCFS with Lane Changing, and the DP-Based Algorithm without Lane Changing. Experimental results for the load-balancing problem demonstrate that our heuristic approach, although it may not find the optimal solution, still provides a better solution than FCFSen
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dc.description.tableofcontentsAcknowledgements iii
Abstract (Chinese) iv
Abstract vi
List of Tables x
List of Figures xi
Chapter 1. Introduction 1
Chapter 2. Problem Formulation for Lane Merging 6
2.1 Definitions and Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Chapter 3. Proposed Approaches for Lane Merging 10
3.1 DP-Based Algorithm without Lane Changing . . . . . . . . . . . . . . . 10
3.1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.1.2 Derivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.1.3 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.2 DP-Based Algorithm with Lane Changing . . . . . . . . . . . . . . . . . 13
3.2.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.2.2 Derivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.2.3 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Chapter 4. Problem Formulation for Load Balancing 21
4.1 Definitions and Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Chapter 5. Proposed Approaches for Load Balancing 27
5.1 Our Heuristic Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
5.1.1 Phase 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
5.1.2 Phase 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Chapter 6. Experimental Results 30
6.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
6.2 Experiments for Lane Merging . . . . . . . . . . . . . . . . . . . . . . . . 30
6.2.1 Comparative Approaches . . . . . . . . . . . . . . . . . . . . . . . 31
6.2.2 Experimental Results with Different Traffic Densities . . . . . . . . 32
6.2.3 Experimental Results with Different Numbers of Vehicles . . . . . 33
6.3 Experiments for Load Balancing . . . . . . . . . . . . . . . . . . . . . . . 33
6.3.1 Comparative Approaches . . . . . . . . . . . . . . . . . . . . . . . 34
6.3.2 Experimental Results with Different Traffic Densities . . . . . . . . 35
6.3.3 Experimental Results with Different Detection Range . . . . . . . 36
6.3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Chapter 7. Conclusions 38
Bibliography 40
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dc.language.isoen-
dc.subject線道擴增zh_TW
dc.subject線道合併zh_TW
dc.subject動態規劃zh_TW
dc.subject排程zh_TW
dc.subject聯網自駕車zh_TW
dc.subject混合整數線性規劃zh_TW
dc.subject負載平衡zh_TW
dc.subjectDynamic Programmingen
dc.subjectConnected and Autonomous Vehicleen
dc.subjectLane Mergingen
dc.subjectLane Changingen
dc.subjectLane Expandingen
dc.subjectLoad Balancingen
dc.subjectSchedulingen
dc.subjectMixed-Integer Linear Programmingen
dc.title聯網自駕車線道合併與擴增之通過順序決策zh_TW
dc.titleLane-Merging and Lane-Expanding Passing-Order Decision for Connected and Autonomous Vehiclesen
dc.typeThesis-
dc.date.schoolyear111-2-
dc.description.degree碩士-
dc.contributor.oralexamcommittee江蕙如;黎士瑋;周詩梵zh_TW
dc.contributor.oralexamcommitteeHui-Ru Jiang;Shih-Wei Li;Shih-Fan Chouen
dc.subject.keyword聯網自駕車,線道合併,線道擴增,排程,混合整數線性規劃,負載平衡,動態規劃,zh_TW
dc.subject.keywordConnected and Autonomous Vehicle,Lane Merging,Lane Changing,Lane Expanding,Load Balancing,Scheduling,Mixed-Integer Linear Programming,Dynamic Programming,en
dc.relation.page43-
dc.identifier.doi10.6342/NTU202303752-
dc.rights.note同意授權(全球公開)-
dc.date.accepted2023-08-11-
dc.contributor.author-college電機資訊學院-
dc.contributor.author-dept資訊工程學系-
顯示於系所單位:資訊工程學系

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