國立臺灣師範大學博碩士論文全文系統

簡易檢索 / 詳目顯示

回結果列表

研究生：	高御圻 Kao, Yu-Chi
論文名稱：	遠端操控自動駕駛車輛系統中支援向量機之研究 Research on Support Vector Machines in Telecontrol Autopilot Vehicle System
指導教授：	黃政吉 Huang, Jeng-Ji
口試委員：	馮輝文 Ferng, Huei-Wen 熊大為 Shiung, David 黃政吉 Huang, Jeng-Ji
口試日期：	2023/06/13
學位類別：	碩士 Master
系所名稱：	電機工程學系 Department of Electrical Engineering
論文出版年：	2023
畢業學年度：	111
語文別：	中文
論文頁數：	48
中文關鍵詞：	自動駕駛系統、支持向量機、機器學習、序列最小優化
英文關鍵詞：	automated driving system (ADS), support vector machine (SVM), machine-learning, sequential minimal optimization (SMO)
DOI URL：	http://doi.org/10.6345/NTNU202301077
論文種類：	學術論文
相關次數：	點閱：133 下載：13
分享至:	分享至facebook 分享至twitter

查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

遠端駕駛是一種嚴重依賴遠程操作員與車輛自動駕駛系統之間可靠通信鏈來運作的系統。雖然變道和加速/減速等細節的操作（低級動作）由 ADS 執行，但路線規劃（高級指令）是通過使用從沿路的攝像頭或傳感器收集的信息來遠程完成的。在論文中，討論了支持向量機的理論，它在中有著核心的作用。作為一種基於機器學習的技術，必須經過訓練才能用於執行分類任務。因此，我們將說明如何在中找到優化的支持超平面的推導，包括如何使用序列最小優化。

A tele-operated vehicle is a system relying heavily on a reliable communication link between a remote operator and an automated driving system (ADS) at a vehicle. While detailed maneuvers (low level actions) such as lane changes and accelerations/decelerations are performed by the ADS, route planning ( high level instructions) is remotely done by using gathered information from cameras or sensors along the road. In the thesis, the theory of the support vector machine (SVM) is discussed, which plays a central role in an ADS. As a machine-learning based technique, a SVM has to be trained before it can be used to do classification tasks. Therefore, the derivations of how an optimized supporting hyperplane in SVM can be found are illustrated, including the use of the sequential minimal optimization (SMO).

誌   謝................................................................................................................i
中文摘要.............................................................................................................ii
英文摘要 ..........................................................................................................iii
目   錄...............................................................................................................v
圖 目 錄...........................................................................................................vii
第一章緒論.......................................................................................................1
1.1 自動駕駛技術發展(Intelligent Transportation System, ITS)...............1
1.2 遠端駕駛用途定位(Vehicular ad-hoc network, VANET)....................3
1.3 機器學習(Vehicle Platooning)......................................................4
1.4 論文動機.............................................................................................5
1.5 章節編排.............................................................................................7
第二章 重要相關文獻與相關背景知識.........................................................8
2.1 [6].........................................................................................................8
2.2 [4].........................................................................................................10
2.3 [5].........................................................................................................12
2.4 [8].........................................................................................................15
2.5 [7].........................................................................................................18
第三章 推導演算法.......................................................................................................20
3.1 SVM演算法的特色........................................................... .......... ...................20
3.2 求解最大Margin........................................................... .......... .......................26
3.3 對偶性...................................................................... .......... .............................31
3.4 核函數.................................................................... .......... ...............................33
3.5 序列最小最佳化(SMO)...................................... .......... .................................37
第四章 實作結果分析與討論............................................ .......... ...............................38
4.1 問題設定...................................................................... ...................................39
4.2 線性可分實作.............................................................. ...................................39
4.3 非線性可分實作........................................................................... ..................43
第五章 結論............................................................................. ..... ...............................45
相 關 文 獻................................................................. .......... .........................................46
自傳................................................................................... .......... .................................47
學 術 研 究........................................................................................ .......... ..................48
                                

[1] A. Aslam, F. Santos, and L. Almeida, “Reconfiguring TDMA Communications for Dynamic Formation of Vehicle Platoons,” IEEE International Conference on Emerging Technologies and Factory Automation (ETFA),pp.1713–1714,Oct.2020.
[2] C. Wissing, T. Nattermann, K.-H. Glander, C. Hass, T. Bertram, Lane change prediction by combining movement and situation based probabilities, IFACPapersOnLine 50 (1) (2017) 3554–3559, doi:10.1016/j.ifacol.2017.08.960. 20th IFAC World Congress
[3] F. Chucholowski, S. Büchner, J. Reicheneder, and M. Lienkamp, “Prediction methods for teleoperated road vehicles,” in Conference on Future Automotive Technology - Focus Electromobility, Garching. Bayern Innovativ GmbH, 01 2013.
[4] C. Lienke, C. Wissing, M. Keller, T. Nattermann and T. Bertram, "Predictive Driving: Fusing Prediction and Planning for Automated Highway Driving," in IEEE Transactions on Intelligent Vehicles, vol. 4, no. 3, pp. 456-467, Sept. 2019, doi: 10.1109/TIV.2019.2919477.
[5] L. Jian and C. Gao, "Binary Coding SVMs for the Multiclass Problem of Blast Furnace System," in IEEE Transactions on Industrial Electronics, vol. 60, no. 9, pp. 3846-3856, Sept. 2013, doi: 10.1109/TIE.2012.2206336.
[6] Hsu, Chih-Wei, Chih-Chung Chang, and Chih-Jen Lin. "A practical guide to support vector classification." (2003): 1396-1400.
[7] R. R. Prasojoe and S. Setyorini, "SVM Parallel Concept Test with SMO Decomposition on Cancer Microarray Dataset," 2021 9th International Conference on Information and Communication Technology (ICoICT), Yogyakarta, Indonesia, 2021, pp. 467-471, doi: 10.1109/ICoICT52021.2021.9527411.
[8] J.C.Platt, Sequential minimal optimization: a fast algorithm for training support vector machines. 1998, S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, K.R.K. Murthy, Improvements to platt’s SMO algorithm for svm classifier design, Neural Computing 13(2001)637–649.
[9] M. Codreanu, M. Juntti and M. Latva-aho, "On the Dual Decomposition Based Sum Capacity Maximization for Vector Broadcast Channels," 2006 Fortieth Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, 2006, pp. 468-472, doi: 10.1109/ACSSC.2006.354791.
[10] Cai Zhili and Jiang Guiyan, "Application of multiple SVM classifier fusion technique in freeway automatic incident detection," 2008 27th Chinese Control Conference, Kunming, China, 2008, pp. 581-585, doi: 10.1109/CHICC.2008.460568

簡易檢索 / 詳目顯示

相關論文