本論文研究基於學生方程式無人車競賽，製作縮小比例之電動載具、並在由角錐定義之賽道實現對應之定位與導航系統。電動載具仿造實際競賽車輛設計，由安裝於各輪之無刷直流馬達提供推力，並由兩個小型伺服馬達分別控制兩個前輪之轉向；此外也裝有光學相機、光流速度相機、加速規與陀螺儀等感測器。所開發之自駕系統係由感測器融合與導航控制兩部分構成。感測訊號利用擴展卡爾曼濾波與FastSALM-1.0演算法融合相機與各項感測器資訊，以估計出車輛定位與角錐地圖資訊。其中角錐地圖資訊可經德勞內三角化後作適當組合、還原為賽道之路徑範圍。導航控制則是基於定位與路徑資料，應用圖形處理器平行運算多個車輛動力學模型、以隨機最佳化求解模型預測控制來達成車輛姿態之穩定性與循跡控制。透過模擬與實驗驗證、整體系統在已知地圖下之橢圓形與綜合型等兩種賽道可達成預期之行駛性能；未知地圖下則可由直線賽道展現自駕系統之即時探索與導航性能。

Based on the Formula Student Driverless (FSD) competition, this thesis is devoted to the construction of a scale-down electric vehicle, and the self-driving system for the vehicle to autonomously run on the track defined by traffic cones. The design of the electric vehicle follows the requirements of the FSD competition. Its propulsion force is provided by four brushless DC motors installed on each of the wheels, and the steering is accomplished by two small servo motors that independently orient the two front wheels. In addition, the vehicle is also equipped with an optical camera, an optical-flow speed sensor, accelerometers, and gyroscopes. The self-driving system contains two parts: sensor fusion and navigation control. The sensor fusion part uses Extended Kalman Filtering and FastSALM-1.0 algorithm to fuse camera and various sensor information to estimate vehicle location and cone map information. Particularly, the cone map information can be further processed using Delaunay triangulation so as to recover the path area of the track. The navigation control part utilizes a graphic processor unit to simulate vehicle dynamics for a massive number of control input trajectories parallelly, and then applies stochastic optimization to solve model predictive control so that control inputs achieving both attitude stability and tracking performance can be obtained. Through simulations and experiments, it is proved that the proposed methods can accomplish the expected self-driving performance on both elliptical and mixed tracks under known-map condition. For the unknown-map case, the real-time exploration and self-driving are verified experimentally using a straight track.

摘要 i
Abstract ii
目錄 iv
圖目錄 vii
表目錄 x
名詞縮寫與常用代號對照表 xii
1 緒論 1
1.1 研究動機 1
1.2 文獻回顧 2
1.3 論文概覽 11
2 自駕系統規劃與模型建構 12
2.1 駕駛情境 13
2.2 四輪獨立驅動測試載具（QRacer） 15
2.3 載具動態模型 16
2.3.1 動力學模型（修正單軌模型） 19
2.3.2 無側滑角運動學模型 19
2.3.3 輪胎地面交互作用力模型 20
2.3.4 四輪獨立驅動輸出模型 21
3 狀態估測與地圖構建 22
3.1 高速動態估測器 23
3.2 同時定位與地圖構建系統 26
3.3 車體狀態資訊之轉換與傳遞 28
4 路面邊界探索 31
4.1 離散化探索空間-德勞內三角化 31
4.2 二元樹探索與價格函數設計 32
4.3 閉路檢驗與邊界資料輸出 34
4.4 路面邊界探索演算法 36
5 導航與動態控制 38
5.1 自行車動態模型 38
5.2 隨機最佳化模型預測控制 39
6 硬體與軟體設計 44
6.1 底盤總成與運動控制器 45
6.1.1 電子轉向與等效前輪轉角 47
6.1.2 牽引電機與驅動器 49
6.2 軟體環境 51
6.2.1 作業系統與x86中央處理器（CPU） 51
6.2.2 圖形處理器（GPU） 51
6.2.3 微控制器（STM32/ARM） 52
6.2.4 進程管理與通訊 53
6.3 感測器訊號處理與參數鑑別 55
6.3.1 輸出扭矩與輪速感測 55
6.3.2 慣性姿態感測器 56
6.3.3 光流速度感測器 59
6.3.4 光學相機 60
6.3.5 路面特徵物件辨識 62
7 實驗設計與結果 64
7.1 輪胎參數鑑別 64
7.2 影像資訊延遲補償 68
7.3 已知地圖測試結果 70
7.3.1 橢圓賽道 72
7.3.1 綜合賽道 75
7.4 未知地圖測試結果 78
8 未來工作 79
8.1 利用四輪驅動冗餘自由度 79
8.2 利用後輪轉向自由度 79
8.3 延展車輛感測範圍 80
9 附錄 81
9.1 系統可觀性檢驗 81
9.2 隨機最佳化控制演算法 84
9.3 圖型處理器運算模型 87
9.4 相機與成像術 89
9.5 車輛縮放尺度分析 91
9.6 整體功率與配電 92
10 參考文獻 93

[1] FormulaStudentTV. "FSG19 Trackdrive D." https://youtu.be/gcnngFyWnFQ?t=5236 (accessed 2020).
[2] T. Kanade, C. Thorpe, and W. Whittaker, "Autonomous land vehicle project at CMU," presented at the Proceedings of the 1986 ACM fourteenth annual conference on Computer science, Cincinnati, Ohio, USA, 1986. [Online]. Available: https://doi.org/10.1145/324634.325197.
[3] R. Verschueren, S. D. Bruyne, M. Zanon, J. V. Frasch, and M. Diehl, "Towards time-optimal race car driving using nonlinear MPC in real-time," in 53rd IEEE Conference on Decision and Control, 15-17 Dec. 2014 2014, pp. 2505-2510, doi: 10.1109/CDC.2014.7039771.
[4] Q. Zou, H. Jiang, Q. Dai, Y. Yue, L. Chen, and Q. Wang, "Robust Lane Detection From Continuous Driving Scenes Using Deep Neural Networks," IEEE Transactions on Vehicular Technology, vol. 69, no. 1, pp. 41-54, 2020, doi: 10.1109/TVT.2019.2949603.
[5] P. Pydipogu, M. Fahim, and M. Shafique, "Robust lane detection and object tracking In relation to the intelligence transport system," 2013.
[6] SAE International. "FORMULA SAE." https://www.fsaeonline.com/ (accessed 2020).
[7] FSG. "Fsg competition handbook 2020." https://www.formulastudent.de/fileadmin/user_upload/all/2020/rules/FSG20_Competition_Handbook_v1.0.pdf (accessed 2020).
[8] G. Tanzmeister, M. Friedl, A. Lawitzky, D. Wollherr, and M. Buss, "Road course estimation in unknown, structured environments," in 2013 IEEE Intelligent Vehicles Symposium (IV), 23-26 June 2013 2013, pp. 630-635, doi: 10.1109/IVS.2013.6629537.
[9] G. Tanzmeister, M. Friedl, D. Wollherr, and M. Buss, "Path planning on grid maps with unknown goal poses," in 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), 6-9 Oct. 2013 2013, pp. 430-435, doi: 10.1109/ITSC.2013.6728269.
[10] G. Tanzmeister, D. Wollherr, and M. Buss, "Environment-based trajectory clustering to extract principal directions for autonomous vehicles," in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 14-18 Sept. 2014 2014, pp. 667-673, doi: 10.1109/IROS.2014.6942630.
[11] G. Tanzmeister, D. Wollherr, and M. Buss, "Grid-Based Multi-Road-Course Estimation Using Motion Planning," IEEE Transactions on Vehicular Technology, vol. 65, no. 4, pp. 1924-1935, 2016, doi: 10.1109/TVT.2015.2420752.
[12] K. Brandes, A. Wang, and R. Shah, "Robust Lane Detection with Binary Integer Optimization," in 2020 IEEE International Conference on Robotics and Automation (ICRA), 31 May-31 Aug. 2020 2020, pp. 229-235, doi: 10.1109/ICRA40945.2020.9197098.
[13] S. Srinivasan, I. Sa, A. Zyner, V. Reijgwart, M. I. Valls, and R. Siegwart, "End-to-End Velocity Estimation for Autonomous Racing," IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 6869-6875, 2020, doi: 10.1109/LRA.2020.3016929.
[14] M. Bojarski et al., "End to end learning for self-driving cars. arXiv 2016," arXiv preprint arXiv:1604.07316, 2016.
[15] L. Curiel-Ramirez et al., "End-to-End Automated Guided Modular Vehicle," Applied Sciences, vol. 10, p. 4400, 06/26 2020, doi: 10.3390/app10124400.
[16] J. Vázquez, M. Brühlmeier, A. Liniger, A. Rupenyan, and J. Lygeros, Optimization-Based Hierarchical Motion Planning for Autonomous Racing. 2020.
[17] J. Kabzan et al., AMZ Driverless: The Full Autonomous Racing System. 2019.
[18] R. C. Coulter, "Implementation of the pure pursuit path tracking algorithm," Carnegie-Mellon UNIV Pittsburgh PA Robotics INST, 1992.
[19] S. Koehler, A. Viehl, O. Bringmann, and W. Rosenstiel, "Energy-Efficiency Optimization of Torque Vectoring Control for Battery Electric Vehicles," IEEE Intelligent Transportation Systems Magazine, vol. 9, no. 3, pp. 59-74, 2017, doi: 10.1109/MITS.2017.2709799.
[20] C. Knobel, A. Pruckner, and T. Bünte, "OPTIMIZED FORCE ALLOCATION: A General Approach to Control and to Investigate the Motion of Over-Actuated Vehicles," IFAC Proceedings Volumes, vol. 39, no. 16, pp. 366-371, 2006/01/01/ 2006, doi: https://doi.org/10.3182/20060912-3-DE-2911.00065.
[21] J. Y. Goh, T. Goel, and J. Christian Gerdes, "Toward Automated Vehicle Control Beyond the Stability Limits: Drifting Along a General Path," Journal of Dynamic Systems, Measurement, and Control, vol. 142, no. 2, 2019, doi: 10.1115/1.4045320.
[22] H. J. Kappen, "Linear Theory for Control of Nonlinear Stochastic Systems," Physical Review Letters, vol. 95, no. 20, p. 200201, 11/07/ 2005, doi: 10.1103/PhysRevLett.95.200201.
[23] H. J. Kappen, "Path integrals and symmetry breaking for optimal control theory," Journal of Statistical Mechanics: Theory and Experiment, vol. 2005, no. 11, pp. P11011-P11011, 2005/11/30 2005, doi: 10.1088/1742-5468/2005/11/p11011.
[24] E. Todorov, "Efficient computation of optimal actions," Proceedings of the National Academy of Sciences, vol. 106, no. 28, pp. 11478-11483, 2009, doi: 10.1073/pnas.0710743106.
[25] MIT. "mit-RACECAR." https://mit-racecar.github.io/ (accessed 2020).
[26] F. T. Foundation. "F1TENTH." https://f1tenth.org/ (accessed 2020).
[27] J. Gonzales. "BARC-project." http://www.barc-project.com/ (accessed 2020).
[28] G. Tech. "AutoRally." https://autorally.github.io/ (accessed 2020).
[29] W. G. Bennis and P. W. Biederman, Organizing Genius: The Secrets of Creative Collaboration. Basic Books, 2007.
[30] ETH. "AMZracing." https://driverless.amzracing.ch/ (accessed 2020).
[31] S. H. Żak and S. H. Żak, Systems and Control. Oxford University Press, 2003.
[32] H. Pacejka, I. J. M. Besselink, and I. Besselink, Tire and Vehicle Dynamics. Elsevier Science, 2012.
[33] R. E. Kalman, "A New Approach to Linear Filtering and Prediction Problems," Transactions of the ASME--Journal of Basic Engineering, vol. 82, Series D, pp. 35-45, 1960.
[34] H. Durrant-Whyte and T. Bailey, "Simultaneous localization and mapping: part I," IEEE Robotics & Automation Magazine, vol. 13, no. 2, pp. 99-110, 2006, doi: 10.1109/MRA.2006.1638022.
[35] M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit, "Fastslam: a factored solution to simultaneous mapping and localization," in Proceedings of the National Conference on Artificial Intelligence (AAAI), 2003.
[36] M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit, "FastSLAM 2.0: An Improved Particle Filtering Algorithm for Simultaneous Localization and Mapping that Provably Converges," Proc. IJCAI Int. Joint Conf. Artif. Intell., 06/25 2003.
[37] D. Lee and B. Schachter, "Two Algorithms for Constructing a Delaunay Triangulation," International Journal of Parallel Programming, vol. 9, pp. 219-242, 06/01 1980, doi: 10.1007/BF00977785.
[38] J. E. Bresenham, "Algorithm for computer control of a digital plotter," IBM Systems Journal, vol. 4, no. 1, pp. 25-30, 1965, doi: 10.1147/sj.41.0025.
[39] P. Szynkarczyk, J. Wrona, and A. Bartnicki, "Controller Area Network Standard for Unmanned Ground Vehicles Hydraulic Systems in Construction Applications," Journal of Automation, Mobile Robotics and Intelligent Systems, vol. 14, pp. 3-12, 07/06 2020, doi: 10.14313/JAMRIS/1-2020/1.
[40] N. Irie and J. Kuroki, "4WS Technology and the Prospects for Improvement of Vehicle Dynamics," in Vehicle Electronics in the 90's: Proceedings of the International Congress on Transportation Electronics, Oct. 1990 1990, pp. 429-437, doi: 10.1109/ICTE.1990.713040.
[41] M. J. P. Groenendijk, "Improving Vehicle Handling Behaviour with Active Toe-control," Master, Department Mechanical Engineering Dynamics and Control Group, Eindhoven University of Technology, 2009.
[42] D. L. Milliken, Race Car Vehicle Dynamics: Problems, Answers, and Experiments. SAE International, 2003.
[43] R. H. Park, "Two-reaction theory of synchronous machines generalized method of analysis-part I," Transactions of the American Institute of Electrical Engineers, vol. 48, no. 3, pp. 716-727, 1929, doi: 10.1109/T-AIEE.1929.5055275.
[44] P. C. Krause, O. Wasynczuk, S. D. Sudhoff, and S. D. Pekarek, Analysis of Electric Machinery and Drive Systems. Wiley, 2013.
[45] STmicroelectronics, "Arm®Cortex®-M4 32-bit MCU+FPU." STM32F446xC/E datasheet, Feb. 2015 [Revised Jul. 2020]
[46] S. Corrigan, "Introduction to the Controller Area Network (CAN)," 2016.
[47] D. Stancevic. "Zero Copy I: User-Mode Perspective." https://www.linuxjournal.com/article/6345?page=0,0 (accessed 2020).
[48] IEEE, "IEEE Standard Specification Format Guide and Test Procedure for Single-Axis Laser Gyros," IEEE Std 647-2006 (Revision of IEEE Std 647-1995), pp. 0_1-83, 2006, doi: 10.1109/IEEESTD.2006.246241.
[49] S.-H. Lee, Y. Son, C. M. Kang, and C. C. Chung, "Slip Angle Estimation: Development and Experimental Evaluation," IFAC Proceedings Volumes, vol. 46, no. 10, pp. 286-291, 2013/06/01/ 2013, doi: https://doi.org/10.3182/20130626-3-AU-2035.00071.
[50] KISTLER, "Correvit SFII sensors." CSF2A datasheet, unknown [Revised unknown]
[51] PixArt Imaging Inc, "PMW3901MB-TXQT: Optical Motion Tracking Chip." PMW3901 datasheet, Mar. 2017 [Revised Jun. 2017]
[52] C. Liang, L. Chang, and H. H. Chen, "Analysis and Compensation of Rolling Shutter Effect," IEEE Transactions on Image Processing, vol. 17, no. 8, pp. 1323-1330, 2008, doi: 10.1109/TIP.2008.925384.
[53] E. Hecht, Optics. Pearson Education, Incorporated, 2017.
[54] A. Bochkovskiy, C.-Y. Wang, and H.-y. Liao, YOLOv4: Optimal Speed and Accuracy of Object Detection. 2020.
[55] D. E. Goldberg, G. David Edward, D. E. G. Goldberg, and V. A. P. H. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Company, 1989.
[56] R. Hermann and A. Krener, "Nonlinear controllability and observability," IEEE Transactions on Automatic Control, vol. 22, no. 5, pp. 728-740, 1977, doi: 10.1109/TAC.1977.1101601.
[57] G. Williams, P. Drews, B. Goldfain, J. M. Rehg, and E. A. Theodorou, "Information-Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving," IEEE Transactions on Robotics, vol. 34, no. 6, pp. 1603-1622, 2018, doi: 10.1109/TRO.2018.2865891.
[58] E. Theodorou, J. Buchli, and S. Schaal, "A Generalized Path Integral Control Approach to Reinforcement Learning," Journal of Machine Learning Research, vol. 11, pp. 3137-3181, 11/01 2010.
[59] E. Theodorou, F. Stulp, J. Buchli, and S. Schaal, "An Iterative Path Integral Stochastic Optimal Control Approach for Learning Robotic Tasks," IFAC Proceedings Volumes, vol. 44, no. 1, pp. 11594-11601, 2011/01/01/ 2011, doi: https://doi.org/10.3182/20110828-6-IT-1002.02249.
[60] E. A. Theodorou, "Iterative path integral stochastic optimal control: Theory and applications to motor control," 2011.
[61] G. Williams, A. Aldrich, and E. A. Theodorou, "Model Predictive Path Integral Control: From Theory to Parallel Computation," Journal of Guidance, Control, and Dynamics, vol. 40, no. 2, pp. 344-357, 2017, doi: 10.2514/1.G001921.
[62] Intel, Intel® 64 and IA-32 Architectures Software Developer’s Manual. 2019.
[63] NVIDIA, Parallel Thread Execution ISA - Application Guide. 2020.
[64] NVIDIA, CUDA C++ Programming Guide - Design Guide. 2020.