跳到主要內容

臺灣博碩士論文加值系統

(44.192.247.184) 您好!臺灣時間:2023/02/07 12:54
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:鄭富澤
研究生(外文):Fu-TseCheng
論文名稱:自駕車於雙向行駛車道之超車研究
論文名稱(外文):Overtake Maneuvers for Autonomous Driving Vehicles in Two-way Traffic Environment
指導教授:莊智清莊智清引用關係
指導教授(外文):Jyh-Ching Juang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:英文
論文頁數:60
中文關鍵詞:自動駕駛車輛感測器融合切換車道超車
外文關鍵詞:Autonomous VehicleSensor FusionLane Change ManeuverOvertake Maneuver
相關次數:
  • 被引用被引用:0
  • 點閱點閱:278
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
在未來,自動駕駛車輛會出現在人們的日常生活中,這些車輛被設計滿足人們通勤中的舒適性、安全性以及交通的通暢,為了要因應各種路況,自動駕駛車輛配備著許多像是光達、雷達、聲納、視覺等等的感測器,每種感測器都有各自不同的優缺點,因此將各種感測器組合在一起也就是所稱的感測器融合可以帶來許多好處,本論文在感測器部分融合了雷達與影像感測器,用以感知其他車輛的位置與速度。
對人們而言,在駕駛時做出切換車道或是超車等動作是相當常見的狀況,然而在從事這些行為時增加了出意外的風險,為了讓自駕車能夠解決這些問題,關於讓自動駕駛車輛切換車道的研究相當重要,這些研究主題包含了定位、感知、決策和控制等技術。
許多關於高速公路上讓自駕車執行切換車道的研究並未考慮相反車流的交通環境,本論文的目標為解決在相反車流交通環境下讓自駕車執行超車行為的問題,由於有了現有關於讓自駕車在高速公路執行切換車道的研究的幫助下,本篇論文提出了讓自駕車執行超車的新方法,並且透過模擬測試確保其可靠性。
The autonomous driving vehicle is the technology that will appear in daily life in the future. These vehicles are designed to assist the human driver in the task of lateral and longitudinal control with guaranteed comfort, safety, and throughput. In order to perform well in all kinds of the road environment, the autonomous vehicle is equipped with sensors such as LIDAR, radar, sonar, vision, and so on, Each sensor has different strength and weakness. As a result, combining them which is called sensor fusion will give an advantage. In this thesis, we fuse radar and vision sensors to get the position and velocity of other non-ego vehicles.
For human drivers, performing a lane change or overtake maneuver is common when driving. However, when performing these steps it increases the risk of accidents. We need autonomous vehicles to solve these problems so that the research on performing lane change maneuvers for autonomous vehicles is crucial. They consisted of localization, perception, decision-making, and control.
Most of the research on highway lane change doesn’t consider the two-way traffic environment. This thesis aims to solving the overtaking maneuver problems for autonomous vehicles in the two-way traffic environment. This thesis proposes a new method of performing overtake maneuvers for autonomous vehicles. It is tested by some simulations to show it's feasibility.
摘要 I
Abstract III
Acknowledgements V
Contents VI
List of Tables IX
List of Figures X
List of Abbreviations XII
Chapter 1 Introduction 1
1.1 Motivation and Objectives 1
1.2 Literature Review 1
1.3 Contributions 2
1.4 Thesis Overview 2
Chapter 2 Lane Change Maneuver 4
2.1 Coordinate Systems 4
2.2 Vehicle Model 6
2.3 Sensor Model 7
2.3.1 Vision sensor 8
2.3.2 Radar Sensor 10
2.4 Road Geometry 13
Chapter 3 Lane Change Algorithm 14
3.1 Review of Previous Research 14
3.1.1 Case A 14
3.1.2 Case B 15
3.1.3 Case C 16
3.1.4 Case D 18
3.2 Algorithms Overview 18
3.3 The Initialization of Lane Change Maneuver 20
3.4 Selecting Traffic Gap 21
3.5 Trajectory Planning 22
Chapter 4 Simulation and Results 27
4.1 Scenario Setup 27
4.2 Parameters 28
4.3 Driving Scenario Designer 29
4.4 Simulation and Analysis 33
4.4.1 Case 1 33
4.4.2 Case 2 40
4.4.3 Case 3 46
4.4.4 Case 4 51
Chapter 5 Conclusions and Future Works 56
5.1 Conclusions 56
5.2 Future Work 57
References 58
References
[1]G. P.Reddy, “Hierarchical Model Predictive Control for Trajectory Generation and Tracking in Highly Automated Vehicles, Delft University of Technology, 2016.
[2]J.Kong, M.Pfeiffer, G.Schildbach, andF.Borrelli, “Kinematic and dynamic vehicle models for autonomous driving control design, IEEE Intelligent Vehicles Symposium, Proceedings, vol. August, pp. 1094–1099, 2015.
[3]L.Zhao, W. Y.Ochieng, M. A.Quddus, and R. B.Noland, “An extended Kalman filter algorithm for integrating GPS and low cost dead reckoning system data for vehicle performance and emissions monitoring, Journal of Navigation, vol. 56, no. 2, pp. 257–275, 2003.
[4]H.Durrant-Whyte and T.Bailey, “Simultaneous localization and mapping: Part I, IEEE Robotics and Automation Magazine, vol. 13, no. 2, pp. 99–108, 2006.
[5]“Lane Following Control with Sensor Fusion and Lane Detection - MATLAB & Simulink. https://www.mathworks.com/help/driving/examples/lane-following-control-with-sensor-fusion-and-lane-detection.html (accessed Jun. 27, 2020).
[6]Y. W.Seo, J.Lee, W.Zhang, and D.Wettergreen, “Recognition of Highway Workzones for Reliable Autonomous Driving, IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 2, pp. 708–718, 2015.
[7]M.Lehtomäki et al., “Object Classification and Recognition From Mobile Laser Scanning Point Clouds in a Road Environment, IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 2, pp. 1226–1239, 2016.
[8]S.Gu, T.Lu, Y.Zhang, J. M.Alvarez, J.Yang, and H.Kong, “3-D LiDAR + Monocular Camera: An inverse-depth-induced fusion framework for urban road detection, IEEE Transactions on Intelligent Vehicles, vol. 3, no. 3, pp. 351–360, 2018.
[9]E.Galceran, A. G.Cunningham, R. M.Eustice, and E.Olson, “Multipolicy decision-making for autonomous driving via changepoint-based behavior prediction: Theory and experiment, Autonomous Robots, vol. 41, no. 6, pp. 1367–1382, 2017.
[10]J.Wei, J. M.Snider, T.Gu, J. M.Dolan, and B.Litkouhi, “A behavioral planning framework for autonomous driving, IEEE Intelligent Vehicles Symposium, Proceedings, pp. 458–464, 2014.
[11]M. K.Park, S. Y.Lee, C. K.Kwon, and S. W.Kim, “Design of pedestrian target selection with funnel map for pedestrian AEB system, IEEE Transactions on Vehicular Technology, vol. 66, no. 5, pp. 3597–3609, 2017.
[12]J.Nilsson, M.Brannstrom, E.Coelingh, and J.Fredriksson, “Lane Change Maneuvers for Automated Vehicles, IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 5, pp. 1087–1096, 2017.
[13]J.Nilsson and J.Fredriksson, “How to Perform Lane Change Maneuvers on Highways, IEEE Trans. Intell. Transp. Syst., vol. 8, no. 4, pp. 68–78.
[14]C. J.Hoel, K.Wolff, and L.Laine, “Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning, IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, vol. 2018-Novem, pp. 2148–2155, 2018.
[15]J.Nilsson, P.Falcone, M.Ali, and J.Sjöberg, “Control Engineering Practice Receding horizon maneuver generation for automated highway driving, Control Engineering Practice, vol. 41, pp. 124–133, 2015.
[16]R. N.Jazar, Vehicle dynamics: Theory and applications, Springer, New York, USA, 2008.
[17]“Simple Understanding of Kinematic Bicycle Model - Yan Ding - Medium. https://medium.com/@dingyan7361/simple-understanding-of-kinematic-bicycle-model-81cac6420357 (accessed Jun. 21, 2020).
[18]“Model Vision Sensor Detections - MATLAB & Simulink. https://www.mathworks.com/help/driving/examples/model-vision-sensor-detections.html (accessed Jun. 21, 2020).
[19]“Model Radar Sensor Detections - MATLAB & Simulink. https://www.mathworks.com/help/driving/examples/model-radar-sensor-detections.html (accessed Jun. 21, 2020).
[20]“Vehicle Dynamics - MATLAB & Simulink. https://www.mathworks.com/products/vehicle-dynamics.html (accessed Jun. 27, 2020).
[21]“Coordinate Systems in Automated Driving Toolbox - MATLAB & Simulink. https://www.mathworks.com/help/driving/ug/coordinate-systems.html (accessed Jun. 21, 2020).
[22]“Driving Scenario Tutorial - MATLAB & Simulink. https://www.mathworks.com/help/driving/examples/driving-scenario-tutorial.html (accessed Jun. 27, 2020).
[23]“Design driving scenarios, configure sensors, and generate synthetic object detections - MATLAB. https://www.mathworks.com/help/driving/ref/drivingscenariodesigner-app.html (accessed Jun. 27, 2020).
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top