臺灣博碩士論文加值系統

時至今日，自駕車技術致力於為日常生活提供便捷。然而，現存先進駕駛輔助系統 (ADAS)的應用只能被歸類在自駕等級1至4，而非全自動等級5。基於危險與環境的考量，自動停車可能成為第一個自駕車的全自動應用。大部分自駕停車所需要的技術可從現有技術中選擇，例如：感測結合、SLAM和周圍感知等。另一方面，某些技術應該依據停車場景與考量來做開發，例如：停車格探測、運動規劃與車輛控制等。
在本篇論文中，有兩項用於自駕停車技術的關鍵方法，分別是運動規劃法與車輛控制器：
就運動規劃而言，一個基於隨機取樣的運動規劃法被提出用來從任何可行的起始點有效率地產生仿人類且免於碰撞的停車路徑於日常生活中的停車情境。此外，被提出的方法其目的是為要解決基於隨機取樣的運動規劃法的共同缺點，以維持每次執行結果的高度路徑品質與一致性。
就車輛控制而言，一個停車導向的模型預測控制器被提出來同時控制方向盤與速度以達到精確且順暢的停車路徑追蹤。更進一步來說，被提出的車輛控制器致力於解決實作問題，例如：車輛考量、即時控制與訊號延遲。因此，實作表現上得以接近在理想狀態下的表現。
為了證實兩項本篇所提出方法的效果，本篇論文中的模擬與實驗分別被執行在常見與嚴苛的停車情境中，例如：垂直停車、平行停車、斜向停車與U字型迴轉。模擬結果不僅驗證了本篇研究中所提出兩項方法中技術元素的效果，也展示處理多樣停車情境的能力。最重要的是，實車實驗充分展現了該兩項方法是可被確實實踐在日常生活中的，而非僅僅是理論上的模擬。

Nowadays, techniques of autonomous vehicles are devoted to providing convenience for people in everyday life. However, existing applications of Advanced Driver Assistance Systems (ADAS) can just be categorized into autonomous driving level 1-4 but not full automation in level 5. With the considerations of risks and environments, autonomous parking may be the first fully autonomous applications for autonomous vehicles. Most required techniques for autonomous parking can be chosen from existing ones, like sensor fusion, SLAM, and surrounding perception. On the other hand, some required techniques should be developed based on the parking scenarios and considerations, such as parking slot detection, motion planning, and vehicle control.
In this thesis, there are two key methods proposed for the autonomous parking technique, that is, a motion planner and a vehicle controller:
For motion planning, a sampling based motion planner is proposed to generate a human-like and collision-free parking path efficiently with any feasible start and goal configurations for parking scenarios in everyday life. Moreover, the proposed method aims to deal with the common defects of sampling based motion planners to maintain high path quality and consistency for each execution.
For vehicle control, a parking-oriented model predictive controller is proposed to control steering and speed simultaneously for accurate and smooth parking path tracking. Furthermore, the proposed vehicle controller is dedicated to working around the practical problems, such as vehicle considerations, real-time control, and signal delay. As a result, the practical performance can approach the performance in ideal condition.
To verify the effects of both proposed methods, the simulations and experiments in the thesis are conducted in common and strict parking scenarios, such as perpendicular parking, parallel parking, angle parking, and U-turn. The simulation results not only verify the effects of each technical element in both proposed methods, but also show the ability to deal with the various parking scenarios. The most important of all, the on-car experiments sufficiently demonstrate that both proposed methods can be actually implemented in everyday life instead of theoretical simulations.

摘要 i
ABSTRACT iii
CONTENTS vii
LIST OF FIGURES x
LIST OF TABLES xvi
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Problem Formulation 3
1.3 Contributions 6
1.3.1 Optimize-feedback Bi-RRT* with Reeds-Shepp Curve 6
1.3.2 Parking-oriented Model Predictive Control 7
1.4 Organization of the Thesis 8
Chapter 2 Background and Literature Survey 10
2.1 Advanced Driver Assistance Systems 10
2.2 Autonomous Vehicle Techniques 12
2.3 Motion Planning 13
2.4 Vehicle Control 20
2.5 Autonomous Parking System 24
Chapter 3 Related Theories 25
3.1 Vehicle Model 25
3.2 RRT* with Reeds-Shepp Curve 27
3.2.1 Optimal Rapidly-exploring Random Tree 27
3.2.2 Reeds-Shepp Curve 29
3.3 Model Predictive Control 29
3.3.1 The Periods of MPC 29
3.3.2 Linear State Space Model 31
Chapter 4 Optimize-feedback Bidirectional RRT* with Reeds-Shepp Curve 33
4.1 Overview of The Proposed Motion Planner 34
4.2 Collision Model Formulation for Various Vehicles 39
4.3 Exploration-improved Bidirectional RRT* with Reeds-Shepp Curve 41
4.3.1 The Revised Bi-RRT* with Reeds-Shepp Curve 42
4.3.2 Collision-avoidable Sampler 45
4.3.3 The Revised Radial Sampler 48
4.4 Optimize-feedback Process 51
4.5 State Space Convergence 53
4.6 Human-like Cost Function 54
4.7 Summary 57
Chapter 5 Parking-oriented Model Predictive Control 60
5.1 Overview of The Vehicle Controller 61
5.2 Tracking Strategy for Autonomous Parking 62
5.2.1 Segment by Segment Tracking 63
5.2.2 Steering Angle Initialization for Each Segment 63
5.3 Parking-oriented Cost Function and Constraint Design 65
5.4 Simultaneous Computation and Control Framework 68
5.4.1 Multi-thread Framework 69
5.4.2 Execution Time Compensation 70
5.5 Steering Command Smoother 73
5.6 Summary 74
Chapter 6 Simulation and Experimental Results and Analysis 77
6.1 Simulation and Experimental Setups 78
6.2 Method Effects of Optimize-feedback Bidirectional RRT with Reeds-Shepp Curve 88
6.2.1 Simulation Process 89
6.2.2 Collision Model Formulation for Various Vehicles 90
6.2.3 Exploration-improved Bidirectional RRT* with Reeds-Shepp Curve 92
6.2.4 Optimize-feedback Process 101
6.2.5 State Space Convergence 103
6.2.6 Human-like Cost Function 106
6.3 Method Effects of Parking-oriented Model Predictive Control 110
6.3.1 Simulation Process 110
6.3.2 Tracking Strategy for Autonomous Parking 115
6.3.3 Parking-oriented Cost Function and Constraint Design 118
6.3.4 Simultaneous Computation and Control Framework 122
6.3.5 Steering Command Smoother 126
6.4 Simulations in Autonomous Parking Scenarios 130
6.4.1 System Structure 131
6.4.2 Perpendicular Parking 132
6.4.3 Parallel Parking 144
6.4.4 Angle Parking 158
6.4.5 U-turn 171
6.5 Experiments in Autonomous Parking Scenarios 184
6.5.1 System Structure 185
6.5.2 Perpendicular Parking with ITRI Pacifica 186
6.5.3 Parallel Parking with ITRI Pacifica 204
6.5.4 Signal Delay for Practical Consideration 215
6.6 Comparison for The Proposed Motion Planner 218
6.6.1 Simulation Process 218
6.6.2 Compared to Kinematic Bi-RRT 219
6.6.3 Compared to Waypoint-Guided Bi-RRT 226
6.7 Summary 230
Chapter 7 Conclusions and Future Works 232
7.1 Conclusions 232
7.2 Future Works 235
References 237

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