| ABSTRACT | 第4-5页 |
| CHAPTER 1 INTRODUCTION | 第9-19页 |
| 1.1 Overview of the Intelligent Vehicle and its Research | 第9-14页 |
| 1.1.1 Intelligent Transportation Systems | 第9-11页 |
| 1.1.2 Intelligent Vehicle | 第11-12页 |
| 1.1.3 Competitive Driverless Car | 第12-14页 |
| 1.2 Common Sensors and Technologies | 第14-17页 |
| 1.3 Mainly Studies, Work and Organization of Research | 第17页 |
| 1.4 Specific Research Contents | 第17-18页 |
| 1.5 Research Arrangements | 第18-19页 |
| CHAPTER 2 MULTIDIMENSIONAL LASER RADAR | 第19-33页 |
| 2.1 Introduction | 第19页 |
| 2.2 General Familiarity with Laser | 第19-27页 |
| 2.2.1 System of Standard Individual Components | 第21-22页 |
| 2.2.2 Ranging Principle of IBEO LUX | 第22-27页 |
| 2.3 Establishment and Transformation between the Coordinate Systems | 第27-31页 |
| 2.3.1 Three-dimensional Coordinate System, Geometric Transformations | 第27-30页 |
| 2.3.2 Establish and Coordinate Transformation | 第30-31页 |
| 2.4 Summary of Chapter 2 | 第31-33页 |
| CHAPTER 3 EXPERIMENTAL PLATFORM AND VEHICLE TEST | 第33-43页 |
| 3.1 Test Platform Overview | 第33-36页 |
| 3.2 Software System | 第36-37页 |
| 3.2.1 MATLAB | 第36-37页 |
| 3.3 Scan and Data Receiving | 第37-41页 |
| 3.3.1 Analysis of the Scan Data | 第37-38页 |
| 3.3.2 Segmenting the Scan Data | 第38-41页 |
| 3.4 Summary of Chapter 3 | 第41-43页 |
| CHAPTER 4 ROAD AND OBJECT DETECTION BASED ON MULTI-LAYERLASER RADAR | 第43-65页 |
| 4.1 Introduction | 第43-44页 |
| 4.2 Hough Transform Method | 第44-49页 |
| 4.2.1 Implementation of the Hough Transform | 第44-46页 |
| 4.2.2 The General Algorithm of Hough Method | 第46-49页 |
| 4.3 Theil–Sen Estimator | 第49-53页 |
| 4.3.1 Definition | 第49页 |
| 4.3.2 Statistical Properties and Implementation | 第49-53页 |
| 4.4 The Method of Least Squares | 第53-58页 |
| 4.4.1 The Geometry of Ordinary Least Squares | 第54-56页 |
| 4.4.2 The least-squares Method with the Introduction of the Polynomial | 第56-58页 |
| 4.4.3 Advantages and Disadvantages of Least Squares Method | 第58页 |
| 4.5 Bounding Box | 第58-59页 |
| 4.6 The Partitioning of Data into Clusters Using the Clustering Method | 第59-62页 |
| 4.6.1 K-means Algorithm | 第59-60页 |
| 4.6.2 Standard Algorithm | 第60-61页 |
| 4.6.3 Example of Standard Algorithm | 第61-62页 |
| 4.6.4 Problem of the K-means Method | 第62页 |
| 4.7 Summary of Chapter 4 | 第62-65页 |
| CHAPTER 5 EXPERIMENTAL RESULTS AND ANALYSIS | 第65-89页 |
| 5.1 Introduction | 第65页 |
| 5.2 Object Detection with Hough Line Detection | 第65-72页 |
| 5.3 Object Detection with Theil–Sen Estimator | 第72-75页 |
| 5.4 Least Squares Method | 第75-80页 |
| 5.5 Bounding Box | 第80-82页 |
| 5.6 K-means Clustering | 第82-85页 |
| 5.6.1 Specify the Number of Clusters | 第83-85页 |
| 5.7 Comparison of Objects Detection Methods | 第85-88页 |
| 5.8 Summary of Chapter 5 | 第88-89页 |
| CONCLUSION AND OUTLOOK | 第89-91页 |
| 1. Conclusion | 第89-90页 |
| 2. Outlook | 第90-91页 |
| REFERENCES | 第91-95页 |
| Published Papers during the Academic of Master Degree | 第95-97页 |
| Acknowledgements | 第97页 |
