학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. 서승우.This dissertation aims to present precise and cost-efficient mapping and localization algorithms for autonomous vehicles. Mapping and localization are ones of the key components in autonomous vehicles. The major concern for mapping and localization research is maximizing the accuracy and precision of the systems while minimizing the cost. For this goal, this dissertation proposes a road map generation system to create a precise and efficient lane-level road map, and a localization system based on the proposed road map and affordable sensors.
In chapter 2, the road map generation system is presented. The road map generation system integrates a 3D LIDAR
data and high-precision vehicle positioning system to acquire accurate road geometry
data. Acquired road geometry data is represented as sets of piecewise
polynomial curves in order to increase the storage efficiency and the usability.
From extensive experiments using a real urban and highway road data, it is verified that
the proposed road map generation system generates a road map that is
accurate and more efficient than previous road maps in terms of the storage
efficiency and usability.
In chapter 3, the localization system is presented. The localization system targets an environment
that the localization is difficult due to the lack of feature information for localization. The proposed system integrates the lane-level road map presented in chapter 2, and various low-cost sensors for accurate and cost-effective vehicle localization. A measurement ambiguity problem due to the use of low-cost sensors and poor feature
information was presented, and a probabilistic measurement association-based particle
filter is proposed to resolve the measurement ambiguity problem. Experimental results using a real highway road data is presented to verify the accuracy and reliability of the localization system.
In chapter 4, an application of the accurate vehicle localization system is presented. It is demonstrated that sharing of accurate position information among vehicles can improve the traffic flow and suppress the traffic jam effectively. The effect of the position information sharing is evaluated based on numerical experiments. For this, a traffic model is proposed by extending conventional SOV traffic model. The numerical experiments show that the traffic flow is increased based on accurate vehicle localization and information sharing among vehicles.Chapter 1 Introduction 1
1.1 Background andMotivations 1
1.2 Contributions and Outline of the Dissertation 3
1.2.1 Generation of a Precise and Efficient Lane-Level Road Map 3
1.2.2 Accurate and Cost-Effective Vehicle Localization in Featureless Environments 4
1.2.3 An Application of Precise Vehicle Localization: Traffic Flow Enhancement Through the Sharing of Accurate Position Information Among Vehicles 4
Chapter 2 Generation of a Precise and Efficient Lane-Level Road Map 6
2.1 RelatedWorks 9
2.1.1 Acquisition of Road Geometry 11
2.1.2 Modeling of Road Geometry 13
2.2 Overall System Architecture 15
2.3 Road Geometry Data Acquisition and Processing 17
2.3.1 Data Acquisition 18
2.3.2 Data Processing 18
2.3.3 Outlier Problem 26
2.4 RoadModeling 27
2.4.1 Overview of the sequential approximation algorithm 29
2.4.2 Approximation Process 30
2.4.3 Curve Transition 35
2.4.4 Arc length parameterization 38
2.5 Experimental Validation 39
2.5.1 Experimental Setup 39
2.5.2 Data Acquisition and Processing 40
2.5.3 RoadModeling 42
2.6 Summary 49
Chapter 3 Accurate and Cost-Effective Vehicle Localization in Featureless Environments 51
3.1 RelatedWorks 53
3.2 SystemOverview 57
3.2.1 Test Vehicle and Sensor Configuration 57
3.2.2 Augmented RoadMap Data 57
3.2.3 Vehicle Localization SystemArchitecture 61
3.2.4 ProblemStatement 62
3.3 Particle filter-based Vehicle Localization Algorithm 63
3.3.1 Initialization 65
3.3.2 Time Update 66
3.3.3 Measurement Update 66
3.3.4 Integration 68
3.3.5 State Estimation 68
3.3.6 Resampling 69
3.4 Map-Image Measurement Update with Probabilistic Data Association 69
3.4.1 Lane Marking Extraction and Measurement Error Model 70
3.5 Experimental Validation 76
3.5.1 Experimental Environments 76
3.5.2 Localization Accuracy 77
3.5.3 Effect of the Probabilistic Measurement Association 79
3.5.4 Effect of theMeasurement ErrorModel 80
3.6 Summary 80
Chapter 4 An Application of Precise Vehicle Localization: Traffic Flow Enhancement Through the Sharing of Accurate Position Information Among Vehicles 82
4.1 Extended SOVModel 84
4.1.1 SOVModel 85
4.1.2 Extended SOVModel 89
4.2 Results and Discussions 91
4.3 Summary 93
Chapter 5 Conclusion 95
Bibliography 97
국문 초록 108Docto
