Autonomous Intelligent Vehicles Theory, Algorithms, and Implementation

✍ Scribed by Cheng, Hong

• Publisher: Springer London
• Year: 2011
• Tongue: English
• Leaves: 150
• Series: Advances in Computer Vision and Pattern Recognition
• Category: Library

✦ Synopsis

Autonomous intelligent vehicles pose unique challenges in robotics, that encompass issues of environment perception and modeling, localization and map building, path planning and decision-making, and motion control.

This important text/reference presents state-of-the-art research on intelligent vehicles, covering not only topics of object/obstacle detection and recognition, but also aspects of vehicle motion control. With an emphasis on both high-level concepts, and practical detail, the text links theory, algorithms, and issues of hardware and software implementation in intelligent vehicle research.

Topics and features: presents a thorough introduction to the development and latest progress in intelligent vehicle research, and proposes a basic framework; provides detection and tracking algorithms for structured and unstructured roads, as well as on-road vehicle detection and tracking algorithms using boosted Gabor features; discusses an approach for multiple sensor-based multiple-object tracking, in addition to an integrated DGPS/IMU positioning approach; examines a vehicle navigation approach using global views; introduces algorithms for lateral and longitudinal vehicle motion control.

An essential reference for researchers in the field, the broad coverage of all aspects of this research will also appeal to graduate students of computer science and robotics who are interested in intelligent vehicles.

✦ Table of Contents

Autonomous Intelligent Vehicles......Page 2
Preface......Page 4
Contents......Page 6
Part I: Autonomous Intelligent Vehicles......Page 10
1.1 Research Motivation and Purpose......Page 11
1.2 The Key Technologies of Intelligent Vehicles......Page 13
1.2.1 Multi-sensor Fusion Based Environment Perception and Modeling......Page 14
1.2.2 Vehicle Localization and Map Building......Page 15
1.2.3 Path Planning and Decision-Making......Page 16
1.3 The Organization of This Book......Page 17
References......Page 18
2.2 Carnegie Mellon University-Boss......Page 20
2.3 Stanford University-Junior......Page 22
2.4 Virginia Polytechnic Institute and State University-Odin......Page 23
2.5 Massachusetts Institute of Technology-Talos......Page 24
2.6 Cornell University-Skynet......Page 25
2.7 University of Pennsylvania and Lehigh University-Little Ben......Page 26
2.8 Oshkosh Truck Corporation-TerraMax......Page 27
References......Page 28
3.1 Introduction......Page 30
3.2 Related Work......Page 31
3.3 Interactive Safety Analysis Framework......Page 32
References......Page 35
Part II: Environment Perception and Modeling......Page 37
4.1 Introduction......Page 38
4.2.2 Multi-cue Fusion Based Approach......Page 40
4.2.5 Stereo-Based Approaches......Page 41
4.3.1 The Lane Shape Model......Page 42
4.3.2 The Adaptive Random Hough Transform......Page 43
A. Pixel Sampling on Edges......Page 44
B. Multi-Resolution Parameter Estimating Strategy......Page 45
4.3.3 Experimental Results......Page 46
4.4.1 Particle Filtering......Page 48
4.4.2 Lane Model......Page 50
4.4.4 The Imaging Model......Page 51
4.4.5.1 Factored Sampling......Page 53
4.4.5.2 The Observation and Measure Models......Page 54
4.4.5.3 The Algorithm Flow......Page 55
4.5 Road Recognition Using a Mean Shift algorithm......Page 56
4.5.1 The Basic Mean Shift Algorithm......Page 57
The Mean Shift Segmentation......Page 59
4.5.3 The Road Recognition Algorithm......Page 60
4.5.4 Experimental Results and Analysis......Page 61
References......Page 63
5.1 Introduction......Page 65
5.2 Related Work......Page 66
5.3 Generating Candidate ROIs......Page 67
5.4 Multi-resolution Vehicle Hypothesis......Page 69
5.5.1 Vehicle Representation......Page 71
5.5.2 SVM Classifier......Page 72
5.6 Boosted Gabor Features......Page 75
5.6.1.2 Boosted Gabor Features......Page 76
5.6.2.2 Boosted Gabor Features......Page 78
5.6.2.3 Vehicle Detection Results and Discussions......Page 81
References......Page 83
6.2 Related Work......Page 85
6.3 Obstacles Stationary or Moving Judgement Using Lidar Data......Page 86
6.4.1.1 Probability Framework of Tracking......Page 88
6.4.1.2 System Model......Page 89
6.4.1.4 Data Association for a Single Sensor......Page 91
2. Observation-to-Track Association......Page 92
6.4.2.2 Track Association......Page 94
6.5 Conclusion and Future Work......Page 96
References......Page 98
Part III: Vehicle Localization and Navigation......Page 100
7.1 Introduction......Page 101
7.2 Related Work......Page 102
7.3.1 The System Equation......Page 103
7.3.2 The Measurement Equation......Page 106
7.3.3 Data Fusion Using EKF......Page 107
References......Page 108
8.1 Introduction......Page 110
8.2 The Problem and Proposed Approach......Page 111
8.3 The Panoramic Imaging Model......Page 113
8.4.1 The Mapping Relationship Between Each Image and a Panoramic Image......Page 115
8.4.2 The Panoramic Inverse Perspective Mapping......Page 116
8.5.2 Calculation of Each Interest Point's View Angle in the Vehicle Coordinate System......Page 117
8.5.4 Image Interpolation in the Vehicle Coordinate System......Page 118
8.6 The Elimination of Wide-Angle Lens' Radial Error......Page 119
8.7 Combining Panoramic Images with Electronic Maps......Page 120
References......Page 121
Part IV: Advanced Vehicle Motion Control......Page 123
9.2 Related Work......Page 124
9.3 The Mixed Lateral Control Strategy......Page 125
2. Calculating Looking-Ahead Error......Page 126
9.3.2 Curvilinear Roads......Page 127
2. The Proposed Segmenting Approach of Contours......Page 128
9.3.3 Calculating the Radius of an Arc......Page 130
9.3.4 The Algorithm Flow......Page 131
9.4 The Relationship Between Motor Pulses and the Front Wheel Lean Angle......Page 132
References......Page 135
10.1 Introduction......Page 137
10.2.1 The First-Order Systems......Page 138
10.2.2 First-Order Lag Systems......Page 140
1. The First-Order System Assumption......Page 141
3. Validating Second-Order Assumption......Page 142
10.3.1 Validating the Longitudinal Control System Function......Page 143
10.3.2 Velocity Controller Design......Page 144
10.4 Experimental Results and Analysis......Page 146
References......Page 148
Index......Page 149