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Autonomous Intelligent Vehicles : Theory, Algorithms, and Implementation / by Hong Cheng
Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Advances in Computer Vision and Pattern Recognition | SpringerLink BücherPublisher: London : Springer London, 2011Description: Online-Ressource (X, 152p. 74 illus., 10 illus. in color, digital)ISBN:- 9781447122807
- 006.6
- 006.37
- 629.8
- TA1637-1638 TA1637-1638
- TJ217.5 .C44 2011
Contents:
Summary: 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 strPPN: PPN: 1651070121Package identifier: Produktsigel: ZDB-2-SCS
Autonomous Intelligent Vehicles; Preface; Contents; Part I: Autonomous Intelligent Vehicles; Chapter 1: Introduction; 1.1 Research Motivation and Purpose; 1.2 The Key Technologies of Intelligent Vehicles; 1.2.1 Multi-sensor Fusion Based Environment Perception and Modeling; 1.2.2 Vehicle Localization and Map Building; 1.2.3 Path Planning and Decision-Making; 1.2.4 Low-Level Motion Control; 1.3 The Organization of This Book; References; Chapter 2: The State-of-the-Art in the USA; 2.1 Introduction; 2.2 Carnegie Mellon University-Boss; 2.3 Stanford University-Junior
2.4 Virginia Polytechnic Institute and State University-Odin2.5 Massachusetts Institute of Technology-Talos; 2.6 Cornell University-Skynet; 2.7 University of Pennsylvania and Lehigh University-Little Ben; 2.8 Oshkosh Truck Corporation-TerraMax; References; Chapter 3: The Framework of Intelligent Vehicles; 3.1 Introduction; 3.2 Related Work; 3.3 Interactive Safety Analysis Framework; References; Part II: Environment Perception and Modeling; Chapter 4: Road Detection and Tracking; 4.1 Introduction; 4.2 Related Work; 4.2.1 Model-Based Approaches; 4.2.2 Multi-cue Fusion Based Approach
4.2.3 Hypothesis-Validation Based Approaches4.2.4 Neural Network Based Approaches; 4.2.5 Stereo-Based Approaches; 4.2.6 Temporal Correlation Based Approaches; 4.2.7 Image Filtering Based Approaches; 4.3 Lane Detection Using Adaptive Random Hough Transform; 4.3.1 The Lane Shape Model; 4.3.2 The Adaptive Random Hough Transform; A. Pixel Sampling on Edges; B. Multi-Resolution Parameter Estimating Strategy; 4.3.3 Experimental Results; 4.4 Lane Tracking; 4.4.1 Particle Filtering; 4.4.2 Lane Model; 4.4.3 Dynamic System Model; 4.4.4 The Imaging Model; 4.4.5 The Algorithm Implementation
4.4.5.1 Factored Sampling4.4.5.2 The Observation and Measure Models; 4.4.5.3 The Algorithm Flow; 4.5 Road Recognition Using a Mean Shift algorithm; 4.5.1 The Basic Mean Shift Algorithm; 4.5.2 Various Applications of the Mean Shift Algorithm; Mean Shift Clustering; The Mean Shift Segmentation; Mean Shift Tracking; 4.5.3 The Road Recognition Algorithm; 4.5.4 Experimental Results and Analysis; References; Chapter 5: Vehicle Detection and Tracking; 5.1 Introduction; 5.2 Related Work; 5.3 Generating Candidate ROIs; 5.4 Multi-resolution Vehicle Hypothesis
5.5 Vehicle Validation using Gabor Features and SVM5.5.1 Vehicle Representation; 5.5.2 SVM Classi?er; 5.6 Boosted Gabor Features; 5.6.1 Boosted Gabor Features Using AdaBoost; 5.6.1.1 Gabor Feature; 5.6.1.2 Boosted Gabor Features; 5.6.2 Experimental Results and Analysis; 5.6.2.1 Vehicle Database for Detection and Tracking; 5.6.2.2 Boosted Gabor Features; 5.6.2.3 Vehicle Detection Results and Discussions; References; Chapter 6: Multiple-Sensor Based Multiple-Object Tracking; 6.1 Introduction; 6.2 Related Work; 6.3 Obstacles Stationary or Moving Judgement Using Lidar Data
6.4 Multi-obstacle Tracking and Situation Assessment
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