| Document Type
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BL
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| Record Number
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891123
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| Main Entry
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Wang, Shifeng
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| Title & Author
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Road terrain classification technology for autonomous vehicle /\ Shifeng Wang.
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| Publication Statement
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Singapore :: Springer,, [2019]
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| Series Statement
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Unmanned system technologies
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| Page. NO
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1 online resource
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| ISBN
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9789811361555
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: 9789811361562
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: 9789811361579
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: 981136155X
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: 9811361568
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: 9811361576
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9789811361548
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9811361541
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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Intro; Preface; Contents; Abbreviations; List of Figures; List of Tables; 1 Introduction; 1.1 Background; 1.2 Motivation; 1.3 Overview; References; 2 Review of Related Work; 2.1 Accelerometer Applications; 2.1.1 Small-Sized Rover Platform Using Accelerometer; 2.1.2 Road Vehicle Using Accelerometer; 2.2 Camera Applications; 2.2.1 Small-Sized Rover Using Camera; 2.2.2 Road Vehicle Using Camera; 2.3 LRF Applications; 2.3.1 Small-Sized Rover Using LRF; 2.3.2 Road Vehicle Using LRF; 2.4 Mutiple-sensor Applications; 2.4.1 Small-Sized Rover Using Multiple Sensors
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2.4.2 Road Vehicle Using Multiple Sensors2.5 Conclusion; References; 3 Acceleration-Based Road Terrain Classification; 3.1 Road Profile Estimation; 3.1.1 Acceleration (acc-t); 3.1.2 Quarter Vehicle Model (acc-t to y-t); 3.1.3 Vertical Displacement (y-t); 3.1.4 Speed (v-t); 3.1.5 Speed to Displacement (v-t to x-t); 3.1.6 Road Profile (y-x); 3.2 Features Extraction; 3.2.1 FFT Feature Extracted from Road Profile (y-x); 3.2.2 FFT Feature Extracted from Acceleration (acc-t); 3.2.3 Fast Wavelet Transform Feature Extracted from Acceleration (acc-t) and Road Profile (y-x); 3.3 Normalization
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3.4 Principal Component Analysis3.5 K-Fold Cross-Validation; 3.6 Alternative Classifiers; 3.6.1 Naïve Bayes Classifier; 3.6.2 Neural Network Classifier; 3.6.3 Support Vector Machines Classifier; 3.7 Experiment; 3.7.1 Experiment Platform; 3.7.2 Acceleration-Based Experiments; 3.8 Experiment Results; 3.8.1 Feature Selection; 3.8.2 Speed Dependency; 3.8.3 Classifiers Selection; 3.8.4 Acceleration-Based Experiment Result; 3.9 Conclusion; References; 4 Image-Based Road Terrain Classification; 4.1 Texture Features from Image; 4.2 Image Feature Matrix Establishment
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4.2.1 Grey-Level Co-occurrence Matrix4.2.2 Feature Extraction and Feature Matrix Formation; 4.3 Experiment; 4.3.1 Experimental Platform; 4.3.2 Image-Based Experiments; 4.4 Experiment Results; 4.5 Conclusion; References; 5 LRF-Based Road Terrain Classification; 5.1 Geometric Arrangement of the LRF; 5.2 Reconstruction of the Road Surface; 5.2.1 Range Data Processing; 5.2.2 Speed Data Processing; 5.2.3 Road Surface; 5.3 Feature Matrix; 5.4 Experiment; 5.4.1 Experimental Platform; 5.4.2 LRF-Based Experiments; 5.5 Experiment Results; 5.5.1 Speed Independency; 5.5.2 LRF-Based Experiment
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5.6 ConclusionReferences; 6 Multiple-Sensor Based Road Terrain Classification; 6.1 Predicting LRF-Based Probe; 6.2 Markov Random Field; 6.2.1 Conditional Independence Properties; 6.2.2 Factorization Properties; 6.3 Establishment of MRF Application; 6.3.1 Nodes in MRF; 6.3.2 Variable Values of Nodes in MRF; 6.3.3 Clique Potentials in MRF; 6.3.4 Values of Clique Potentials in MRF; 6.3.5 Energy Function; 6.3.6 Optimization; 6.4 Experiment; 6.4.1 Experimental Platform; 6.4.2 Multiple-Sensor Fusion Based Experiment; 6.5 Experiment Results; 6.6 Conclusion; References; 7 Summary; 7.1 Conclusion
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| Abstract
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This book provides cutting-edge insights into autonomous vehicles and road terrain classification, and introduces a more rational and practical method for identifying road terrain. It presents the MRF algorithm, which combines the various sensors? classification results to improve the forward LRF for predicting upcoming road terrain types. The comparison between the predicting LRF and its corresponding MRF show that the MRF multiple-sensor fusion method is extremely robust and effective in terms of classifying road terrain. The book also demonstrates numerous applications of road terrain classification for various environments and types of autonomous vehicle, and includes abundant illustrations and models to make the comparison tables and figures more accessible.
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| Subject
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Automated vehicles.
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| Subject
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Automated vehicles.
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| Subject
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TECHNOLOGY ENGINEERING-- Engineering (General)
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| Dewey Classification
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629.046
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| LC Classification
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TL152.8.W36 2019
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