9 An experimental study on road surface classification
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Addisson Salazar
Abstract
This chapter presents an experimental study that explores the discrimination capabilities of a set of features for road surface classification. The features are extracted from signals collected by a number of sensors installed in a vehicle traveling on three different surfaces: smooth flat asphalt, stripes, and cobblestones. The features consist of time-, frequency-, and statistic-domain parameters that represent the behavior of the signals under different driving conditions. The accuracy on road classification from those features is a determining factor in the quality of automatic power-assisted steering systems. The experiments implemented several driving configurations, i.e., hands on or off the wheel and constant or accelerated vehicle speed. In addition, several setups of the classification procedure were tested varying the classifier method (random forest, support vector machine, linear discriminant analysis, and a decision fusion method) and the number of features (selected features by ranking and reducing the number of features using principal component analysis). The results show high accuracy of the proposed classification system for road surface classification.
Abstract
This chapter presents an experimental study that explores the discrimination capabilities of a set of features for road surface classification. The features are extracted from signals collected by a number of sensors installed in a vehicle traveling on three different surfaces: smooth flat asphalt, stripes, and cobblestones. The features consist of time-, frequency-, and statistic-domain parameters that represent the behavior of the signals under different driving conditions. The accuracy on road classification from those features is a determining factor in the quality of automatic power-assisted steering systems. The experiments implemented several driving configurations, i.e., hands on or off the wheel and constant or accelerated vehicle speed. In addition, several setups of the classification procedure were tested varying the classifier method (random forest, support vector machine, linear discriminant analysis, and a decision fusion method) and the number of features (selected features by ranking and reducing the number of features using principal component analysis). The results show high accuracy of the proposed classification system for road surface classification.
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
-
Methods and instrumentation
- 1 Identifying and estimating outliers in time series with nonstationary mean through multiobjective optimization method 1
- 2 Using the intentionally linked entities (ILE) database system to create hypergraph databases with fast and reliable relationship linking, with example applications 21
- 3 Rapid and automated determination of cluster numbers for high-dimensional big data: a comprehensive update 37
- 4 Canonical correlation analysis and exploratory factor analysis of the four major centrality metrics 49
- 5 Navigating the landscape of automated data preprocessing: an in-depth review of automated machine learning platforms 71
- 6 Generating random XML 83
-
Applications and case studies
- 7 Exploring autism risk: a deep dive into graph neural networks and gene interaction data 105
- 8 Leveraging ChatGPT and table arrangement techniques in advanced newspaper content analysis for stock insights 121
- 9 An experimental study on road surface classification 145
- 10 RNN models for evaluating financial indices: examining volatility and demand-supply shifts in financial markets during COVID-19 165
- 11 Topological methods for vibration feature extraction 185
- 12 Dyna-SPECTS: DYNAmic enSemble of Price Elasticity Computation models using Thompson Sampling in e-commerce 215
- 13 Creating a metadata schema for reservoirs of data: a systems engineering approach 251
- 14 Implementation and evaluation of an eXplainable artificial intelligence to explain the evaluation of an assessment analytics algorithm for freetext exams in psychology courses in higher education to attest QBLM-based competencies 271
- 15 Toward a skill-centered qualification ontology supporting data mining of human resources in knowledge-based enterprise process representations 307
- Index 333
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
-
Methods and instrumentation
- 1 Identifying and estimating outliers in time series with nonstationary mean through multiobjective optimization method 1
- 2 Using the intentionally linked entities (ILE) database system to create hypergraph databases with fast and reliable relationship linking, with example applications 21
- 3 Rapid and automated determination of cluster numbers for high-dimensional big data: a comprehensive update 37
- 4 Canonical correlation analysis and exploratory factor analysis of the four major centrality metrics 49
- 5 Navigating the landscape of automated data preprocessing: an in-depth review of automated machine learning platforms 71
- 6 Generating random XML 83
-
Applications and case studies
- 7 Exploring autism risk: a deep dive into graph neural networks and gene interaction data 105
- 8 Leveraging ChatGPT and table arrangement techniques in advanced newspaper content analysis for stock insights 121
- 9 An experimental study on road surface classification 145
- 10 RNN models for evaluating financial indices: examining volatility and demand-supply shifts in financial markets during COVID-19 165
- 11 Topological methods for vibration feature extraction 185
- 12 Dyna-SPECTS: DYNAmic enSemble of Price Elasticity Computation models using Thompson Sampling in e-commerce 215
- 13 Creating a metadata schema for reservoirs of data: a systems engineering approach 251
- 14 Implementation and evaluation of an eXplainable artificial intelligence to explain the evaluation of an assessment analytics algorithm for freetext exams in psychology courses in higher education to attest QBLM-based competencies 271
- 15 Toward a skill-centered qualification ontology supporting data mining of human resources in knowledge-based enterprise process representations 307
- Index 333
