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Taylor, Phillip M., Griffiths, Nathan, Bhalerao, Abhir, Anand, Sarabjot Singh, Popham, T. J., Xu, Zhou and Gelencser, Adam (2016) Data mining for vehicle telemetry. Applied Artificial Intelligence, 30 (3). pp. 233-256. doi:10.1080/08839514.2016.1156954 ISSN 0883-9514.
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Official URL: http://dx.doi.org/10.1080/08839514.2016.1156954
Abstract
This article presents a data mining methodology for driving-condition monitoring via CAN-bus data that is based on the general data mining process. The approach is applicable to many driving condition problems, and the example of road type classification without the use of location information is investigated. Location information from Global Positioning Satellites and related map data are often not available (for business reasons), or cannot represent the full dynamics of road conditions. In this work, Controller Area Network (CAN)-bus signals are used instead as inputs to models produced by machine learning algorithms. Road type classification is formulated as two related labeling problems: Road Type (A, B, C, and Motorway) and Carriageway Type (Single or Dual). An investigation is presented into preprocessing steps required prior to applying machine learning algorithms, that is, signal selection, feature extraction, and feature selection. The selection methods used include principal components analysis (PCA) and mutual information (MI), which are used to determine the relevance and redundancy of extracted features and are performed in various combinations. Finally, because there is an inherent bias toward certain road and carriageway labelings, the issue of class imbalance in classification is explained and investigated. A system is produced, which is demonstrated to successfully ascertain road type from CAN-bus data, and it is shown that the classification correlates well with input signals such as vehicle speed, steering wheel angle, and suspension height.
| Item Type: | Journal Article | ||||||
|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics | ||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
| Library of Congress Subject Headings (LCSH): | Data mining, Global Positioning System | ||||||
| Journal or Publication Title: | Applied Artificial Intelligence | ||||||
| Publisher: | Taylor & Francis Inc. | ||||||
| ISSN: | 0883-9514 | ||||||
| Official Date: | 14 April 2016 | ||||||
| Dates: |
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| Volume: | 30 | ||||||
| Number: | 3 | ||||||
| Number of Pages: | 24 | ||||||
| Page Range: | pp. 233-256 | ||||||
| DOI: | 10.1080/08839514.2016.1156954 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| Date of first compliant deposit: | 28 April 2016 | ||||||
| Date of first compliant Open Access: | 14 April 2017 |
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