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Road vehicle classification using machine learning techniques
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Paper Abstract

The vehicle classification system developed by Federal Highway Administration (FHWA) of United States divides vehicle type into 13 categories depending on the number of axles and the wheelbase. However, establishing a fixed threshold for classifying a vehicle is difficult. The overlapping between vehicles pattern in the system needs a pattern recognition technique to distinguish between different vehicle categories. In this study, machine learning algorithms were used to classify various vehicles based on the collected traffic data from the embedded three-dimension Glass Fiber-Reinforced Polymer packaged Fiber Bragg Grating sensors (3D GFRP-FBG). The investigated machine learning algorithms include the support vector machines (SVM), Neural Network, and k-nearest neighbors (KNN) algorithms.

Paper Details

Date Published: 27 March 2019
PDF: 12 pages
Proc. SPIE 10970, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019, 109700O (27 March 2019); doi: 10.1117/12.2514320
Show Author Affiliations
Mu'ath Al-Tarawneh, North Dakota State Univ. (United States)
Ying Huang, North Dakota State Univ. (United States)


Published in SPIE Proceedings Vol. 10970:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019
Jerome P. Lynch; Haiying Huang; Hoon Sohn; Kon-Well Wang, Editor(s)

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