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Proceedings Paper

Vehicle classification by pattern- matching gage sensors
Author(s): Dryver R. Huston; William B. Spillman Jr.; Richard O. Claus; Vivek Arya; Noel Zabaronick
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Paper Abstract

This paper describes a method of using matched-pattern gage sensors that are embedded into highway pavements to classify vehicles, i.e. cars vs. trucks. The classification of vehicle type is an important technology for a variety of highway operations, e.g. traffic control, maintenance planning, weigh-in-motion, and the assignment of tolls. Vehicle classification schemes that are based on strip-crossing methods are not very robust due to the large variability of strip-crossing sequences. Visual methods still rely primarily on human identification. The method described here involves placing long gage length sensors in highway pavements. The spatial pattern of the sensor is configured so that it will match the wheel pattern of the type of vehicle that is being identified. Theoretical modeling shows that the signal received from the sensor is a cross-correlation function relating the wheel and sensor patterns in space and time. The sensor can be any one of a variety that transduce by integrating pressure along a length. The technique is demonstrated in the laboratory with PVDF and fiber optic sensors. Experimental results and computer simulations are presented as well as a discussion of the realistic possibility of using such a vehicle identification scheme under field conditions.

Paper Details

Date Published: 30 May 1996
PDF: 12 pages
Proc. SPIE 2718, Smart Structures and Materials 1996: Smart Sensing, Processing, and Instrumentation, (30 May 1996); doi: 10.1117/12.240865
Show Author Affiliations
Dryver R. Huston, Univ. of Vermont (United States)
William B. Spillman Jr., Univ. of Vermont (United States)
Richard O. Claus, Virginia Polytechnic Institute and State Univ. (United States)
Vivek Arya, Virginia Polytechnic Institute and State Univ. (United States)
Noel Zabaronick, Virginia Polytechnic Institute and State Univ. (United States)


Published in SPIE Proceedings Vol. 2718:
Smart Structures and Materials 1996: Smart Sensing, Processing, and Instrumentation
Kent A. Murphy; Dryver R. Huston, Editor(s)

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