Abstract
This paper describes a virtual instrument capable of the automatic and quasi-instantaneous classification of a vehicle according to category when it is driving along the road. The vehicle’s classification is based on accurate measurements of both the vehicle’s speed and its wheelbase. Our research is focused on achieving accurate speed and wheelbase measurements and then determining the category of the vehicle through the developed software. The vehicle categorization is based on the wheelbase measurements and the number of axles and metal masses of the vehicle. The system has a complementary magnetic sensor, which helps in classifying the vehicle when the wheelbase measurement could be representative of different categories, and a camera to confirm the results of the experiment. The proposed measurement system presents a novel method for classifying vehicles according to type using piezoelectric transducers (piezo sensors). In addition, no system references have been found that encompass the functionalities of the presented system based on the information of only two piezoelectric transducers. The system has important advantages over current alternatives (systems based on inductive loops, cameras, fiber optic sensors or lasers), the installation is simple and non-invasive and with a success rate of the classification greater than 90%. The system consists of a signal acquisition point on the pavement, signal conditioning hardware and a data acquisition (DAQ) module, which links the hardware and the virtual instrument developed in LabVIEW®. Finally, the system has been tested on the road with real traffic, and the experimental results are presented and discussed in this paper.
Highlights
In recent decades there has been a considerable increase in intelligent electronic systems applied to vehicles and infrastructure
This paper describes a virtual instrument capable of the automatic and quasi-instantaneous classification of a vehicle according to category when it is driving along the road
The system consists of a signal acquisition point on the pavement, signal conditioning hardware and a data acquisition (DAQ) module, which links the hardware and the virtual instrument developed in LabVIEW®
Summary
In recent decades there has been a considerable increase in intelligent electronic systems applied to vehicles and infrastructure. In so-called “smart cities” and “smart roads” [10], it is currently of great interest to know the quantities and categories of vehicles that are circulating in “real time” For this reason, within the scientific technology of electronic instrumentation and measurement and control systems, there are different lines of work [1,2,3,4,5,6] focused on meeting this demand. The second problem is the large volume of data to be processed, which makes the detection and classification slow Another problem is that the location of the camera directly influences the calculation parameters in the detection algorithm [11], and this makes the settings required in each installation variable. Error estimation: An analysis of the sources of error is carried out and the estimate of the maximum error is calculated
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