Introducing my Bachelor’s Final Project in Vehicle Recognition Technology

In Spain, when pursuing an engineering degree, the last step before graduation is the development of a final project. Electronic communications engineering covers many different specializations, ranging from analog electronics to embedded systems programming.

My final project involved the design and implementation of a Virtual Instrument for wheeled vehicle recognition. A virtual instrument integrates measurement hardware with software applications, typically executed on a computer. In this specific project, the hardware component integrates piezoelectric cables, a magnetic sensor, along with their corresponding signal conditioning circuits and data acquisition (DAQ) systems. Meanwhile, the software aspect, developed using LabVIEW, facilitates data processing for vehicle categorization. Such devices are typically recognized as Intelligent Transport Systems (ITS).

The project documentation is detailed and breaks down its parts clearly. However, to make my work accessible to interested English-speaking individuals, I aim to present the core concepts of the project here.

The designed virtual instrument employs a categorization method that combines two key ideas. By utilizing signals from two piezoelectric cables, the system can accurately identify the vehicle’s speed, number of axles, and the distance between them. Simultaneously, the magnetic sensor captures a magnetic footprint of the vehicle, providing valuable supplementary information for verification. This system distinguishes with high degree of accuracy between various types of vehicles, including small cars, hatchbacks, sedans, vans, trucks, and buses.

Most ITS solutions for vehicle categorization rely on cameras and image recognition algorithms. In contrast, the presented device stands out as an excellent choice for specific locations where minimal infrastructure impact is desired, and factors like low installation cost, low maintenance, or challenging environmental conditions, such as heavy rain, hinder the performance of image-based alternatives available in the market.

You can check the full document at the “UPM Digital Archive”: