Design, Implementation, and Configuration of Laser Systems for Vehicle Detection and Classification in Real Time

Author:

Ripoll Nieves Gallego,Aguilera Luís Enrique Gómez,Belenguer Ferran MocholíORCID,Salcedo Antonio MocholíORCID,Ballester Merelo Francisco JoséORCID

Abstract

The use of real-time vehicle detection and classification systems is essential for the accurate management of traffic and road infrastructure. Over time, diverse systems have been proposed for it, such as the widely known magnetic loops or microwave radars. However, these types of sensors do not offer all the information currently required for exhaustive and comprehensive traffic control. Thus, this paper presents the design, implementation, and configuration of laser systems to obtain 3D profiles of vehicles, which collect more precise information about the state of the roads. Nevertheless, to obtain reliable information on vehicle traffic by means of these systems, it is fundamental to correctly carry out a series of preliminary steps: choose the most suitable type of laser, select its configuration properly, determine the optimal location, and process the information provided accurately. Therefore, this paper details a series of criteria to help make these crucial and difficult decisions. Furthermore, following these guidelines, a complete laser system implemented for vehicle detection and classification is presented as result, which is characterized by its versatility and the ability to control up to four lanes in real time.

Funder

Universitat Politècnica de València

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Acoustic Vehicle Classification using Deep Learning Trained on a Spectrogram and Scalogram Fusion;2024-05-08

2. Road Traffic Analysis Using 2D LIDAR;2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS);2024-01-03

3. Emerging Sensors Techniques and Technologies for Intelligent Environments;Sensors;2022-08-26

4. Edge preserving noise robust deep learning networks for vehicle classification;Concurrency and Computation: Practice and Experience;2022-07-21

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