Cargando…

Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System

Collision avoidance is an important feature in advanced driver-assistance systems, aimed at providing correct, timely and reliable warnings before an imminent collision (with objects, vehicles, pedestrians, etc.). The obstacle recognition library is designed and implemented to address the design and...

Descripción completa

Detalles Bibliográficos
Autores principales: Castaño, Fernando, Beruvides, Gerardo, Haber, Rodolfo E., Artuñedo, Antonio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5620580/
https://www.ncbi.nlm.nih.gov/pubmed/28906450
http://dx.doi.org/10.3390/s17092109
_version_ 1783267616358400000
author Castaño, Fernando
Beruvides, Gerardo
Haber, Rodolfo E.
Artuñedo, Antonio
author_facet Castaño, Fernando
Beruvides, Gerardo
Haber, Rodolfo E.
Artuñedo, Antonio
author_sort Castaño, Fernando
collection PubMed
description Collision avoidance is an important feature in advanced driver-assistance systems, aimed at providing correct, timely and reliable warnings before an imminent collision (with objects, vehicles, pedestrians, etc.). The obstacle recognition library is designed and implemented to address the design and evaluation of obstacle detection in a transportation cyber-physical system. The library is integrated into a co-simulation framework that is supported on the interaction between SCANeR software and Matlab/Simulink. From the best of the authors’ knowledge, two main contributions are reported in this paper. Firstly, the modelling and simulation of virtual on-chip light detection and ranging sensors in a cyber-physical system, for traffic scenarios, is presented. The cyber-physical system is designed and implemented in SCANeR. Secondly, three specific artificial intelligence-based methods for obstacle recognition libraries are also designed and applied using a sensory information database provided by SCANeR. The computational library has three methods for obstacle detection: a multi-layer perceptron neural network, a self-organization map and a support vector machine. Finally, a comparison among these methods under different weather conditions is presented, with very promising results in terms of accuracy. The best results are achieved using the multi-layer perceptron in sunny and foggy conditions, the support vector machine in rainy conditions and the self-organized map in snowy conditions.
format Online
Article
Text
id pubmed-5620580
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-56205802017-10-03 Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System Castaño, Fernando Beruvides, Gerardo Haber, Rodolfo E. Artuñedo, Antonio Sensors (Basel) Article Collision avoidance is an important feature in advanced driver-assistance systems, aimed at providing correct, timely and reliable warnings before an imminent collision (with objects, vehicles, pedestrians, etc.). The obstacle recognition library is designed and implemented to address the design and evaluation of obstacle detection in a transportation cyber-physical system. The library is integrated into a co-simulation framework that is supported on the interaction between SCANeR software and Matlab/Simulink. From the best of the authors’ knowledge, two main contributions are reported in this paper. Firstly, the modelling and simulation of virtual on-chip light detection and ranging sensors in a cyber-physical system, for traffic scenarios, is presented. The cyber-physical system is designed and implemented in SCANeR. Secondly, three specific artificial intelligence-based methods for obstacle recognition libraries are also designed and applied using a sensory information database provided by SCANeR. The computational library has three methods for obstacle detection: a multi-layer perceptron neural network, a self-organization map and a support vector machine. Finally, a comparison among these methods under different weather conditions is presented, with very promising results in terms of accuracy. The best results are achieved using the multi-layer perceptron in sunny and foggy conditions, the support vector machine in rainy conditions and the self-organized map in snowy conditions. MDPI 2017-09-14 /pmc/articles/PMC5620580/ /pubmed/28906450 http://dx.doi.org/10.3390/s17092109 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Castaño, Fernando
Beruvides, Gerardo
Haber, Rodolfo E.
Artuñedo, Antonio
Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System
title Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System
title_full Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System
title_fullStr Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System
title_full_unstemmed Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System
title_short Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System
title_sort obstacle recognition based on machine learning for on-chip lidar sensors in a cyber-physical system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5620580/
https://www.ncbi.nlm.nih.gov/pubmed/28906450
http://dx.doi.org/10.3390/s17092109
work_keys_str_mv AT castanofernando obstaclerecognitionbasedonmachinelearningforonchiplidarsensorsinacyberphysicalsystem
AT beruvidesgerardo obstaclerecognitionbasedonmachinelearningforonchiplidarsensorsinacyberphysicalsystem
AT haberrodolfoe obstaclerecognitionbasedonmachinelearningforonchiplidarsensorsinacyberphysicalsystem
AT artunedoantonio obstaclerecognitionbasedonmachinelearningforonchiplidarsensorsinacyberphysicalsystem