Analysis of machine learning techniques applied to sensory detection of vehicles in intelligent crosswalks
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.1/14900 |
Resumo: | Improving road safety through artificial intelligence-based systems is now crucial turning smart cities into a reality. Under this highly relevant and extensive heading, an approach is proposed to improve vehicle detection in smart crosswalks using machine learning models. Contrarily to classic fuzzy classifiers, machine learning models do not require the readjustment of labels that depend on the location of the system and the road conditions. Several machine learning models were trained and tested using real traffic data taken from urban scenarios in both Portugal and Spain. These include random forest, time-series forecasting, multi-layer perceptron, support vector machine, and logistic regression models. A deep reinforcement learning agent, based on a state-of-the-art double-deep recurrent Q-network, is also designed and compared with the machine learning models just mentioned. Results show that the machine learning models can efficiently replace the classic fuzzy classifier. |
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Analysis of machine learning techniques applied to sensory detection of vehicles in intelligent crosswalksSmart road safetyTime series forecastingPedestrian crossings accidentsVehicle detectionMachine learningImproving road safety through artificial intelligence-based systems is now crucial turning smart cities into a reality. Under this highly relevant and extensive heading, an approach is proposed to improve vehicle detection in smart crosswalks using machine learning models. Contrarily to classic fuzzy classifiers, machine learning models do not require the readjustment of labels that depend on the location of the system and the road conditions. Several machine learning models were trained and tested using real traffic data taken from urban scenarios in both Portugal and Spain. These include random forest, time-series forecasting, multi-layer perceptron, support vector machine, and logistic regression models. A deep reinforcement learning agent, based on a state-of-the-art double-deep recurrent Q-network, is also designed and compared with the machine learning models just mentioned. Results show that the machine learning models can efficiently replace the classic fuzzy classifier.Ministry of Economy and Knowledge of the Andalusian Government, Spain 5947MDPISapientiaLozano Domínguez, José ManuelAl-Tam, FaroqMateo Sanguino, Tomás de J.Correia, Noélia2020-12-11T17:42:28Z20202020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.1/14900eng10.3390/s202160191424-8220info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-24T10:27:14Zoai:sapientia.ualg.pt:10400.1/14900Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:05:50.602271Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Analysis of machine learning techniques applied to sensory detection of vehicles in intelligent crosswalks |
title |
Analysis of machine learning techniques applied to sensory detection of vehicles in intelligent crosswalks |
spellingShingle |
Analysis of machine learning techniques applied to sensory detection of vehicles in intelligent crosswalks Lozano Domínguez, José Manuel Smart road safety Time series forecasting Pedestrian crossings accidents Vehicle detection Machine learning |
title_short |
Analysis of machine learning techniques applied to sensory detection of vehicles in intelligent crosswalks |
title_full |
Analysis of machine learning techniques applied to sensory detection of vehicles in intelligent crosswalks |
title_fullStr |
Analysis of machine learning techniques applied to sensory detection of vehicles in intelligent crosswalks |
title_full_unstemmed |
Analysis of machine learning techniques applied to sensory detection of vehicles in intelligent crosswalks |
title_sort |
Analysis of machine learning techniques applied to sensory detection of vehicles in intelligent crosswalks |
author |
Lozano Domínguez, José Manuel |
author_facet |
Lozano Domínguez, José Manuel Al-Tam, Faroq Mateo Sanguino, Tomás de J. Correia, Noélia |
author_role |
author |
author2 |
Al-Tam, Faroq Mateo Sanguino, Tomás de J. Correia, Noélia |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Sapientia |
dc.contributor.author.fl_str_mv |
Lozano Domínguez, José Manuel Al-Tam, Faroq Mateo Sanguino, Tomás de J. Correia, Noélia |
dc.subject.por.fl_str_mv |
Smart road safety Time series forecasting Pedestrian crossings accidents Vehicle detection Machine learning |
topic |
Smart road safety Time series forecasting Pedestrian crossings accidents Vehicle detection Machine learning |
description |
Improving road safety through artificial intelligence-based systems is now crucial turning smart cities into a reality. Under this highly relevant and extensive heading, an approach is proposed to improve vehicle detection in smart crosswalks using machine learning models. Contrarily to classic fuzzy classifiers, machine learning models do not require the readjustment of labels that depend on the location of the system and the road conditions. Several machine learning models were trained and tested using real traffic data taken from urban scenarios in both Portugal and Spain. These include random forest, time-series forecasting, multi-layer perceptron, support vector machine, and logistic regression models. A deep reinforcement learning agent, based on a state-of-the-art double-deep recurrent Q-network, is also designed and compared with the machine learning models just mentioned. Results show that the machine learning models can efficiently replace the classic fuzzy classifier. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-11T17:42:28Z 2020 2020-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.1/14900 |
url |
http://hdl.handle.net/10400.1/14900 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.3390/s20216019 1424-8220 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799133298806489088 |