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Analysis of machine learning techniques applied to sensory detection of vehicles in intelligent crosswalks

2020Journal article Open access
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dc.contributor.authorLozano Domínguez, José Manuel
dc.contributor.authorAl-Tam, Faroq
dc.contributor.authorMateo Sanguino, Tomás de J.
dc.contributor.authorCorreia, Noélia
dc.date.accessioned2020-12-11T17:42:28Z
dc.date.available2020-12-11T17:42:28Z
dc.date.issued2020
dc.description.abstractImproving 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.pt_PT
dc.description.sponsorshipMinistry of Economy and Knowledge of the Andalusian Government, Spain 5947pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/s20216019pt_PT
dc.identifier.eissn1424-8220
dc.identifier.urihttp://hdl.handle.net/10400.1/14900
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectSmart road safetypt_PT
dc.subjectTime series forecastingpt_PT
dc.subjectPedestrian crossings accidentspt_PT
dc.subjectVehicle detectionpt_PT
dc.subjectMachine learningpt_PT
dc.titleAnalysis of machine learning techniques applied to sensory detection of vehicles in intelligent crosswalkspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue21pt_PT
oaire.citation.startPage6019pt_PT
oaire.citation.titleSensorspt_PT
oaire.citation.volume20pt_PT
person.familyNameAl-Tam
person.familyNameCorreia
person.givenNameFaroq
person.givenNameNoélia
person.identifierR-00G-A33
person.identifierR-000-DJV
person.identifier.ciencia-id2515-AFE3-525F
person.identifier.ciencia-idDD19-1F35-B804
person.identifier.orcid0000-0001-9718-2039
person.identifier.orcid0000-0001-7051-7193
person.identifier.ridK-7031-2016
person.identifier.ridM-3554-2013
person.identifier.scopus-author-id55246034700
person.identifier.scopus-author-id8411596100
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication15ac97f4-a867-462d-9fc6-0a47bb2919d3
relation.isAuthorOfPublicationfdbe5057-0478-46cd-9506-caa73ea79d9f
relation.isAuthorOfPublication.latestForDiscoveryfdbe5057-0478-46cd-9506-caa73ea79d9f

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