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Πεδίο DCΤιμήΓλώσσα
dc.contributor.authorMichail, Harris-
dc.contributor.authorGregoriades, Andreas-
dc.contributor.authorDimitriou, Loukas-
dc.contributor.authorPampaka, Maria-
dc.contributor.authorGeorgiades, Michael-
dc.date.accessioned2023-04-03T09:35:45Z-
dc.date.available2023-04-03T09:35:45Z-
dc.date.issued2022-
dc.identifier.citation12th International Workshop on Agents in Traffic and Transportation held in conjunction with IJCAI-ECAI, 2022en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14279/28998-
dc.description.abstractPedestrian tourists are considered the most vulnerable road users of urban mobility environments. Tourists are a special category of pedestrians, exhibiting different visual behaviour to residents due to their enthusiasm and unfamiliarity with the environment. These characteristics of pedestrian touristsinfluence their hazard perception. Eye tracking technology became popular in investigating pedestrian safety problems after findings that eye-gaze behaviour is linked with human attention and hazard anticipation. The majority of eye-tracking studies to date use stationary technology that may miss out important properties relating to environmental dynamics that cannot be accurately simulated. This study employs a novel method utilising mobile eye-tracking technology in naturalistic settings to investigate the application of machine learning in identifying differences between tourist and resident pedestrians’ visual behaviour. Eye tracking metrics are used to train an Extreme Gradient Boost (XGBoost) model to examine whether tourists have less hazard perception than residents when visiting destinations with opposite driving conventions to their own. Preliminary results with a small group of tourist and resident pedestrians demonstrate how such machine learning models could be used in real-time by agent-based systems that utilise wearable augmented reality displays to support hazard perception of tourist pedestrians.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.rights©️ Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).en_US
dc.subjectWearable Augmented Reality Displaysen_US
dc.subjectPedestrian safetyen_US
dc.subjectMobile eye trackingen_US
dc.subjectXGBoost classificationen_US
dc.titleMachine Learning Analysis of Pedestrians' Hazard Anticipation from Eye Tracking Dataen_US
dc.typeConference Papersen_US
dc.linkhttps://ceur-ws.org/Vol-3173/10.pdfen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationUniversity of Cyprusen_US
dc.collaborationUniversity of Manchesteren_US
dc.collaborationNeapolis University Pafosen_US
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.journalsOpen Accessen_US
dc.countryCyprusen_US
dc.countryUnited Kingdomen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.relation.conference12th International Workshop on Agents in Traffic and Transportation held in conjunction with IJCAI-ECAI 2022en_US
cut.common.academicyear2022-2023en_US
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.cerifentitytypePublications-
item.openairetypeconferenceObject-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.deptDepartment of Communication and Marketing-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.facultyFaculty of Communication and Media Studies-
crisitem.author.orcid0000-0002-8299-8737-
crisitem.author.orcid0000-0002-7422-1514-
crisitem.author.parentorgFaculty of Engineering and Technology-
crisitem.author.parentorgFaculty of Communication and Media Studies-
Εμφανίζεται στις συλλογές:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
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