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The Concept of Determining Route Signatures in Urban and Extra-Urban Driving Conditions Using Artificial Intelligence Methods

cris.lastimport.scopus2024-09-19T01:30:11Z
dc.abstract.enThe article describes the implementation of road driving tests with a vehicle in urban and extra-urban traffic conditions. Descriptions of the hardware and software needed for archiving the data obtained from the vehicle’s on-board diagnostic connector are presented. Then, the routes are analyzed using artificial intelligence methods. In this article, the reference of the route was defined as the trajectory of the driving process, represented by the engine rotational speed, the driving speed, and acceleration in the state space. The state space was separated into classes based on the results of the cluster analysis. In the experiment, five classes were clustered. The K-Means clustering algorithm was employed to determine the clusters in the variant without prior labelling of the classes using the teaching method and without participation of a teacher. In this way, the trajectories of the driving process in the five-state state space were determined. The article compares the signatures of routes created in urban and extra-urban driving conditions. Significant differences between the obtained results were indicated. Interesting methods of displaying the saved data are presented and the potential practical applications of the proposed method are indicated.
dc.affiliationTransportu i Informatyki
dc.contributor.authorArkadiusz Małek
dc.contributor.authorJacek Caban
dc.contributor.authorAgnieszka Dudziak
dc.contributor.authorAndrzej Marciniak
dc.contributor.authorJán Vrábel
dc.date.accessioned2024-04-12T08:24:20Z
dc.date.available2024-04-12T08:24:20Z
dc.date.issued2023
dc.description.abstract<jats:p>The article describes the implementation of road driving tests with a vehicle in urban and extra-urban traffic conditions. Descriptions of the hardware and software needed for archiving the data obtained from the vehicle’s on-board diagnostic connector are presented. Then, the routes are analyzed using artificial intelligence methods. In this article, the reference of the route was defined as the trajectory of the driving process, represented by the engine rotational speed, the driving speed, and acceleration in the state space. The state space was separated into classes based on the results of the cluster analysis. In the experiment, five classes were clustered. The K-Means clustering algorithm was employed to determine the clusters in the variant without prior labelling of the classes using the teaching method and without participation of a teacher. In this way, the trajectories of the driving process in the five-state state space were determined. The article compares the signatures of routes created in urban and extra-urban driving conditions. Significant differences between the obtained results were indicated. Interesting methods of displaying the saved data are presented and the potential practical applications of the proposed method are indicated.</jats:p>
dc.identifier.doi10.3390/machines11050575
dc.identifier.issn2075-1702
dc.identifier.urihttps://repo.akademiawsei.eu/handle/item/187
dc.languageen
dc.pbn.affiliationinformation and communication technology
dc.relation.ispartofMachines
dc.rightsCC-BY
dc.subject.enroad vehicles
dc.subject.entransportation
dc.subject.enecology
dc.subject.enInternet of Things
dc.subject.enartificial intelligence
dc.titleThe Concept of Determining Route Signatures in Urban and Extra-Urban Driving Conditions Using Artificial Intelligence Methods
dc.typeReviewArticle
dspace.entity.typePublication
oaire.citation.issue5
oaire.citation.volume11