A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

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dc.contributor.authorShokravi, Hoofar
dc.contributor.authorShokravi, Hooman
dc.contributor.authorBakhary, Norhisham
dc.contributor.authorHeidarrezaei, Mahshid
dc.contributor.authorKoloor, Seyed Saeid Rahimian
dc.contributor.authorPetrů, Michal
dc.date.accessioned2020-06-09T06:27:41Z
dc.date.available2020-06-09T06:27:41Z
dc.date.issued2020-06-08
dc.description.abstractVehicle classification (VC) is an underlying approach in an intelligent transportation system and is widely used in various applications like the monitoring of traffic flow, automated parking systems, and security enforcement. The existing VC methods generally have a local nature and can classify the vehicles if the target vehicle passes through fixed sensors, passes through the short-range coverage monitoring area, or a hybrid of these methods. Using global positioning system (GPS) can provide reliable global information regarding kinematic characteristics; however, the methods lack information about the physical parameter of vehicles. Furthermore, in the available studies, smartphone or portable GPS apparatuses are used as the source of the extraction vehicle’s kinematic characteristics, which are not dependable for the tracking and classification of vehicles in real time. To deal with the limitation of the available VC methods, potential global methods to identify physical and kinematic characteristics in real time states are investigated. Vehicular Ad Hoc Networks (VANETs) are networks of intelligent interconnected vehicles that can provide traffic parameters such as type, velocity, direction, and position of each vehicle in a real time manner. In this study, VANETs are introduced for VC and their capabilities, which can be used for the above purpose, are presented from the available literature. To the best of the authors’ knowledge, this is the first study that introduces VANETs for VC purposes. Finally, a comparison is conducted that shows that VANETs outperform the conventional techniques.cs
dc.format.extent29 strancs
dc.identifier.WebofScienceResearcherIDG-6623-2013 Petrů, Michal
dc.identifier.doi10.3390/s20113274
dc.identifier.orcid0000-0002-1820-6379 Koloor, Seyed Saeid Rahimian
dc.identifier.orcid0000-0002-7643-8450 Petrů, Michal
dc.identifier.urihttps://dspace.tul.cz/handle/15240/157164
dc.identifier.urihttps://www.mdpi.com/1424-8220/20/11/3274
dc.language.isocscs
dc.publisherMDPI
dc.relation.ispartofSensors 2020
dc.subjectvehicle classificationcs
dc.subjectvehicular ad hoc networkscs
dc.subjectweight-in-motioncs
dc.subjectglobal positioning systemcs
dc.subjectlight detection and rangingcs
dc.subjectultrasoniccs
dc.subjectradarcs
dc.subjectvideo imagescs
dc.titleA Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniquescs
local.relation.issue11
local.relation.volume20
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