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dc.contributor.authorMercelis, Siegfried
dc.contributor.authorWatelet, Sylvain
dc.contributor.authorCasteels, Wim
dc.contributor.authorBogaerts, Toon
dc.contributor.authorVan den Bergh, Joris
dc.contributor.authorReyniers, Maarten
dc.contributor.authorHellinckx, Peter
dc.coverage.spatialAntwerpenen_US
dc.coverage.temporalJune 2019en_US
dc.date2020-06-30
dc.date.accessioned2020-09-07T14:50:00Z
dc.date.available2020-09-07T14:50:00Z
dc.identifier.urihttps://orfeo.belnet.be/handle/internal/7607
dc.descriptionBad weather conditions such as heavy rain, black ice and fog can have a significant impact on road safety. Currently vehicle safety technologies such as the electronic stability program work reactive to hazardous situations. In this paper, we propose the use of crowd-sourced vehicle data to improve road-weather models and provide real-time local warnings for weather-related hazards. We present our initial results from a field test where we used vehicle CAN-bus data and low cost external sensors to observe local weather phenomena. The CAN-bus contains, among others, data on vehicle dynamics such as wheel speeds. Our approach is to isolate anomalies within these signals. Our initial research suggests some anomalies are weather related and can be used to describe local weather phenomena. Furthermore, the externally installed sensors provide more information on which we can build our assumptions. The results show that the gathered measurements are consistent with the reliable observations from road weather stations.en_US
dc.languageengen_US
dc.publisherIEEEen_US
dc.titleTowards Detection of Road Weather Conditions using Large-Scale Vehicle Fleetsen_US
dc.typeConferenceen_US
dc.subject.frascatiEarth and related Environmental sciencesen_US
dc.audienceScientificen_US
dc.subject.freeAnomaly Detection, Vehicle Data, Can-Bus, Road Safety, Road Weather Conditions, Road Weather Modelsen_US
dc.source.title2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)en_US
dc.relation.projectFlanders Innovation & Entrepreneurship (VLAIO), project nr. HBC.2017.0999en_US
Orfeo.peerreviewedYesen_US
dc.identifier.doi10.1109/VTC2020-Spring48590.2020.9128484


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