Towards Detection of Road Weather Conditions using Large-Scale Vehicle Fleets
| dc.contributor.author | Mercelis, Siegfried | |
| dc.contributor.author | Watelet, Sylvain | |
| dc.contributor.author | Casteels, Wim | |
| dc.contributor.author | Bogaerts, Toon | |
| dc.contributor.author | Van den Bergh, Joris | |
| dc.contributor.author | Reyniers, Maarten | |
| dc.contributor.author | Hellinckx, Peter | |
| dc.coverage.spatial | Antwerpen | en_US |
| dc.coverage.temporal | June 2019 | en_US |
| dc.date | 2020-06-30 | |
| dc.date.accessioned | 2020-09-07T14:50:00Z | |
| dc.date.available | 2020-09-07T14:50:00Z | |
| dc.identifier.uri | https://orfeo.belnet.be/handle/internal/7607 | |
| dc.description | Bad 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.language | eng | en_US |
| dc.publisher | IEEE | en_US |
| dc.title | Towards Detection of Road Weather Conditions using Large-Scale Vehicle Fleets | en_US |
| dc.type | Conference | en_US |
| dc.subject.frascati | Earth and related Environmental sciences | en_US |
| dc.audience | Scientific | en_US |
| dc.subject.free | Anomaly Detection, Vehicle Data, Can-Bus, Road Safety, Road Weather Conditions, Road Weather Models | en_US |
| dc.source.title | 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) | en_US |
| dc.relation.project | Flanders Innovation & Entrepreneurship (VLAIO), project nr. HBC.2017.0999 | en_US |
| Orfeo.peerreviewed | Yes | en_US |
| dc.identifier.doi | 10.1109/VTC2020-Spring48590.2020.9128484 |
