Leveraging Artificial Intelligence and Fleet Sensor Data towards a Higher Resolution Road Weather Model

被引：0

作者：

Bogaerts, Toon ^{[1
]}

Watelet, Sylvain ^{[2
]}

De Bruyne, Niko ^{[3
]}

Thoen, Chris ^{[3
]}

Coopman, Tom ^{[4
]}

Van den Bergh, Joris ^{[2
]}

Reyniers, Maarten ^{[2
]}

Seynaeve, Dirck ^{[3
]}

Casteels, Wim ^{[1
,2
]}

Latre, Steven ^{[1
]}

Hellinckx, Peter ^{[1
]}

机构：

[1] Univ Antwerp IMEC, Fac Appl Engn, IDLab, Sint Pietersvliet 7, B-2000 Antwerp, Belgium

[2] Royal Meteorol Inst Belgium, Ringlaan 3, B-1180 Brussels, Belgium

[3] Verhaert Masters Innovat, Hogenakkerhoekstr 21, B-9150 Kruibeke, Belgium

[4] Inuits Open Source Innovators, Essensteenweg 31, B-2930 Brasschaat, Belgium

来源：

SENSORS | 2022年 / 22卷 / 07期

关键词：

vehicle data; smart sensors; artificial intelligence; machine learning; road safety; road weather conditions; road weather models; road weather services; nowcasting; weather warnings; SURFACE-TEMPERATURE;

D O I：

10.3390/s22072732

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

Road weather conditions such as ice, snow, or heavy rain can have a significant impact on driver safety. In this paper, we present an approach to continuously monitor the road conditions in real time by equipping a fleet of vehicles with sensors. Based on the observed conditions, a physical road weather model is used to forecast the conditions for the following hours. This can be used to deliver timely warnings to drivers about potentially dangerous road conditions. To optimally process the large data volumes, we show how artificial intelligence is used to (1) calibrate the sensor measurements and (2) to retrieve relevant weather information from camera images. The output of the road weather model is compared to forecasts at road weather station locations to validate the approach.

引用

页数：14

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