Bogaerts Toon, Watelet Sylvain, De Bruyne Niko, Thoen Chris, Coopman Tom, Van den Bergh Joris, Reyniers Maarten, Seynaeve Dirck, Casteels Wim, Latré Steven, Hellinckx Peter
IDLab-Faculty of Applied Engineering, University of Antwerp-IMEC, Sint-Pietersvliet 7, 2000 Antwerp, Belgium.
Royal Meteorological Institute of Belgium, Ringlaan 3, 1180 Brussels, Belgium.
Sensors (Basel). 2022 Apr 2;22(7):2732. doi: 10.3390/s22072732.
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.
道路天气状况，如结冰、下雪或暴雨，会对驾驶员安全产生重大影响。在本文中，我们提出了一种通过为一组车辆配备传感器来实时持续监测道路状况的方法。基于观测到的状况，使用物理道路天气模型来预测接下来几个小时的状况。这可用于向驾驶员及时发出有关潜在危险道路状况的警告。为了最佳地处理大量数据，我们展示了如何使用人工智能来（1）校准传感器测量值以及（2）从相机图像中检索相关天气信息。将道路天气模型的输出与道路气象站位置的预报进行比较，以验证该方法。
