Wavelet Transform for Analyzing Fog Visibility
IEEE Intelligent Systems
Using Robust Estimation Algorithms for Tracking Explicit Curves
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Automatic fog detection and estimation of visibility distance through use of an onboard camera
Machine Vision and Applications
Estimation of the Visibility Distance by Stereovision: A Generic Approach
IEICE - Transactions on Information and Systems
Automatic Detection of Adverse Weather Conditions in Traffic Scenes
AVSS '08 Proceedings of the 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance
Real-time disparity contrast combination for onboard estimation of the visibility distance
IEEE Transactions on Intelligent Transportation Systems
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Fog is a local meteorological phenomena which drastically reduces the visibility range. Fog detection and visibility range estimation are critical tasks for road operators who need to warn the drivers and advise them on speed reductions. To achieve this task, fixed sensors are quite accurate but they have a reduced spatial cover. Mobile sensors are less accurate, but they have a good spatial cover. Based on the combination of roadside sensors and in-vehicle devices (sensors or fog lamps), a data fusion framework is presented aiming at taking the advantages of both fixed and mobile sensors for the extensive detection and estimation of the fog density. The proposed solution is implemented by means of a local dynamic map fed by vehicle to infrastructure (V2I) communication, which gives a coherent view of the road environment.