  Slippery road weather conditions are prevalent in many regions and cause a
regular risk for traffic. Still, there has been less research on how autonomous
vehicles could detect slippery driving conditions on the road to drive safely.
In this work, we propose a method to predict a dense grip map from the area in
front of the car, based on postprocessed multimodal sensor data. We trained a
convolutional neural network to predict pixelwise grip values from fused RGB
camera, thermal camera, and LiDAR reflectance images, based on weakly
supervised ground truth from an optical road weather sensor.
  The experiments show that it is possible to predict dense grip values with
good accuracy from the used data modalities as the produced grip map follows
both ground truth measurements and local weather conditions, such as snowy
areas on the road. The model using only the RGB camera or LiDAR reflectance
modality provided good baseline results for grip prediction accuracy while
using models fusing the RGB camera, thermal camera, and LiDAR modalities
improved the grip predictions significantly.
