  We introduce a recurrent neural network architecture for automated road
surface wetness detection from audio of tire-surface interaction. The
robustness of our approach is evaluated on 785,826 bins of audio that span an
extensive range of vehicle speeds, noises from the environment, road surface
types, and pavement conditions including international roughness index (IRI)
values from 25 in/mi to 1400 in/mi. The training and evaluation of the model
are performed on different roads to minimize the impact of environmental and
other external factors on the accuracy of the classification. We achieve an
unweighted average recall (UAR) of 93.2% across all vehicle speeds including 0
mph. The classifier still works at 0 mph because the discriminating signal is
present in the sound of other vehicles driving by.
