  Current self-driving car systems operate well in sunny weather but struggle
in adverse conditions. One of the most commonly encountered adverse conditions
involves water on the road caused by rain, sleet, melting snow or flooding.
While some advances have been made in using conventional RGB camera and LIDAR
technology for detecting water hazards, other sources of information such as
polarization offer a promising and potentially superior approach to this
problem in terms of performance and cost. In this paper, we present a novel
stereo-polarization system for detecting and tracking water hazards based on
polarization and color variation of reflected light, with consideration of the
effect of polarized light from sky as function of reflection and azimuth
angles. To evaluate this system, we present a new large `water on road'
datasets spanning approximately 2 km of driving in various on-road and off-road
conditions and demonstrate for the first time reliable water detection and
tracking over a wide range of realistic car driving water conditions using
polarized vision as the primary sensing modality. Our system successfully
detects water hazards up to more than 100m. Finally, we discuss several
interesting challenges and propose future research directions for further
improving robust autonomous car perception in hazardous wet conditions using
polarization sensors.
