  It has been well recognized that detecting drivable area is central to
self-driving cars. Most of existing methods attempt to locate road surface by
using lane line, thereby restricting to drivable area on which have a clear
lane mark. This paper proposes an unsupervised approach for detecting drivable
area utilizing both image data from a monocular camera and point cloud data
from a 3D-LIDAR scanner. Our approach locates initial drivable areas based on a
"direction ray map" obtained by image-LIDAR data fusion. Besides, a fusion of
the feature level is also applied for more robust performance. Once the initial
drivable areas are described by different features, the feature fusion problem
is formulated as a Markov network and a belief propagation algorithm is
developed to perform the model inference. Our approach is unsupervised and
avoids common hypothesis, yet gets state-of-the-art results on ROAD-KITTI
benchmark. Experiments show that our unsupervised approach is efficient and
robust for detecting drivable area for self-driving cars.
