  This paper proposes a spatiotemporal architecture with a deep neural network
(DNN) for road surface conditions and types classification using LiDAR. It is
known that LiDAR provides information on the reflectivity and number of point
clouds depending on a road surface. Thus, this paper utilizes the information
to classify the road surface. We divided the front road area into four
subregions. First, we constructed feature vectors using each subregion's
reflectivity, number of point clouds, and in-vehicle information. Second, the
DNN classifies road surface conditions and types for each subregion. Finally,
the output of the DNN feeds into the spatiotemporal process to make the final
classification reflecting vehicle speed and probability given by the outcomes
of softmax functions of the DNN output layer. To validate the effectiveness of
the proposed method, we performed a comparative study with five other
algorithms. With the proposed DNN, we obtained the highest accuracy of 98.0\%
and 98.6\% for two subregions near the vehicle. In addition, we implemented the
proposed method on the Jetson TX2 board to confirm that it is applicable in
real-time.

Image and Video Processing