  We propose the use of deep neural networks (DNN) for solving the problem of
inferring the position and relevant properties of lanes of urban roads with
poor or absent horizontal signalization, in order to allow the operation of
autonomous cars in such situations. We take a segmentation approach to the
problem and use the Efficient Neural Network (ENet) DNN for segmenting LiDAR
remission grid maps into road maps. We represent road maps using what we called
road grid maps. Road grid maps are square matrixes and each element of these
matrixes represents a small square region of real-world space. The value of
each element is a code associated with the semantics of the road map. Our road
grid maps contain all information about the roads' lanes required for building
the Road Definition Data Files (RDDFs) that are necessary for the operation of
our autonomous car, IARA (Intelligent Autonomous Robotic Automobile). We have
built a dataset of tens of kilometers of manually marked road lanes and used
part of it to train ENet to segment road grid maps from remission grid maps.
After being trained, ENet achieved an average segmentation accuracy of 83.7%.
We have tested the use of inferred road grid maps in the real world using IARA
on a stretch of 3.7 km of urban roads and it has shown performance equivalent
to that of the previous IARA's subsystem that uses a manually generated RDDF.
