  In this work, a novel learning-based approach has been developed to generate
driving paths by integrating LIDAR point clouds, GPS-IMU information, and
Google driving directions. The system is based on a fully convolutional neural
network that jointly learns to carry out perception and path generation from
real-world driving sequences and that is trained using automatically generated
training examples. Several combinations of input data were tested in order to
assess the performance gain provided by specific information modalities. The
fully convolutional neural network trained using all the available sensors
together with driving directions achieved the best MaxF score of 88.13% when
considering a region of interest of 60x60 meters. By considering a smaller
region of interest, the agreement between predicted paths and ground-truth
increased to 92.60%. The positive results obtained in this work indicate that
the proposed system may help fill the gap between low-level scene parsing and
behavior-reflex approaches by generating outputs that are close to vehicle
control and at the same time human-interpretable.
