  Road detection and segmentation is a crucial task in computer vision for safe
autonomous driving. With this in mind, a new net architecture (3D-DEEP) and its
end-to-end training methodology for CNN-based semantic segmentation are
described along this paper for. The method relies on disparity filtered and
LiDAR projected images for three-dimensional information and image feature
extraction through fully convolutional networks architectures. The developed
models were trained and validated over Cityscapes dataset using just fine
annotation examples with 19 different training classes, and over KITTI road
dataset. 72.32% mean intersection over union(mIoU) has been obtained for the 19
Cityscapes training classes using the validation images. On the other hand,
over KITTIdataset the model has achieved an F1 error value of 97.85%
invalidation and 96.02% using the test images.
