  This work proposes a perception system for autonomous vehicles and advanced
driver assistance specialized on unpaved roads and off-road environments. In
this research, the authors have investigated the behavior of Deep Learning
algorithms applied to semantic segmentation of off-road environments and
unpaved roads under differents adverse conditions of visibility. Almost 12,000
images of different unpaved and off-road environments were collected and
labeled. It was assembled an off-road proving ground exclusively for its
development. The proposed dataset also contains many adverse situations such as
rain, dust, and low light. To develop the system, we have used convolutional
neural networks trained to segment obstacles and areas where the car can pass
through. We developed a Configurable Modular Segmentation Network (CMSNet)
framework to help create different architectures arrangements and test them on
the proposed dataset. Besides, we also have ported some CMSNet configurations
by removing and fusing many layers using TensorRT, C++, and CUDA to achieve
embedded real-time inference and allow field tests. The main contributions of
this work are: a new dataset for unpaved roads and off-roads environments
containing many adverse conditions such as night, rain, and dust; a CMSNet
framework; an investigation regarding the feasibility of applying deep learning
to detect region where the vehicle can pass through when there is no clear
boundary of the track; a study of how our proposed segmentation algorithms
behave in different severity levels of visibility impairment; and an evaluation
of field tests carried out with semantic segmentation architectures ported for
real-time inference.
