  Perception of the environment is a critical component for enabling autonomous
driving. It provides the vehicle with the ability to comprehend its
surroundings and make informed decisions. Depth prediction plays a pivotal role
in this process, as it helps the understanding of the geometry and motion of
the environment. This thesis focuses on the challenge of depth prediction using
monocular self-supervised learning techniques. The problem is approached from a
broader perspective first, exploring conditional generative adversarial
networks (cGANs) as a potential technique to achieve better generalization was
performed. In doing so, a fundamental contribution to the conditional GANs, the
acontrario cGAN was proposed. The second contribution entails a single
image-to-depth self-supervised method, proposing a solution for the rigid-scene
assumption using a novel transformer-based method that outputs a pose for each
dynamic object. The third significant aspect involves the introduction of a
video-to-depth map forecasting approach. This method serves as an extension of
self-supervised techniques to predict future depths. This involves the creation
of a novel transformer model capable of predicting the future depth of a given
scene. Moreover, the various limitations of the aforementioned methods were
addressed and a video-to-video depth maps model was proposed. This model
leverages the spatio-temporal consistency of the input and output sequence to
predict a more accurate depth sequence output. These methods have significant
applications in autonomous driving (AD) and advanced driver assistance systems
(ADAS).
