Abstract Fisheye cameras are widely used in various fields, including automotive contexts for $$360^{\circ }$$ 360 ∘ near-field vision around vehicles, as well as in photography, robotics, underwater imaging, and virtual reality. However, conventional image compression techniques do not take into account the specific characteristics of fisheye images, such as radial distortion and wide-angle field of view, especially when operating at low bit rates. This can lead to degradation of image quality and distortion of geometric features that are essential for computer vision (CV) applications such as object detection, semantic segmentation, and motion estimation. Recent studies have highlighted the impact of various noise factors on automotive camera sensors, the challenges of correcting radial lens distortion, and the effects of image compression artifacts on fisheye camera visual perception tasks. In this work, a comprehensive study of fisheye image compression and perception using deep learning-based techniques is conducted. It is demonstrated that deep learning-based techniques achieve better compression performance and perceptual quality than conventional techniques, particularly at low bitrates crucial for automotive applications.

With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. This review summarises deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving tasks where (D)RL methods have been employed, while addressing key computational challenges in real world deployment of autonomous driving agents. It also delineates adjacent domains such as behavior cloning, imitation learning, inverse reinforcement learning that are related but are not classical RL algorithms. The role of simulators in training agents, methods to validate, test and robustify existing solutions in RL are discussed.