For autonomous driving to become a reality, future sensor systems must be able to not only capture the vehicle's environment, but also to provide semantic information. In this work, deep learning methods, meant to enhance-or even replace-the classical radar signal processing chain, are developed and evaluated in the context of automotive applications. For this purpose, state of the art computer vision approaches are adapted and applied to radar signals in order to detect and classify different road users.
The present research aimed to analyze the main experiences collected in the mainstream literature about social knowledge and its management. For this, Scopus, Wos and LISA were searched, as well as Google Scholar; A set of good practices and considerations was identified that are analyzed in this work. As a fundamental result, the concept of social knowledge and its role in society are clarified, as it is the main asset of the Knowledge Society. The experience of a network that has a thematic domain provided with information, interaction and resources for the optimal and systematic development of activities is presented. It is characterized with positive attributes; it is distinguished by leadership and culture; technology constitutes its main limitation and demands a greater use of the synergy of its components, so the evaluation of the processes to identify, value and use knowledge accordingly and its management in search of better results constitutes an effective way for its subsequent development and improvement.
