Spatio-temporal perception nets

Abstract

State of the art approaches to autonomous systems face the challenge of sensor data fusion, abstraction, classification, and prediction of events. The trend is going towards the integration of more and more sensors into automation systems, which will reach a number of sensors comparable to the amount of sensory receptors in the human body in the not too distant future. While today's technical systems cannot cope with such a flood of information to be processed rapidly, these challenges are mastered exceptionally well by the human brain. Based on this observation, in prior work, a biologically inspired model for sensor data processing has been proposed [1]. This socalled neuro-symbolic information processing model is based on a functional model of the human perception system. Here, an extension of this concept to spatial and temporal aspects of perception is presented. The challenges for solving these tasks as well as the strategies to master these challenges based on perception-nets are presented.