Evolving adaptive, high-dimensional, camera-based speed sensors

This paper reviews some attempts that exploit a phenomenon, also known as motion parallax, to estimate the distance of closest approach of a moving object. Despite their success, the existing evolutionary methods lack some desirable properties, such as reasonable scalability and online learning. To overcome these practically-relevant limitations, this paper proposes a new model that is based on Hebbian learning. Due to its scalability and online learning capabilities, this model is especially suited to mobile robots.