Selective attention for detection and tracking of road-networks in autonomous driving

This paper deals with selective attention for the detection and tracking of road-networks for autonomous driving while utilizing a limited field of view sensor mounted on a fast camera platform with limited dynamics. While a previous paper derived an uncertainty cost function to determine where to look when, this paper introduces dynamic sensor constraints and examines the trade-off between a wish to perform frequent saccades on one hand and limiting factors like information loss due to saccadic motion blurr and time required at the new view direction to gain information on the other hand. A variety of those effects is examined and a new cost function is proposed to dynamically select platform orientations promising to minimize information theoretic uncertainty related to objects and road elements of interest required for autonomous driving. The method works within the 100ms cycle time aboard our autonomous vehicle MuCAR-3.