Modeling unstructured environments with dynamic persistence grids and object delimiters in urban traffic scenarios

Abstract

Modeling dynamic environments is an essential research topic in any driving assistance system. The complexity of the surrounding world, the measurement uncertainties or the unpredictable behavior of the traffic participants are the main factors that influence the detection and tracking process. In this paper we present a vision-based method for modeling and tracking unstructured dynamic environments. The proposed solution relies on raw information provided by a classified grid computed from a digital elevation map and employs two separate representation levels: a local dynamic persistence grid (DyPerGrid) that is generated as an intermediate representation level and a map of delimiters as a higher level obstacle description. A fast tracking solution is proposed by using the two models. The result is a geometrically consistent and accurate representation of the dynamic environment.