We present a stereo-based dense mapping algorithm for large-scale dynamic urban
environments. In contrast to other existing methods, we simultaneously reconstruct the static …

Driving environment perception technology is a key issue for driving assistance systems and
autonomous vehicles. Driving environment information collection and information fusion and …

In an autonomous vehicle setting, we propose a method for the estimation of a semantic
grid, ie a bird's eye grid centered on the car's position and aligned with its driving direction …

Accurately estimating the current state of local traffic scenes is one of the key problems in the
development of software components for automated vehicles. In addition to details on free …

We present a generic evidential grid mapping pipeline designed for imaging sensors such
as LiDAR and cameras. Our grid-based evidential model contains semantic estimates for …

Accurately estimating the current state of local traffic scenes is a crucial component of
automated vehicles. The desired representation may include static and dynamic traffic …

In the course of the development and integration of the autonomous driving the knowledge
about the current environment and especially the road is one of the basic requirements to …

Uncertainty about urban environments stems not only from imprecise pose estimation and
noisy information in images but also from the lack of semantic information. This article …

We present a generic evidential grid mapping pipeline designed for imaging sensors such
as LiDARs and cameras. Our grid-based evidential model contains semantic estimates for …

The knowledge about the local environment is of utmost importance for all robotics and
autonomous driving applications. Currently this information is often extracted from high …