326 citations · 334 across the 4 of their papers we have counts for
3 papers · 1 filter
Long-Term Occupancy Grid Prediction Using Recurrent Neural Networks
Marcel Schreiber, Stefan Hoermann, Klaus Dietmayer
We tackle the long-term prediction of scene evolution in a complex downtown scenario for automated driving based on Lidar grid fusion and recurrent neural networks (RNNs). A bird's…
Deep Object Tracking on Dynamic Occupancy Grid Maps Using RNNs
Nico Engel, Stefan Hoermann, Philipp Henzler +1
The comprehensive representation and understanding of the driving environment is crucial to improve the safety and reliability of autonomous vehicles. In this paper, we present a n…
Offline Object Extraction from Dynamic Occupancy Grid Map Sequences
Daniel Stumper, Fabian Gies, Stefan Hoermann +1
A dynamic occupancy grid map (DOGMa) allows a fast, robust, and complete environment representation for automated vehicles. Dynamic objects in a DOGMa, however, are commonly repres…