Stefan Hoermann — accepted AI-conference papers

2019

Long-Term Occupancy Grid Prediction Using Recurrent Neural Networks

Marcel Schreiber, Stefan Hoermann, Klaus Dietmayer

ICRA 2019poster

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 eye view of the scene, including occupancy and velocity, is fed as a sequence to a RNN which is trained to predict futu…

Cited by 100 Source ↗Scholar ↗

2018

Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling

Stefan Hoermann, Martin Bach, Klaus Dietmayer

ICRA 2018poster

Long-term situation prediction plays a crucial role for intelligent vehicles. A major challenge still to overcome is the prediction of complex downtown scenarios with multiple road users, e.g., pedestrians, bikes, and motor vehicles, interacting with each other. This contribution tackles this challe…

Cited by 223 Source ↗Scholar ↗