1 code implementation • 26 May 2021 • Luca Bergamini, Yawei Ye, Oliver Scheel, Long Chen, Chih Hu, Luca Del Pero, Blazej Osinski, Hugo Grimmett, Peter Ondruska
We train our system directly from 1, 000 hours of driving logs and measure both realism, reactivity of the simulation as the two key properties of the simulation.
no code implementations • 17 May 2018 • Oliver Scheel, Loren Schwarz, Nassir Navab, Federico Tombari
One of the greatest challenges towards fully autonomous cars is the understanding of complex and dynamic scenes.
no code implementations • 4 Mar 2019 • Oliver Scheel, Naveen Shankar Nagaraja, Loren Schwarz, Nassir Navab, Federico Tombari
Lane change prediction of surrounding vehicles is a key building block of path planning.
no code implementations • 10 Mar 2020 • Alessandro Berlati, Oliver Scheel, Luigi Di Stefano, Federico Tombari
Ambiguity is inherently present in many machine learning tasks, but especially for sequential models seldom accounted for, as most only output a single prediction.
no code implementations • 24 Apr 2020 • Oliver Scheel, Loren Schwarz, Nassir Navab, Federico Tombari
In this work we propose a transfer learning framework, core of which is learning an explicit mapping between domains.
no code implementations • 26 May 2021 • Long Chen, Lukas Platinsky, Stefanie Speichert, Blazej Osinski, Oliver Scheel, Yawei Ye, Hugo Grimmett, Luca Del Pero, Peter Ondruska
If cheaper sensors could be used for collection instead, data availability would go up, which is crucial in a field where data volume requirements are large and availability is small.
no code implementations • 27 Sep 2021 • Oliver Scheel, Luca Bergamini, Maciej Wołczyk, Błażej Osiński, Peter Ondruska
In this work we are the first to present an offline policy gradient method for learning imitative policies for complex urban driving from a large corpus of real-world demonstrations.