Search Results for author: Oliver Scheel

Found 7 papers, 0 papers with code

Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients

no code implementations27 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.

SimNet: Learning Reactive Self-driving Simulations from Real-world Observations

no code implementations26 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.

What data do we need for training an AV motion planner?

no code implementations26 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.

Imitation Learning Motion Planning

Explicit Domain Adaptation with Loosely Coupled Samples

no code implementations24 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.

Autonomous Driving Domain Adaptation +3

Ambiguity in Sequential Data: Predicting Uncertain Futures with Recurrent Models

no code implementations10 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.

Time Series Analysis Trajectory Forecasting

Attention-based Lane Change Prediction

no code implementations4 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.

Situation Assessment for Planning Lane Changes: Combining Recurrent Models and Prediction

no code implementations17 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.

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