Publications

Christopher Diehl, Tobias Klosek, Martin Krüger, Nils Murzyn, Timo Osterburg, Torsten Bertram (2023). Energy-based Potential Games for Joint Motion Forecasting and Control. Conference on Robot Learning (CoRL), 2023.

Christopher Diehl, Janis Adamek, Martin Krüger, Frank Hoffmann, Torsten Bertram (2023). Differentiable Constrained Imitation Learning for Robot Motion Planning and Control. IEEE International Conference on Intelligent Robots and Systems Agent4AD Workshop (IROS), 2023.

Christopher Diehl, Tobias Klosek, Martin Krüger, Nils Murzyn, Torsten Bertram (2023). On a Connection between Differential Games, Optimal Control, and Energy-based Models for Multi-Agent Interactions. International Conference on Machine Learning (ICML) - New Frontiers in Learning, Control, and Dynamical Systems Workshop, 2023.

Christopher Diehl, Timo Sebastian Sievernich, Martin Krüger, Frank Hoffmann, Torsten Bertram (2023). Uncertainty-Aware Model-Based Offline Reinforcement Learning for Automated Driving. IEEE Robotics and Automation Letters (RA-L), International Conference on Intelligent Robots and Systems (IROS), 2023.

Timo Osterburg, Christopher Diehl, Torsten Bertram (2023). Social Behavior Prediction for Automated Vehicles Using Contrastive Learning. IFToMM D‑A‑CH, 2023.

Christopher Diehl, Timo Osterburg, Nils Murzyn, Georg Schneider, Frank Hoffmann, Torsten Bertram (2022). Conditional Behavior Prediction for Automated Driving on Highways. Proc. 32. Workshop Computational Intelligence. 2022 Best Paper Award.

Martin Krüger, Patrick Palmer, Christopher Diehl, Timo Osterburg, Torsten Bertram (2022). Beyond Perception: Environmental Model Completion by Reasoning for Occluded Vehicles. IEEE Robotics and Automation Letters (RA‑L)/ International Conference on Intelligent Robots and Systems (IROS), 2022.

Christopher Diehl, Artemi Makarow, Christoph Rösmann, Torsten Bertram (2022). Time‑Optimal Nonlinear Model Predictive Control for Radar‑based Automated Parking. IFAC Symposium on Intelligent Autonomous Vehicles (IAV), 2022.

Christopher Diehl, Timo Sievernich, Martin Krüger, Frank Hoffmann, Torsten Bertram (2021). UMBRELLA: Uncertainty-Aware Model-Based Offline Reinforcement Learning Leveraging Planning. Neural Information Processing Systems 34 - Machine Learning for Autonomous Driving Workshop (NeurIPS-ML4AD), Best Paper Award.

Christopher Diehl, Tamino Waldeyer, Frank Hoffmann, Torsten Bertram (2021). VectorRL: Interpretable Graph‑based Reinforcement Learning for Automated Driving. Proc. 31. Workshop Computational Intelligence, 2021.

Christopher Diehl, Niklas Stannartz, Torsten Bertram (2021). Navigation with Uncertain Map Data for Automated Vehicles. Automated Driving, 2021.

Christopher Diehl, Eduard Feicho, Alexander Schwambach, Thomas Dammeier, Eric Mares Torsten Bertram (2020). Radar‑based Dynamic Occupancy Grid Mapping and Object Detection. IEEE International Conference on Intelligent Transportation Systems (ITSC), 2020.