Christopher Diehl
E-Mail: christopher.diehl@tu-dortmund.de
Forschungsschwerpunkte:
- Lernbasierte Entscheidungsfindung und Situationsprädiktion für das automatisierte Fahren
- Keywords: Reinforcement Learning, Inverse Reinforcement Learning, Imitation Learning, Game-Theory, Optimal Control
Weitere Tätigkeiten
- WiSe: Betreuung der Vorlesung Automated Driving
- SoSe: Betreuung der Vorlesung Learning in Robotics, Mehrgrößensysteme und Optimale Regelung
Zeitschriften- und Buchbeiträge:
2023
- Diehl, C., T. Sievernich, M. Krüger, F. Hoffmann, T. Bertram: ”Uncertainty‑Aware Model‑Based Offline Reinforcement Learning for Automated Driving”, IEEE Robotics and Automation Letters (RA-L), 2023
2022
- Krüger, M., P. Palmer, C. Diehl, T. Osterburg, T. Bertram: ”Recognition Beyond Perception: Environmental Model Completion by Reasoning for Occluded Vehicles”, IEEE Robotics and Automation Letters (RA-L), 2022.
2021
- Diehl, C., N.Stannartz, T. Bertram: Navigation with Uncertain Map Data for Automated Vehicles
Springer Fachmedien Wiesbaden
Konferenzbeiträge:
2023
- Diehl, C., J. Adamek, M. Krueger, F. Hoffmann und T. Bertram: “Differentiable Constrained Imitation Learning for Robot Motion Planning and Control”, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) - Workshop on Traffic Agent Modeling for Autonomous Driving Simulation, Detroit, USA, Oktober 2023
- Diehl, C., T. Klosek, M. Krueger, N. Murzyn, T. Osterburg, T. Bertram: „Energy-based Potential Games for Joint Motion Forecasting and Control“, Conference on Robot Learning (CoRL), Atlanta, USA, November 2023
- Osterburg, T., C. Diehl, T. Bertram: ”Social Behavior Prediction for Automated Vehicles Using Contrastive Learning”, IFToMM D‑A‑CH, 2023 (Accepted)
2022
- Stockem Novo, A.S., Marco Stolpe, Christopher Diehl, Timo Osterburg, Torsten Bertram, Vijay Parsi,Nils Murzyne, Mualla, Georg,Schneider, and Philipp Töws:"Mid-term status report on KISSaF: AI-based situation interpretation for automated driving" AmE 2022 - Automotive meets Electronics, 13th GMM-Symposium September 2022
- Diehl, C., A. Makarow, C. Rösmann, T. Bertram: ”Time‑Optimal Nonlinear Model Predictive Control for Radar‑based Automated Parking”, IFAC Symposium on Intelligent Autonomous Vehicles, 2022.
- Diehl, C., T. Osterburg, N. Murzyn, G. Schneider, F. Hoffmann, T. Bertram: ”Conditional Behavior Prediction for Automated Driving on Highways”, Proc. 32. Workshop Computational Intelligence (CIW), 2022.
- Novo, A.S., M. Stolpe, C. Diehl, T. Osterburg, T. Bertram, V. Parsi, N. Murzyn, F. Mualla, G. Schneider, P. Töws: ”Mid‑term status report on KISSaF: AI‑based Situation Interpretation for Automated Driving”, Automotive meets Electronics (AME), 2022
2021
- Diehl, C., T. Waldeyer, F. Hoffmann, T. Bertram: VectorRL: Interpretable Graph-based Reinforcement Learning for Automated Driving, Proc. 31. Workshop Computational Intelligence
Berlin, 25.-26.11.2021 - Diehl, C., T. Sievernich, M. Krüger, F. Hoffmann, T. Bertam: UMBRELLA: Uncertainty-Aware Model-Based Offline Reinforcement Learning Leveraging Planning, Advances in Neural Information Processing Systems - Machine Learning for Autonomous Driving Workshop (NeuRIPS 2021 ML4AD)
2021
2020
- Diehl, C., E. Feicho, A. Schwambach, T. Dammeier, T. Bertram: Radar-based Dynamic Occupancy Grid Mapping, 23nd IEEE International Conference on Intelligent Transportation Systems (ITSC), Rhodos, Greece, 20-23.09.2020 (accepted)
September 2020
Diplom-/Bachelor-/ Masterarbeiten:
2020
- C. Wunsch
Pfadplanung auf Basis von unpräzisen externen Karteninformationen und lokalen Sensordaten
Betreuung: C. Diehl, N. Stannartz, T. Bertram