| Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance R Trumpp, H Bayerlein, D Gesbert 2022 IEEE Intelligent Vehicles Symposium (IV), 331-336, 2022 | 18 | 2022 |
| Unifying f1tenth autonomous racing: Survey, methods and benchmarks BD Evans, R Trumpp, M Caccamo, F Jahncke, J Betz, HW Jordaan, ... arXiv preprint arXiv:2402.18558, 2024 | 7 | 2024 |
| Residual Policy Learning for Vehicle Control of Autonomous Racing Cars R Trumpp, D Hoornaert, M Caccamo 2023 IEEE Intelligent Vehicles Symposium (IV), 1-6, 2023 | 7 | 2023 |
| Efficient Learning of Urban Driving Policies Using Bird's-Eye-View State Representations R Trumpp, M Büchner, A Valada, M Caccamo 2023 IEEE 26th International Conference on Intelligent Transportation …, 2023 | 6 | 2023 |
| RaceMOP: Mapless online path planning for multi-agent autonomous racing using residual policy learning R Trumpp, E Javanmardi, J Nakazato, M Tsukada, M Caccamo 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024 | 1 | 2024 |
| Learning to Generate All Feasible Actions M Theile, D Bernardini, R Trumpp, C Piazza, M Caccamo, ... IEEE Access, 2024 | | 2024 |
| Implementierung des Poly-reference Least Square Complex Frequency (p-LSCF) Algorithmus zur Operational Modal Analysis (OMA) RF Trumpp | | 2017 |
