| Umbrella: Uncertainty-aware model-based offline reinforcement learning leveraging planning C Diehl, T Sievernich, M Krüger, F Hoffmann, T Bertram arXiv preprint arXiv:2111.11097, 2021 | 22 | 2021 |
| Probabilistic lane change prediction using Gaussian process neural networks M Krüger, AS Novo, T Nattermann, T Bertram 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 3651-3656, 2019 | 21 | 2019 |
| Uncertainty-aware model-based offline reinforcement learning for automated driving C Diehl, TS Sievernich, M Krüger, F Hoffmann, T Bertram IEEE Robotics and Automation Letters 8 (2), 1167-1174, 2023 | 12 | 2023 |
| Interaction-aware trajectory prediction based on a 3D spatio-temporal tensor representation using convolutional–recurrent neural networks M Krüger, AS Novo, T Nattermann, T Bertram 2020 IEEE Intelligent Vehicles Symposium (IV), 1122-1127, 2020 | 11 | 2020 |
| Kinematic and dynamic description of non-standard snake-like locomotion systems C Behn, L Heinz, M Krüger Mechatronics 37, 1-11, 2016 | 8 | 2016 |
| Structural analysis of a neural network for lane change prediction for automated driving M Krueger, S Meuresch, AS Novo, T Nattermann, KH Glander, T Bertram 26. Workshop Computational Intelligence, 2016 | 6 | 2016 |
| Daten lane change prediction using neural networks considering classwise non-uniformly distributed data M Krueger, AS Novo, T Nattermann, KH Glander, T Bertram AmE 2018-Automotive meets Electronics; 9th GMM-Symposium, 1-6, 2018 | 5 | 2018 |
| Energy-based potential games for joint motion forecasting and control C Diehl, T Klosek, M Krueger, N Murzyn, T Osterburg, T Bertram 7th Annual Conference on Robot Learning, 2023 | 4 | 2023 |
| A review on scene prediction for automated driving A Stockem Novo, M Krüger, M Stolpe, T Bertram Physics 4 (1), 132-159, 2022 | 4 | 2022 |
| Reducing noise in label annotation: a lane change prediction case study M Krüger, AS Novo, T Nattermann, M Mohamed, T Bertram IFAC-PapersOnLine 52 (8), 221-226, 2019 | 4 | 2019 |
| Fahrspurerkennung mit Deep Learning für automatisierte Fahrfunktionen M Schmidt, C Lienke, M Oeljeklaus, M Krüger, T Nattermann, M Mohamed, ... Proceedings 28, 147-173, 2018 | 4 | 2018 |
| Reviewing 3d object detectors in the context of high-resolution 3+ 1d radar P Palmer, M Krueger, R Altendorfer, G Adam, T Bertram arXiv preprint arXiv:2308.05478, 2023 | 3 | 2023 |
| Differentiable constrained imitation learning for robot motion planning and control C Diehl, J Adamek, M Krüger, F Hoffmann, T Bertram arXiv preprint arXiv:2210.11796, 2022 | 3 | 2022 |
| On a Connection between Differential Games, Optimal Control, and Energy-based Models for Multi-Agent Interactions C Diehl, T Klosek, M Krüger, N Murzyn, T Bertram arXiv preprint arXiv:2308.16539, 2023 | 2 | 2023 |
| Fahrstreifenerkennung mit Deep Learning für automatisierte Fahrfunktionen M Schmidt, M Krüger, C Lienke, M Oeljeklaus, T Nattermann, M Mohamed, ... at-Automatisierungstechnik 67 (10), 866-878, 2019 | 2 | 2019 |
| Lane detection for automated driving using Deep Learning M Schmidt, M Krueger, C Lienke, M Oeljeklaus, T Nattermann, ... AT-AUTOMATISIERUNGSTECHNIK 67 (10), 866-878, 2019 | 2 | 2019 |
| Ego-Motion Correction based on Static Objects detected by an Automotive Lidar Sensor System N Stannartz, C Wissing, M Krueger, A Tolmidis, SI Ali, T Nattermann, ... AmE 2019-Automotive meets Electronics; 10th GMM-Symposium, 1-6, 2019 | 2 | 2019 |
| Ego-Motion Estimation and Dynamic Motion Separation from 3D Point Clouds for Accumulating Data and Improving 3D Object Detection P Palmer, M Krueger, R Altendorfer, T Bertram AmE 2023–Automotive meets Electronics; 14. GMM Symposium, 86-91, 2023 | 1 | 2023 |
| Recognition Beyond Perception: Environmental Model Completion by Reasoning for Occluded Vehicles M Krueger, P Palmer, C Diehl, T Osterburg, T Bertram IEEE Robotics and Automation Letters 7 (4), 10999-11006, 2022 | 1 | 2022 |
| Method and controller for controlling a motor vehicle T Nattermann, AS Novo, M Krüger US Patent App. 18/014,528, 2023 | | 2023 |
