| Benchmarking behavior prediction models in gap acceptance scenarios JF Schumann, J Kober, A Zgonnikov IEEE Transactions on Intelligent Vehicles, 2023 | 9 | 2023 |
| Using models based on cognitive theory to predict human behavior in traffic: A case study JF Schumann, AR Srinivasan, J Kober, G Markkula, A Zgonnikov 2023 IEEE 26th International Conference on Intelligent Transportation …, 2023 | 5 | 2023 |
| The COMMOTIONS Urban Interactions Driving Simulator Study Dataset AR Srinivasan, J Schumann, Y Wang, YS Lin, M Daly, A Solernou, ... arXiv preprint arXiv:2305.11909, 2023 | 3 | 2023 |
| Robust Multi-Modal Density Estimation A Mészáros, JF Schumann, J Alonso-Mora, A Zgonnikov, J Kober arXiv preprint arXiv:2401.10566, 2024 | 1 | 2024 |
| Smooth-Trajectron++: Augmenting the Trajectron++ behaviour prediction model with smooth attention FSB Westerhout, JF Schumann, A Zgonnikov 2023 IEEE 26th International Conference on Intelligent Transportation …, 2023 | 1 | 2023 |
| Benchmark for models predicting human behavior in gap acceptance scenarios JF Schumann, J Kober, A Zgonnikov arXiv preprint arXiv:2211.05455, 2022 | 1 | 2022 |
| A survey on robustness in trajectory prediction for autonomous vehicles J Hagenus, FB Mathiesen, JF Schumann, A Zgonnikov arXiv preprint arXiv:2402.01397, 2024 | | 2024 |
| The COMMOTIONS Urban Interactions Driving Simulator Study Dataset A Ramakrishnan Srinivasan, J Schumann, Y Wang, YS Lin, M Daly, ... arXiv e-prints, arXiv: 2305.11909, 2023 | | 2023 |
| A machine learning approach for fighting the curse of dimensionality in global optimization JF Schumann, AM Aragón arXiv preprint arXiv:2110.14985, 2021 | | 2021 |
| Fighting the curse of dimensionality: A machine learning approach to finding global optima. JF Schumann, AM Aragón arXiv preprint arXiv:2110.14985, 2021 | | 2021 |
