| Parting with misconceptions about learning-based vehicle motion planning D Dauner, M Hallgarten, A Geiger, K Chitta Conference on Robot Learning, 1268-1281, 2023 | 67 | 2023 |
| From prediction to planning with goal conditioned lane graph traversals M Hallgarten, M Stoll, A Zell 2023 IEEE 26th International Conference on Intelligent Transportation …, 2023 | 20 | 2023 |
| Rethinking integration of prediction and planning in deep learning-based automated driving systems: a review S Hagedorn, M Hallgarten, M Stoll, A Condurache arXiv preprint arXiv:2308.05731, 2023 | 15 | 2023 |
| Stay on track: A frenet wrapper to overcome off-road trajectories in vehicle motion prediction M Hallgarten, I Kisa, M Stoll, A Zell 2024 IEEE Intelligent Vehicles Symposium (IV), 795-802, 2024 | 7 | 2024 |
| Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios? M Hallgarten, J Zapata, M Stoll, K Renz, A Zell arXiv preprint arXiv:2404.07569, 2024 | 3 | 2024 |
| Conditional unscented autoencoders for trajectory prediction F Janjoš, M Hallgarten, A Knittel, M Dolgov, A Zell, JM Zöllner arXiv preprint arXiv:2310.19944, 2023 | 3 | 2023 |
| NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking D Dauner, M Hallgarten, T Li, X Weng, Z Huang, Z Yang, H Li, ... arXiv preprint arXiv:2406.15349, 2024 | 1 | 2024 |
| The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review S Hagedorn, M Hallgarten, M Stoll, AP Condurache IEEE Transactions on Intelligent Vehicles, 2024 | | 2024 |
| Computer-implemented method for behavior planning of an at least partially automated ego vehicle with a specified navigation destination M Hallgarten, M Stoll US Patent App. 18/406,737, 2024 | | 2024 |
| Supplementary Material for Stay on Track: A Frenet Wrapper to Overcome Off-road Trajectories in Vehicle Motion Prediction M Hallgarten, I Kisa, M Stoll, A Zell | | |
| Predictive Driver Model: A Technical Report D Dauner, M Hallgarten, A Geiger, K Chitta | | |
| Supplementary Material for Parting with Misconceptions about Learning-based Vehicle Motion Planning D Dauner, M Hallgarten, A Geiger, K Chitta | | |
