| [1] | Hallgarten, M., Stoll, M., & Zell, A. (2023, September). From prediction to planning with goal conditioned lane graph traversals. In 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) (pp. 951-958). IEEE [arxiv] |
| [2] | Hallgarten, M., Kisa, I., Stoll, M., & Zell, A. (2023). Stay on Track: A Frenet Wrapper to Overcome Off-road Trajectories in Vehicle Motion Prediction. arXiv preprint arXiv:2306.00605. [arxiv] |
| [3] | Hallgarten, M., Zapata, J., Stoll, M., Renz, K., & Zell, A. (2024). Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?. arXiv preprint arXiv:2404.07569. [arxiv] |
| [4] | Hagedorn, S., Hallgarten, M., Stoll, M., & Condurache, A. (2023). Rethinking integration of prediction and planning in deep learning-based automated driving systems: a review. arXiv preprint arXiv:2308.05731. [arxiv] |
| [5] | Dauner, D., Hallgarten, M., Geiger, A., & Chitta, K. (2023, December). Parting with misconceptions about learning-based vehicle motion planning. In Conference on Robot Learning (pp. 1268-1281). PMLR. [arxiv] |
| [6] | Janjoš, F., Hallgarten, M., Knittel, A., Dolgov, M., Zell, A., & Zöllner, J. M. (2023). Conditional Unscented Autoencoders for Trajectory Prediction. arXiv preprint arXiv:2310.19944. [arxiv] |
