Marcel Hallgarten
Background
Since 2021
PhD student at the Department of Cognitive Systems, University of Tübingen
2019 - 2021
MSc in Mechanical Engineering, Karlsruhe Institute of Technology
2015 - 2019
BSc in Mechanical Engineering, Karlsruhe Institute of Technology
Research Interests
- Machine Learning
- Behavior Planning for Autonomous Driving
- Motion Planning for Autonomous Driving
- Deep Neural Networks
Teaching
- Lecutre: Introduction to Neural Networks (Summer 2023)
- Seminar: Proseminar: Topics in Deep Neural Networks (Winter 2022/23)
- Seminar: Proseminar: Topics in Deep Neural Networks (Summer 2022)
- Seminar: Current Topics in Deep Neural Networks (Winter 2021/22)
Publications
| [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] |