Marcel Hallgarten

Background

2021 - 2025
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

 

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. (2024, June). Stay on track: A frenet wrapper to overcome off-road trajectories in vehicle motion prediction. In 2024 IEEE Intelligent Vehicles Symposium (IV) (pp. 795-802). IEEE. [arxiv]
[3]
Hallgarten, M., Zapata, J., Stoll, M., Renz, K., & Zell, A. (2024, October). Can vehicle motion planning generalize to realistic long-tail scenarios?. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 5388-5395). IEEE. [arxiv]
[4]
Hagedorn, S., Hallgarten, M., Stoll, M., & Condurache, A. P. (2024). The integration of prediction and planning in deep learning automated driving systems: A review. IEEE Transactions on Intelligent Vehicles. [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]
[7]
Dauner, D., Hallgarten, M., Li, T., Weng, X., Huang, Z., Yang, Z., ... & Chitta, K. (2024). Navsim: Data-driven non-reactive autonomous vehicle simulation and benchmarking. Advances in Neural Information Processing Systems, 37, 28706-28719. [arxiv]