Phillip Karle, M. Sc.

e-mail phillip.karle@tum.de
room MW 3505
phone +49.89.289.15898
fax +49.89.289.15357
media ORCID | ResearchGate | LinkedIn 

Projects

EDGAR

BFS-DAD

Student Projects

If you are interested in a student research project in the areas of autonomous driving, scenario understanding and motion prediction, you are welcome to apply to me intiatively in addition to the advertised projects. Please send me an email with a short motivation, curriculum vitae and a current performance record.

 

Publications

  • Betz, Johannes; Betz, Tobias; Fent, Felix; Geisslinger, Maximilian; Heilmeier, Alexander; Hermansdorfer, Leonhard; Herrmann, Thomas; Huch, Sebastian; Karle, Phillip; Lienkamp, Markus; Lohmann, Boris; Nobis, Felix; Ögretmen, Levent; Rowold, Matthias; Sauerbeck, Florian; Stahl, Tim; Trauth, Rainer; Werner, Frederik; Wischnewski, Alexander: TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge. Journal of Field Robotics, 2023 more…
  • Betz, Tobias; Karle, Phillip; Werner, Frederik; Betz, Johannes: An Analysis of Software Latency for a High-Speed Autonomous Race Car—A Case Study in the Indy Autonomous Challenge. SAE International Journal of Connected and Automated Vehicles 6 (3), 2023 more…
  • Wischnewski, Alexander; Geisslinger, Maximilian; Betz, Johannes; Betz, Tobias; Fent, Felix; Heilmeier, Alexander; Hermansdorfer, Leonhard; Herrmann, Thomas; Huch, Sebastian; Karle, Phillip; Nobis, Felix; Ögretmen, Levent; Rowold, Matthias; Sauerbeck, Florian; Stahl, Tim; Trauth, Rainer; Lienkamp, Markus; Lohmann, Boris: Indy Autonomous Challenge - Autonomous Race Cars at the Handling Limits. In: Proceedings. Springer Berlin Heidelberg, 2022 more…
  • Karle, Phillip; Geisslinger, Maximilian; Betz, Johannes; Lienkamp, Markus: Scenario Understanding and Motion Prediction for Autonomous Vehicles - Review and Comparison. IEEE Transactions on Intelligent Transportation Systems, 2022, 1-21 more…
  • Geisslinger, Maximilian; Karle, Phillip; Betz, Johannes; Lienkamp, Markus: Watch-and-Learn-Net: Self-supervised Online Learning for Probabilistic Vehicle Trajectory Prediction. 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, 2021 more…