Maximilian Geisslinger

According to our database, Maximilian Geisslinger authored at least 12 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Ethical Decision-Making for Self-Driving Vehicles: A Proposed Model & List of Value-Laden Terms that Warrant (Technical) Specification.

Franziska Poszler, Maximilian Geisslinger, Christoph Lütge

Sci. Eng. Ethics, October, 2024

2023

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge.

Johannes Betz, Tobias Betz, Felix Fent, Maximilian Geisslinger, Alexander Heilmeier, Leonhard Hermansdorfer, Thomas Herrmann, Sebastian Huch, Phillip Karle, Markus Lienkamp, Boris Lohmann, Felix Nobis, Levent Ögretmen, Matthias Rowold, Florian Sauerbeck, Tim Stahl, Rainer Trauth, Frederik Werner, Alexander Wischnewski

J. Field Robotics, June, 2023

An ethical trajectory planning algorithm for autonomous vehicles.

Maximilian Geisslinger, Franziska Poszler, Markus Lienkamp

Nat. Mac. Intell., February, 2023

EDGAR: An Autonomous Driving Research Platform - From Feature Development to Real-World Application.

Phillip Karle, Tobias Betz, Marcin Bosk, Felix Fent, Nils Gehrke, Maximilian Geisslinger, Luis Gressenbuch, Philipp Hafemann, Sebastian Huber, Maximilian Hübner, Sebastian Huch, Gemb Kaljavesi, Tobias Kerbl, Dominik Kulmer, Tobias Mascetta, Sebastian Maierhofer, Florian Pfab, Filip Rezabek, Esteban Rivera, Simon Sagmeister, Leander Seidlitz, Florian Sauerbeck, Ilir Tahiraj, Rainer Trauth, Nico Uhlemann, Gerald Würsching, Baha Zarrouki, Matthias Althoff, Johannes Betz, Klaus Bengler, Georg Carle, Frank Diermeyer, Jörg Ott, Markus Lienkamp

CoRR, 2023

MixNet: Physics Constrained Deep Neural Motion Prediction for Autonomous Racing.

Phillip Karle, Ferenc Török, Maximilian Geisslinger, Markus Lienkamp

IEEE Access, 2023

Learning and Adapting Behavior of Autonomous Vehicles through Inverse Reinforcement Learning.

Rainer Trauth, Marc Kaufeld, Maximilian Geisslinger, Johannes Betz

Proceedings of the IEEE Intelligent Vehicles Symposium, 2023

2022

Scenario Understanding and Motion Prediction for Autonomous Vehicles - Review and Comparison.

Phillip Karle, Maximilian Geisslinger, Johannes Betz, Markus Lienkamp

IEEE Trans. Intell. Transp. Syst., 2022

MixNet: Structured Deep Neural Motion Prediction for Autonomous Racing.

Phillip Karle, Ferenc Török, Maximilian Geisslinger, Markus Lienkamp

CoRR, 2022

Indy Autonomous Challenge - Autonomous Race Cars at the Handling Limits.

Alexander Wischnewski, Maximilian Geisslinger, Johannes Betz, Tobias Betz, Felix Fent, Alexander Heilmeier, Leonhard Hermansdorfer, Thomas Herrmann, Sebastian Huch, Phillip Karle, Felix Nobis, Levent Ögretmen, Matthias Rowold, Florian Sauerbeck, Tim Stahl, Rainer Trauth, Markus Lienkamp, Boris Lohmann

CoRR, 2022

2021

Watch-and-Learn-Net: Self-supervised Online Learning for Probabilistic Vehicle Trajectory Prediction.

Maximilian Geisslinger, Phillip Karle, Johannes Betz, Markus Lienkamp

Proceedings of the 2021 IEEE International Conference on Systems, Man, and Cybernetics, 2021

2019

A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection.

Felix Nobis, Maximilian Geisslinger, Markus Weber, Johannes Betz, Markus Lienkamp

Proceedings of the 2019 Sensor Data Fusion: Trends, Solutions, Applications, 2019

A Quasi-Steady-State Lap Time Simulation for Electrified Race Cars.

Alexander Heilmeier, Maximilian Geisslinger, Johannes Betz

Proceedings of the Fourteenth International Conference on Ecological Vehicles and Renewable Energies, 2019