Gerald Würsching joined the Cyber Physical Systems Group as a research assistant and PhD student under the supervision of Prof. Dr.-Ing. Matthias Althoff in 2020. He recieved his Bachelor of Science and Master of Science degrees in mechanical engineering from the Technical University of Munich in 2017 and 2020, respectively. His research interests include safe motion planning for autonomous vehicles.
Offered Thesis Topics
I am always looking for self-motivated students to solve interesting problems arising in my research areas. If you are interested in one of the currently available topics, simply send me an e-mail with your up-to-date CV and transcript of records attached.
Students from Informatics, Mechanical Engineering and Electrical Engineering are welcome to apply.
Currently available:
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Ongoing:
[BA] - Learning-Based Selection of Driving Corridors for Motion Planning
[BA] - RRT*-based Motion Planning for Autonomous Vehicles with STL Specifications
Würsching, Gerald; Althoff, Matthias: Robust and Efficient Curvilinear Coordinate Transformation with Guaranteed Map Coverage for Motion Planning. 2024 IEEE Intelligent Vehicles Symposium (IV), 2024 more…BibTeX
Würsching, Gerald; Mascetta, Tobias; Lin, Yuanfei; Althoff, Matthias: Simplifying Sim-to-Real Transfer in Autonomous Driving: Coupling Autoware with the CommonRoad Motion Planning Framework. 2024 IEEE Intelligent Vehicles Symposium (IV), 2024 more…BibTeX
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2022
Irani Liu, Edmond; Würsching, Gerald; Klischat Moritz; Althoff, Matthias: CommonRoad-Reach: A Toolbox for Reachability Analysis of Automated Vehicles. IEEE International Conference on Intelligent Transportation Systems (ITSC), 2022 more…BibTeX
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2021
Würsching, Gerald; Althoff, Matthias: Sampling-Based Optimal Trajectory Generation for Autonomous Vehicles Using Reachable Sets. IEEE International Conference on Intelligent Transportation Systems (ITSC), 2021 more…BibTeX
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Ye, Egon; Würsching, Gerald; Steyer, Sascha; Althoff, Matthias: Offline Dynamic Grid Generation for Automotive Environment Perception Using Temporal Inference Methods. IEEE Robotics and Automation Letters, 2021 more…BibTeX
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