Thomas Rottmann
Papers
1
Total Citations
5
H-Index
1
About
Thomas Rottmann is a researcher in control systems and autonomous vehicle coordination, with a focus on integrating optimization and dynamic control for safe, efficient traffic networks. His major contribution lies in developing a hierarchical control framework for interacting vehicles, combining model-predictive control (MPC) with generalized Nash equilibrium problems and dynamic inversion. This approach enables collision-free trajectory generation at a high level, while a low-level dynamic inversion controller ensures precise path tracking—bridging theoretical optimization with real-world vehicle dynamics. His most-cited work, "Control of interacting vehicles using model-predictive control, generalized Nash equilibrium problems, and dynamic inversion" (2020), has garnered 5 citations, reflecting its niche but growing impact in the field of multi-agent control. This research is notable for addressing the complex interplay between strategic decision-making and physical vehicle constraints, offering a scalable solution for future intelligent transportation systems. Rottmann’s work is particularly relevant for students and researchers exploring MPC, game-theoretic control, or autonomous driving, as it demonstrates a practical synthesis of advanced control theory and real-time implementation challenges.
Research Focus
Key Achievements
Top Papers
- 1