Daniel Baumgartner
Papers
3
Total Citations
37
H-Index
3
About
Daniel Baumgartner is a researcher at the German Aerospace Center (DLR) Institute of System Dynamics and Control, where his work centers on autonomous vehicle navigation, AI-based control methods, and automotive safety systems. His most impactful contribution, "Reactive Obstacle Avoidance for Highly Maneuverable Vehicles Based on a Two-Stage Optical Flow Clustering" (2016, 30 citations), introduces a novel approach that uses only monocular camera data to detect and avoid dynamic obstacles. By clustering optical flow and applying epipolar geometry, the method generates velocity commands in real time—a significant step toward vision-only autonomous driving. Baumgartner is also the driving force behind the AI-For-Mobility (AFM) research platform (2023), a production hybrid vehicle retrofitted to test AI-based control strategies at DLR. This platform bridges theoretical AI research and practical automotive applications, enabling advances in safety, comfort, and energy efficiency. His overview of automotive control research at DLR (2020) further highlights his role in shaping the institute’s agenda. With a focus on real-world implementation and a growing citation footprint, Baumgartner’s work is essential reading for anyone interested in the intersection of computer vision, AI, and vehicle dynamics.
Research Focus
Key Achievements
Top Papers
- 1
- 2AI-For-Mobility—A New Research Platform for AI-Based Control Methods4 citations · 2023
- 3