Embodied AI · Computer Vision · Machine Learning
My research bridges maps, perception, and learning to enable robust real-world AI systems under limited annotation and significant sensor domain shifts. I specialize in cross-sensor domain adaptation and scalable training data generation for embodied AI in autonomous driving.
I am a PhD candidate at the Karlsruhe Institute of Technology (KIT) and FZI Research Center for Information Technology, supervised by Prof. Christoph Stiller. My thesis focuses on "Learning from Maps: Scalable Ground Truth Generation in Autonomous Driving" and I will defend on March 10, 2026.
My research enables robust real-world AI models under limited annotation and significant sensor domain shifts by bridging maps, perception, and learning. I am particularly interested in leveraging HD maps and multi-drive mapping to overcome data scarcity in autonomous driving.
During my studies, I have completed extensive coursework in optimization, system theory, and machine learning, and gained research, industry, and teaching experience in autonomous driving and robotics. I have developed strong proficiency in Python, C++, ROS, and modern deep learning frameworks (PyTorch, TensorFlow).
I actively collaborate with leading research institutions and industry partners worldwide, bridging fundamental research with real-world industrial autonomous driving applications.
My research focuses on enabling robust and scalable embodied AI systems for real-world applications, with emphasis on autonomous driving and mobile robotics.
Scalable ground truth generation for autonomous driving by leveraging high-definition maps and multi-drive data to overcome annotation bottlenecks and enable data-efficient learning.
Developing robust domain adaptation techniques for embodied AI systems to generalize across different sensor modalities, environmental conditions, and deployment scenarios.
Scaling real-world deployment of end-to-end autonomous driving stacks in complex urban environments, from perception through planning to control.
Frank Bieder, Hendrik Königshof, Haohao Hu, Fabian Immel, Yinzhe Shen, Jan-Hendrik Pauls, Christoph Stiller
Cross-modal domain adaptation for robust perception across different sensor modalities.
Fabian Immel, Jan-Hendrik Pauls, Richard Fehler, Frank Bieder, Jonas Merkert, Christoph Stiller
Online HD map construction using text-annotated navigation maps.
Fabian Immel, Richard Fehler, Frank Bieder, Jan-Hendrik Pauls, Christoph Stiller
Generalist transformer model for real-world high-definition map completion.
Frank Bieder, Haohao Hu, Johannes Schantz, Oguzahn Kirik, Florian Ries, Martin Haueis, Christoph Stiller
Automated training data generation using HD maps and multi-drive mapping.
Haohao Hu, F. Han, Frank Bieder, Jan-Hendrik Pauls, Christoph Stiller
Targetless extrinsic calibration of multi-modal sensors with uncertainty quantification.
Christoph Burger, Johannes Fischer, Frank Bieder, Ömer Tas, Christoph Stiller
Game-theoretic approach to motion planning considering interaction between automated vehicles.
Kunyu Peng, Juncong Fei, Kailun Yang, Alina Roitberg, Jiaming Zhang, Frank Bieder, Philipp Heidenreich, Christoph Stiller, Rainer Stiefelhagen
Multi-attentional approach for dense semantic understanding from LiDAR data.
Frank Bieder, Maximilian Link, Simon Romanski, Haohao Hu, Christoph Stiller
Novel approach to semantic segmentation using complementary feature representations.
Frank Bieder, Sascha Wirges, Johannes Janosovits, Sven Richter, Zheyuan Wang, Christoph Stiller
Multi-layer grid map representation for robust semantic segmentation from sparse sensor data.
FZI Research Center for Information Technology
Visual and Spatial Learning for Autonomous Driving · Supervised by Prof. Christoph Stiller
FZI Research Center for Information Technology
Visual and Spatial Learning for Autonomous Driving · Supervised by Prof. Christoph Stiller
UC Berkeley – Mechanical Systems Control Lab
Supervised by Prof. Masayoshi Tomizuka
Atlatec GmbH (acquired by Bosch in 2022)
Supervised by Dr. Julius Ziegler
Daimler AG
Supervised by Dr. Uwe Franke & Fiete Botschen
Karlsruhe Institute of Technology (KIT)
Thesis: Learning from Maps: Scalable Ground Truth Generation in Autonomous Driving
Supervisor: Prof. Christoph Stiller · Defense: March 10, 2026
Hector School of Engineering and Management
Coursework to equip doctoral researchers with expertise in project management, human resource management and finance
UC Berkeley – Mechanical Systems Control Lab
Research on map-less driving and map perception · Supervised by Prof. Masayoshi Tomizuka
Funded by KHYS Exchange Grant
Karlsruhe Institute of Technology (KIT)
GPA: 1.4/1.0 · Focus: Machine learning, system theory & robotics
Thesis: Supervised Domain Adaptation for Semantic Segmentation (Grade: 1.0/1.0)
University of Ottawa, Canada
Canadian GPA: 3.8/4.0 · Focus: Machine learning and data processing
Karlsruhe Institute of Technology (KIT)
Thesis: Fusion of Data from Stereo Camera and Radar Sensor for Tracking of Extended Objects (Grade: 1.0/1.0)
I'm always happy to discuss research collaborations, student projects, and opportunities in autonomous driving and machine learning. Feel free to reach out via email or connect on social media.
Open to: Research collaborations · PhD student supervision · Master's thesis supervision · Industrial partnerships · Speaking engagements
