Dr.-Ing. Christian Müller

Contact +49 681 85775 4823 (Saarbrücken)

Address (Saarbrücken) Gebäude D3 4Stuhlsatzenhausweg 366123 Saarbrücken

Publications

Robust Collaborative Perception: Combining Adversarial Training with Consensus Mechanism for Enhanced V2X Security
Atanas Poibrenski; Farzad Nozarian; Farzaneh Rezaeianaran; Christian Müller
In: IEEE IV-2025. IEEE Intelligent Vehicles Symposium (IV-2025), June 22-25, Cluj-Napoca, Romania, IEEE Xplore, 2025.
HD-VoxelFlex: Flexible High-Definition Voxel Grid Representation
Igor Vozniak; Pavel Astreika; Philipp Müller; Nils Lipp; Christian Müller; Philipp Slusallek
In: VISIGRAPP (4): VISAPP. International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP-2024), SCITEPRESS, 2024.
Reliable Student: Addressing Noise in Semi-Supervised 3D Object Detection
Farzad Nozarian; Shashank Agarwal; Farzaneh Rezaeianaran; Danish Shahzad; Atanas Poibrenski; Christian Müller; Philipp Slusallek
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. CVPR Workshop on Learning with Limited Labelled Data for Image and Video Understanding (L3D-IVU-2023), located at 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 19, Vancouver, Canada, Pages 4981-4990, IEEE, 6/2023.

Co-AVAAIR
Cooperative Autonomous Vehicles with Advanced AI and Reasoning
The Co-AVAAIR project aims to strategically advance competencies in the field of automated and connected driving. Its core focus lies on the research, development, and validation of AI-based systems…
BERTHA
BEhavioural Replication of Human drivers for CCAM
The Horizon Europe project BERTHA kicked off on November 22nd-24th in Valencia, Spain. The project has been granted €7,981,799.50 from the European Commission to develop a Driver Behavioral Model…
B5GCyberTestV2X
Beyond 5G Virtuelle Umgebung für Cybersicherheitstests von V2X-Systeme
The overall goal of the project is to enable safe and efficient autonomous driving by developing a virtual environment for cybersecurity testing of V2X systems in 5G and Beyond 5G scenarios…
MOMENTUM
Robust Learning with Hybrid AI for Trustworthy Interaction of Humans and Machines in Complex Environments
MOMENTUM is a research project dedicated to TRUSTED-AI, which aims to advance the development and application of artificial intelligence by integrating robustness and explainability. The aim of the…
BSI_SiKI2
Studie: Sicherheit von KI-Systemen - TP 2: Symbolische und Hybride KI-Methoden
The Federal Office for Information Security (BSI) has commissioned a study on the topic of "Security of AI systems: Symbolic and Hybrid AI Methods". The study records the current state of research of…