MUTIG-VORAN - Multiple transport processes in Galileo-based traffic scenarios using optimisation methods for real applications
Working Group: | WG Optimization and Optimal Control |
Leadership: |
Prof. Dr. Christof Büskens ((0421) 218-63861, E-Mail: bueskens@math.uni-bremen.de )
Dr. Margareta Runge |
Processor: |
Dr. Kai Wah Chan (E-Mail: kai.wah.chan@topas.tech)
Xibo Li ((0421) 218-63874, E-Mail: lixibo@uni-bremen.de) Jan Phan ((0421) 218-63866, E-Mail: gienapp@uni-bremen.de) |
Funding: | Bundesministerium für Wirtschaft und Energie |
Project partner: |
AG Kognitive Neuroinformatik, Universität Bremen Arbeitsbereich Nachrichtentechnik, Uni Bremen TOPAS Industriemathematik Innovation gGmbH |
Time period: | 01.01.2022 - 30.06.2025 |

The overarching goals of the project, which will be implemented with real vehicles in particular, are:
- Development of efficient AI algorithms (including trajectory optimisation, deep learning, model predictive control, sensor fusion) for highly automated driving.
- Use of mobile communication paths (5G, satellite) for resilient and efficient algorithms.
- Development of predictive algorithms for situation assessment and safeguarding needs.
- Development of V2X and remote control for safe and connected road traffic and edge cloud computing for AI algorithms of highly automated driving.
Publications
- X. Li, S. Patel, C. Büskens.
Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinforcement Learning-Based Planning Framework.
, 11th International Conference on Automation, Robotics, and Applications (ICARA) , IEEE, 2025.