Vehicle Mechatronics
This area focuses on the complex, coordinated dynamic control of vehicles and logistics equipment, taking both machine and human factors into account.
- Integrated Control of Road Vehicles: Aims to accurately maintain a vehicle’s trajectory and speed through the simultaneous, coordinated control of multiple actuators (brakes, powertrain, steering, suspension). The design emphasizes dynamic interactions, environmental factors, and communication capabilities.
- Driver Model-Based Vehicle Control: Integrates artificial control systems and vehicle dynamics by modeling the information processing mechanisms of the human brain (experience, learning, age). The goal is for Advanced Driver Assistance Systems (ADAS) to react to traffic situations as realistically as possible.
- Sway Reduction of Storage and Retrieval Machines: Reduces the dynamic oscillations of cost-effective, single-mast high-bay warehouse machines using modern control theory methods, thereby increasing positioning accuracy and the structural lifespan of the equipment.
Road Transport
Research here is centered on network-level traffic optimization, data-driven forecasting, and cooperation between vehicles and infrastructure.
- Cooperative and Energy-Optimal Control: Trajectory planning and Adaptive Cruise Control (ACC) for individual vehicles and platoons, utilizing topography and V2V/V2I communication. The goal is the multi-criteria optimization of fuel consumption, emissions, and travel time.
- V2X-Based Traffic Control and Simulation: Designing real-time, cooperative interventions based on Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I/V2X) communication data. This involves building complex simulation environments where communication networks and macro/micro traffic models can be analyzed simultaneously.
- Robust Urban Traffic Modeling and Perimeter Control: Applying Model Predictive Control (MPC) to manage uncertain urban traffic. This includes the optimal allocation of intersection green times and protecting inner-city zones by controlling traffic at the borders (perimeter control), potentially incorporating emission parameters into the optimization.
- Data-Driven Traffic State Estimation and Prediction:
- Mobile Network Data: The anonymous use of cell handover and location area update (HO/LAU) events to establish macroscopic origin-destination matrices and support adaptive traffic control.
- GPS (FCD) Based Pattern Recognition: Relying on Floating Car Data and artificial intelligence to identify traffic patterns that precede congestion, enabling proactive, short- and long-term traffic forecasting.
- Digital Twin and Mixed-Reality Solutions: Creating a virtual, continuously data-updated replica (Digital Twin) of real transport networks for precise state tracking. Additionally, developing mixed-reality environments where real vehicles interact with virtual surroundings and agents to safely validate cyber-physical systems.
- Intelligent Traffic Light Heads: Developing cost-effective intersection architectures where controllers (PLCs) integrated directly into the traffic light heads significantly reduce the wiring required between the central controller and the signal bulbs.
Air Traffic
The aviation segment’s research primarily focuses on measuring human factors (workload) and ensuring system-level safety.
- Workload-Based Sectorization: Applying artificial intelligence (neural networks) to objectively measure the workload of air traffic controllers. Algorithms use this data to propose optimal staffing levels, divide airspace into sectors, and generate alerts in case of potential controller overload.
- Integrated Safety Analysis: Developing complex analysis frameworks (component models) capable of uniformly handling safety factors arising from technical subsystems and human (controller) functions. This ultimately supports the development and refinement of Safety Management Systems (SMS).
Railway Transport
Railway research targets the reliability of critical infrastructure elements, the efficiency of control processes, and the software/mathematical assurance of development phases.
- Functional Modeling of Railway Point Machines: Monitoring the condition of railway switches (turnouts) by measuring the electrical parameters of the point machines. The goal is the predictive detection of abnormal (fault) states using trackside sensors.
- Workload-Based Workstation Allocation: A “mirror topic” of the sectorization and workload measurement theories developed in aviation, aimed at establishing a theoretically grounded approach to optimal workload distribution for centralized railway traffic controllers.
- Formal Verification and Validation (V&V): Supporting the development lifecycle of safety-critical railway control equipment with mathematically precise (formal) methods. This includes establishing unified specification, modeling, and analysis toolsets to facilitate their application in the industry.