Team Lead

Research Topics

Our research focuses on

  • Multi-modal perception and data fusion
  • Reinforcement learning
  • Data-driven decision making (3DM)

for autonomous systems and autonomous multi-agent systems.

Multi-modal perception and data fusion

  • Perception modeling (lidar-radar-camera combinations)
    • AI-based, physical sensor models, hybrid models
    • Sensor fault prediction and detection
    • Dynamic sensing of the vehicle environment considering detection uncertainties
  • Fusion of different sensor sources to increase reliability of autonomous systems (probabilistic methods, evidence theory-based, semantics-oriented, etc.)
    • Statistical methods for data association
    • Dynamic occupancy grid filtering
    • Multiple target tracking (MTT) and extended object tracking
      • Multiple detections per object and from multiple targets
      • E.g., Gaussian mixture probability hypothesis density (phd) tracker or Gamma Gaussian inverse Wishart tracker

Reinforcement learning (RL)

  • Bayesian modeling and simulation
  • Dynamic programming
  • Learning from limited samples / sample efficiency
  • Interacting with systems that are partially observable
  • Learning from multi-objective or poorly specified reward functions
  • Stochastic environments
    • Many real-world environments are stochastic, making it difficult for RL agents to learn effectively
  • Transfer learning, i.e. transfer of knowledge from one task or environment to another
  • RL in path planning of autonomous systems
  • RL for multi-agent systems
  • Deep learning implementation

Decision making under uncertainty

  • (Partial observable) Markov decision processes (POMDP and MDP)
  • Modeling of uncertainties in perception and signal propagation (model and state uncertainty)
  • Simulation of collaborative autonomous multi-agent systems
  • Distributed and collaborative decision making
  • Data-driven decision making (3DM) and decision making support capitalizing on AI
  • Large language models (LLMs)






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