Eivind Meyer joined the Cyber-Physical Systems Group in 2021 as a research assistant and Ph.D. student under the supervision of Prof. Dr.-Ing. Matthias Althoff. Previously, he received his Master's degree in Cybernetics and Robotics from the Norwegian University of Science and Technology with the thesis "On Course Towards Model-Free Guidance" about reinforcement learning-based autonomous vessel guidance.

His current research at TUM revolves around deep-learning based state representation learning and trajectory planning for autonomous driving.

My current research is particularly focused on the adoption of graph neural networks for state representation learning and self-supervised, long-term trajectory planning. Within this domain, there are multiple candidate topics that I can offer to interested Master's or Bachelor's students. Currently, the following thesis proposals are open: 

• [MA] Encoding the Future: Deep Representations for Traffic using Graph Neural Networks
• [MA] Deep Generative Models for Road Network Synthesis
• [BA/MA] Learning Isometric Embeddings of Road Networks using Multidimensional Scaling

Additionally, topics related to e.g. reinforcement learning or formal safety are relevant to my research and can potentially be offered as thesis projects.

In general, please feel free to contact me by email if you are interested in any of the currently available topics or have specific ideas for potential research directions yourself.

• Practical course: Motion planning for autonomous vehicles (IN2106, IN0012)
  • WS 21/22: Graph Representations for Predictive Modelling in Traffic Scenes (co-supervised with Luis Gressenbuch)
  • WS 21/22: Developing an Autonomous Vessel Simulation (co-supervised with Hanna Krasowski)
• Lectures: Techniques in Artificial Intelligence (IN2062)
  • WS 21/22
• Seminar: Cyber-Physical Systems (IN2107, IN4813)
  • WS 21/22: Distance-Preserving Embeddings of Lanelet Networks

1. Meyer E, Heiberg A, Rasheed A, and San O: COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicles using Deep Reinforcement Learning, 2020
2. Meyer E, Robinson H, Rasheed A, and San O: Taming an Autonomous Surface Vehicle for Path Following and Collision Avoidance using Deep Reinforcement Learning, 2020