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 research at TUM revolves around deep learning-based autonomous driving, with a special focus on graph-based state representations.

My research is particularly focused on the adoption of graph neural networks for state representation learning and behavior planning. Within this domain, there are multiple candidate topics that I can offer to interested master or bachelor students. In general, 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 (please attach your grades and a resume).

Currently available:

• [MA] Deep Multi-Step Planning for Autonomous Driving

Currently ongoing:

• [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

• 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