Machine Learning and Simulation for Future Autonomous Mobility

Level: MA/(BA)

In the context of future mobility a testbed for development applications is necessary. Different problems such as intermodal vehicle routing, parking or speed recommendations systems have traffic as a common factor. A gym-like environment based on a popular traffic macro-simulation should be extended so that experiments with different methods and problems can be conducted. For the method reinforcement learning or other optimization approaches are possible.


The focus of the proposed thesis can lay in one or multiple topics such as:

  • Multi-agent reinforcement learning methods ( eg. scheduling/routing of delivery robots)
  • Regression methods ( eg. traffic or parking demand forecasting) 
  • Simulation and modeling of traffic based on SUMO and other co-simulations


  • Very good understanding of machine learning through experience and coursework. (at least 2 good completed courses and some practical experience with e.g. Keras/Tensorflow, pytorch, FLAX …) 
  • Practical experience in co-working on Python/C++ projects. Be able to write good documentation and using tests
  • The motivation to contribute to a scientific publication 


RL Intro Book: Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. MIT press, 2018.

RL Intro Lecture:

Traffic Marco-Simulation:

Autonomous Driving Simulation:

Traffic Forecasting Challenge:

Vehicle Routing Challenge:


Send your application with a convincing set of documents to

Patrick Grzywok