Student Assistant - Automated Driving/Autonomous Driving - Machine Learning
Automated, cooperative vehicles have to make decisions in road traffic in a highly dynamic, interacting and incompletely perceptible environment. Previous approaches are usually only considering an egocentric perspective, without considering any cooperative aspects, with or between others.
For the prediction and planning of cooperative driving maneuvers, methods for searching and learning are developed which account for the interdependencies of individual traffic participants, as well as model the system states probabilistically.
The project encompasses a variety of tasks among other things we seek support in the following areas.
- Learning of cost metrics for driving maneuvers (Inverse Reinforcement Learning)
- Learning of behavior models (Deep Reinforcement Learning)
- Hyper Parameter Optimization (Baysian Optimization)
- Parallelizing of the search method
- Description and creation of test scenarios
- Inferring cooperative aspects
- An interdisciplinary research environment with partners from science and industry
- A constructive collaboration with bright, motivated employees
- A pleasant working atmosphere
- Ability to implement both state of the art and experimental algorithms
- Basic C++ knowledge (C++11, STL, etc.)
- Sound English or German skills
- High creativity and productivity
- Knowledge in the field of artificial intelligence (especially searching and learning), game theory or related areas are a plus
- Experiences with methods for searching and learning, e.g. Monte Carlo tree search/Reinforcement Learning are a plus
- current transcript of records
Student Assistants / Tutors
No information available