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Machine Learning applied to Automated/Autonomous Driving - Safe Cooperative Multi-Agent Trajectory Planning



Information on the Thesis

Type of Final Thesis: Master
Supervisor: Karl Kurzer
Research Group: Applied Technical Cognitive Systems

Archive Number: 4.492
Status of Thesis: Open
Date of start: 2019-08-14

Further Information

Automated, cooperative vehicles have to make decisions in road traffic in a highly dynamic, interacting and incompletely perceptible environment. Previous attempts are usually limited to situation assessment from an egocentric perspective, without taking cooperation aspects into account, or interactions between other road users.

TASKS

The goal of this thesis is the extension of an existing cooperative multi-agent planning algorithm by the aspect of safety. In this context, methods such as RSS, which guarantee a one-sided safety while driving, are to be used. RSS can perform both the risk assessment (classification of safe/dangerous situations) and the risk mitigation (generation of the proper response). Both aspects should be integrated within the planning algorithm to ensure the generation of safe actions.

WE OFFER

  • An interdisciplinary research environment with partners from science and industry
  • A constructive collaboration with bright, motivated employees
  • A pleasant working atmosphere

WE EXPECT

  • Knowledge in depth and breadth in the field of artificial intelligence (especially search algorithms and machine learning), game theory or closely related areas
  • Ability to implement both state of the art, as well as experimental algorithms
  • Good C++ (C++11, STL, etc.) and Python Skills
  • Sound English skills
  • High creativity and productivity
  • Experience with Monte Carlo Tree Search/Reinforcement Learning are a plus

REQUIRED DOCUMENTS

  • current transcript of records
  • CV

CONTACT

Karl Kurzer