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Parallelization of Continuous Monte-Carlo Tree Search for Planning Cooperative Driving Maneuvers


Christoph Hörtnagl


Information on the Thesis

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

Archive Number: 4.407
Status of Thesis: Completed
Date of start: 2019-10-21
Date of submission: 2020-01-17

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 work is to use the capabilities of modern CPUs/GPUs and develop an algorithm, that can make full use of the parallelization capabilities increasing the accuracy of the sampling based approach by a large factor and at the same speed up the search satisfying the real-time constraints.

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, game theory or closely related areas
  • Ability to implement both state of the art, as well as experimental algorithms
  • Good C++ Skills (C++11, STL, etc.)
  • Sound English skills
  • High creativity and productivity
  • Experiences with parallel computing is a plus

REQUIRED DOCUMENTS

  • current transcript of records
  • CV

CONTACT

Karl Kurzer