Autonomous driving vehicles are a rapidly emerging technlogy that will radically transform the face of public and personal transportation in the near future. This work is part of project autoBAHN, which has the goal to develop an autonomous driving train and in turn prevent small railroad branch lines from beeing shut down due to cost saving measures. The focus of research in the field of sensors used for autonomous vehicles is on the detection of obstacles. However, detecting obstacles is only a part of an autonomous driving vehicle. This work aims at providing the basis for making a complete autonomous driving train possible. This basis is a combination of sensor calibration techniques, track detection for railroads to classify obstacles and non-obstacles and simulation of sensor data for the verification of the individual underlying algorithms.
M. Gschwandtner: "Support Framework for Obstacle Detection on Autonomous Trains"; Supervisor, Reviewer: A. Uhl, M. Gelautz; Department of Computer Sciences, University of Salzburg, 2013; oral examination: 01-17-2013.
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