@inproceedings{RekePeterSchulteTiggesetal.2020,
  author    = {Reke, Michael and Peter, Daniel and Schulte-Tigges, Joschua and Schiffer, Stefan and Ferrein, Alexander and Walter, Thomas and Matheis, Dominik},
  title     = {A Self-Driving Car Architecture in ROS2},
  series = {2020 International SAUPEC/RobMech/PRASA Conference, Cape Town, South Africa},
  booktitle = {2020 International SAUPEC/RobMech/PRASA Conference, Cape Town, South Africa},
  publisher = {IEEE},
  address   = {New York, NY},
  isbn      = {978-1-7281-4162-6},
  doi       = {10.1109/SAUPEC/RobMech/PRASA48453.2020.9041020},
  pages     = {1 -- 6},
  year      = {2020},
  abstract  = {In this paper we report on an architecture for a self-driving car that is based on ROS2. Self-driving cars have to take decisions based on their sensory input in real-time, providing high reliability with a strong demand in functional safety. In principle, self-driving cars are robots. However, typical robot software, in general, and the previous version of the Robot Operating System (ROS), in particular, does not always meet these requirements. With the successor ROS2 the situation has changed and it might be considered as a solution for automated and autonomous driving. Existing robotic software based on ROS was not ready for safety critical applications like self-driving cars. We propose an architecture for using ROS2 for a self-driving car that enables safe and reliable real-time behaviour, but keeping the advantages of ROS such as a distributed architecture and standardised message types. First experiments with an automated real passenger car at lower and higher speed-levels show that our approach seems feasible for autonomous driving under the necessary real-time conditions.},
  language  = {en}
}
@incollection{PfetschAbeleAltherretal.2021,
  author    = {Pfetsch, Marc E. and Abele, Eberhard and Altherr, Lena and B{\"o}lling, Christian and Br{\"o}tz, Nicolas and Dietrich, Ingo and Gally, Tristan and Geßner, Felix and Groche, Peter and Hoppe, Florian and Kirchner, Eckhard and Kloberdanz, Hermann and Knoll, Maximilian and Kolvenbach, Philip and Kuttich-Meinlschmidt, Anja and Leise, Philipp and Lorenz, Ulf and Matei, Alexander and Molitor, Dirk A. and Niessen, Pia and Pelz, Peter F. and Rexer, Manuel and Schmitt, Andreas and Schmitt, Johann M. and Schulte, Fiona and Ulbrich, Stefan and Weigold, Matthias},
  title     = {Strategies for mastering uncertainty},
  series = {Mastering uncertainty in mechanical engineering},
  booktitle = {Mastering uncertainty in mechanical engineering},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-78353-2},
  doi       = {10.1007/978-3-030-78354-9_6},
  pages     = {365 -- 456},
  year      = {2021},
  abstract  = {This chapter describes three general strategies to master uncertainty in technical systems: robustness, flexibility and resilience. It builds on the previous chapters about methods to analyse and identify uncertainty and may rely on the availability of technologies for particular systems, such as active components. Robustness aims for the design of technical systems that are insensitive to anticipated uncertainties. Flexibility increases the ability of a system to work under different situations. Resilience extends this characteristic by requiring a given minimal functional performance, even after disturbances or failure of system components, and it may incorporate recovery. The three strategies are described and discussed in turn. Moreover, they are demonstrated on specific technical systems.},
  language  = {en}
}