@inproceedings{ChajanSchulteTiggesRekeetal.2021,
  author    = {Chajan, Eduard and Schulte-Tigges, Joschua and Reke, Michael and Ferrein, Alexander and Matheis, Dominik and Walter, Thomas},
  title     = {GPU based model-predictive path control for self-driving vehicles},
  series = {IEEE Intelligent Vehicles Symposium (IV)},
  booktitle = {IEEE Intelligent Vehicles Symposium (IV)},
  publisher = {IEEE},
  isbn      = {978-1-7281-5394-0},
  doi       = {10.1109/IV48863.2021.9575619},
  pages     = {1243 -- 1248},
  year      = {2021},
  abstract  = {One central challenge for self-driving cars is a proper path-planning. Once a trajectory has been found, the next challenge is to accurately and safely follow the precalculated path. The model-predictive controller (MPC) is a common approach for the lateral control of autonomous vehicles. The MPC uses a vehicle dynamics model to predict the future states of the vehicle for a given prediction horizon. However, in order to achieve real-time path control, the computational load is usually large, which leads to short prediction horizons. To deal with the computational load, the control algorithm can be parallelized on the graphics processing unit (GPU). In contrast to the widely used stochastic methods, in this paper we propose a deterministic approach based on grid search. Our approach focuses on systematically discovering the search area with different levels of granularity. To achieve this, we split the optimization algorithm into multiple iterations. The best sequence of each iteration is then used as an initial solution to the next iteration. The granularity increases, resulting in smooth and predictable steering angle sequences. We present a novel GPU-based algorithm and show its accuracy and realtime abilities with a number of real-world experiments.},
  language  = {en}
}
@incollection{BensbergAuthCzarnecki2021,
  author    = {Bensberg, Frank and Auth, Gunnar and Czarnecki, Christian},
  title     = {Finding the perfect RPA match : a criteria-based selection method for RPA solutions},
  series = {Robotic process automation : Management, technology, applications},
  booktitle = {Robotic process automation : Management, technology, applications},
  editor    = {Czarnecki, Christian and Fettke, Peter},
  publisher = {De Gruyter},
  address   = {Oldenbourg},
  isbn      = {978-3-11-067677-8},
  doi       = {10.1515/9783110676693-201},
  pages     = {47 -- 75},
  year      = {2021},
  abstract  = {The benefits of robotic process automation (RPA) are highly related to the usage of commercial off-the-shelf (COTS) software products that can be easily implemented and customized by business units. But, how to find the best fitting RPA product for a specific situation that creates the expected benefits? This question is related to the general area of software evaluation and selection. In the face of more than 75 RPA products currently on the market, guidance considering those specifics is required. Therefore, this chapter proposes a criteria-based selection method specifically for RPA. The method includes a quantitative evaluation of costs and benefits as well as a qualitative utility analysis based on functional criteria. By using the visualization of financial implications (VOFI) method, an application-oriented structure is provided that opposes the total cost of ownership to the time savings times salary (TSTS). For the utility analysis a detailed list of functional criteria for RPA is offered. The whole method is based on a multi-vocal review of scientific and non-scholarly literature including publications by business practitioners, consultants, and vendors. The application of the method is illustrated by a concrete RPA example. The illustrated structures, templates, and criteria can be directly utilized by practitioners in their real-life RPA implementations. In addition, a normative decision process for selecting RPA alternatives is proposed before the chapter closes with a discussion and outlook.},
  language  = {en}
}
@incollection{AltherrLeisePfetschetal.2021,
  author    = {Altherr, Lena and Leise, Philipp and Pfetsch, Marc E. and Schmitt, Andreas},
  title     = {Optimal design of resilient technical systems on the example of water supply systems},
  series = {Mastering Uncertainty in Mechanical Engineering},
  booktitle = {Mastering Uncertainty in Mechanical Engineering},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-78356-3},
  pages     = {429 -- 433},
  year      = {2021},
  language  = {en}
}
@incollection{AltherrLeise2021,
  author    = {Altherr, Lena and Leise, Philipp},
  title     = {Resilience as a concept 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},
  pages     = {412 -- 417},
  year      = {2021},
  language  = {en}
}