@inproceedings{ReisgenSchleserScheiketal.2011, author = {Reisgen, Uwe and Schleser, Markus and Scheik, Sven and Michaeli, Walter and Gr{\"o}nlund, Oliver and Neuß, Andreas and Wunderle, Johannes and Poprawe, Reinhart and R{\"o}sner, A. and Bobzin, Kirsten and Schl{\"a}fer, Thomas and Theiß, Sebastian and Kutschmann, Pia and Haberstroh, Edmund and Flock, Dustin and B{\"u}hrig-Polaczek, Andreas and Jakob, M.}, title = {Novel process chains for the production of plastics/metal-hybrids}, series = {17th International Conference on Concurrent Enterprising (ICE 2011) : Aachen, Germany, 20 - 22 June 2011}, booktitle = {17th International Conference on Concurrent Enterprising (ICE 2011) : Aachen, Germany, 20 - 22 June 2011}, editor = {Thoben, Klaus-Dieter}, publisher = {IEEE}, address = {Piscataway, NJ}, organization = {Forschungsinstitut f{\"u}r Rationalisierung ; International Conference on Concurrent Enterprising <17, 2011, Aachen>}, isbn = {978-1-457-70772-8 ; 978-3-943024-05-0}, pages = {596 -- 604}, year = {2011}, language = {en} } @inproceedings{SchulteTiggesMatheisRekeetal.2023, author = {Schulte-Tigges, Joschua and Matheis, Dominik and Reke, Michael and Walter, Thomas and Kaszner, Daniel}, title = {Demonstrating a V2X enabled system for transition of control and minimum risk manoeuvre when leaving the operational design domain}, series = {HCII 2023: HCI in Mobility, Transport, and Automotive Systems}, booktitle = {HCII 2023: HCI in Mobility, Transport, and Automotive Systems}, editor = {Kr{\"o}mker, Heidi}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-35677-3 (Print)}, doi = {10.1007/978-3-031-35678-0_12}, pages = {200 -- 210}, year = {2023}, abstract = {Modern implementations of driver assistance systems are evolving from a pure driver assistance to a independently acting automation system. Still these systems are not covering the full vehicle usage range, also called operational design domain, which require the human driver as fall-back mechanism. Transition of control and potential minimum risk manoeuvres are currently research topics and will bridge the gap until full autonomous vehicles are available. The authors showed in a demonstration that the transition of control mechanisms can be further improved by usage of communication technology. Receiving the incident type and position information by usage of standardised vehicle to everything (V2X) messages can improve the driver safety and comfort level. The connected and automated vehicle's software framework can take this information to plan areas where the driver should take back control by initiating a transition of control which can be followed by a minimum risk manoeuvre in case of an unresponsive driver. This transition of control has been implemented in a test vehicle and was presented to the public during the IEEE IV2022 (IEEE Intelligent Vehicle Symposium) in Aachen, Germany.}, language = {en} } @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}, address = {New York, NY}, 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} } @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} }