@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},
  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},
  isbn      = {978-1-7281-4162-6},
  doi       = {10.1109/SAUPEC/RobMech/PRASA48453.2020.9041020},
  pages     = {1 -- 6},
  year      = {2020},
  language  = {en}
}