@article{SchulteTiggesFoersterNikolovskietal.2022,
  author    = {Schulte-Tigges, Joschua and F{\"o}rster, Marco and Nikolovski, Gjorgji and Reke, Michael and Ferrein, Alexander and Kaszner, Daniel and Matheis, Dominik and Walter, Thomas},
  title     = {Benchmarking of various LiDAR sensors for use in self-driving vehicles in real-world environments},
  series = {Sensors},
  volume    = {22},
  journal   = {Sensors},
  number    = {19},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1424-8220},
  doi       = {10.3390/s22197146},
  pages     = {20 Seiten},
  year      = {2022},
  abstract  = {Abstract In this paper, we report on our benchmark results of the LiDAR sensors Livox Horizon, Robosense M1, Blickfeld Cube, Blickfeld Cube Range, Velodyne Velarray H800, and Innoviz Pro. The idea was to test the sensors in different typical scenarios that were defined with real-world use cases in mind, in order to find a sensor that meet the requirements of self-driving vehicles. For this, we defined static and dynamic benchmark scenarios. In the static scenarios, both LiDAR and the detection target do not move during the measurement. In dynamic scenarios, the LiDAR sensor was mounted on the vehicle which was driving toward the detection target. We tested all mentioned LiDAR sensors in both scenarios, show the results regarding the detection accuracy of the targets, and discuss their usefulness for deployment in self-driving cars.},
  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}
}