@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}
}
@article{CollPeralesSchulteTiggesRondinoneetal.2022,
  author    = {Coll-Perales, Baldomero and Schulte-Tigges, Joschua and Rondinone, Michele and Gozalvez, Javier and Reke, Michael and Matheis, Dominik and Walter, Thomas},
  title     = {Prototyping and evaluation of infrastructure-assisted transition of control for cooperative automated vehicles},
  series = {IEEE Transactions on Intelligent Transportation Systems},
  volume    = {23},
  journal   = {IEEE Transactions on Intelligent Transportation Systems},
  number    = {7},
  publisher = {IEEE},
  issn      = {1524-9050 (Print)},
  doi       = {10.1109/TITS.2021.3061085},
  pages     = {6720 -- 6736},
  year      = {2022},
  abstract  = {Automated driving is now possible in diverse road and traffic conditions. However, there are still situations that automated vehicles cannot handle safely and efficiently. In this case, a Transition of Control (ToC) is necessary so that the driver takes control of the driving. Executing a ToC requires the driver to get full situation awareness of the driving environment. If the driver fails to get back the control in a limited time, a Minimum Risk Maneuver (MRM) is executed to bring the vehicle into a safe state (e.g., decelerating to full stop). The execution of ToCs requires some time and can cause traffic disruption and safety risks that increase if several vehicles execute ToCs/MRMs at similar times and in the same area. This study proposes to use novel C-ITS traffic management measures where the infrastructure exploits V2X communications to assist Connected and Automated Vehicles (CAVs) in the execution of ToCs. The infrastructure can suggest a spatial distribution of ToCs, and inform vehicles of the locations where they could execute a safe stop in case of MRM. This paper reports the first field operational tests that validate the feasibility and quantify the benefits of the proposed infrastructure-assisted ToC and MRM management. The paper also presents the CAV and roadside infrastructure prototypes implemented and used in the trials. The conducted field trials demonstrate that infrastructure-assisted traffic management solutions can reduce safety risks and traffic disruptions.},
  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}
}