Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Strohbeck, Jan"'
Publikováno v:
2024 American Control Conference (ACC), Publication Year: 2024, Pages: 111-116
Motion prediction for intelligent vehicles typically focuses on estimating the most probable future evolutions of a traffic scenario. Estimating the gap acceptance, i.e., whether a vehicle merges or crosses before another vehicle with the right of wa
Externí odkaz:
http://arxiv.org/abs/2401.14879
For automated driving, predicting the future trajectories of other road users in complex traffic situations is a hard problem. Modern neural networks use the past trajectories of traffic participants as well as map data to gather hints about the poss
Externí odkaz:
http://arxiv.org/abs/2310.15692
Autor:
Tsaregorodtsev, Alexander, Holzbock, Adrian, Strohbeck, Jan, Buchholz, Michael, Belagiannis, Vasileios
Connected and cooperative driving requires precise calibration of the roadside infrastructure for having a reliable perception system. To solve this requirement in an automated manner, we present a robust extrinsic calibration method for automated ge
Externí odkaz:
http://arxiv.org/abs/2304.10814
Autor:
Tsaregorodtsev, Alexander, Müller, Johannes, Strohbeck, Jan, Herrmann, Martin, Buchholz, Michael, Belagiannis, Vasileios
Monocular camera sensors are vital to intelligent vehicle operation and automated driving assistance and are also heavily employed in traffic control infrastructure. Calibrating the monocular camera, though, is time-consuming and often requires signi
Externí odkaz:
http://arxiv.org/abs/2208.03949
Publikováno v:
2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), 2022, pp. 805-810
The advance towards higher levels of automation within the field of automated driving is accompanied by increasing requirements for the operational safety of vehicles. Induced by the limitation of computational resources, trade-offs between the compu
Externí odkaz:
http://arxiv.org/abs/2207.01902
Motion planning at urban intersections that accounts for the situation context, handles occlusions, and deals with measurement and prediction uncertainty is a major challenge on the way to urban automated driving. In this work, we address this challe
Externí odkaz:
http://arxiv.org/abs/2110.11246
Publikováno v:
2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)
Autonomous vehicles need precise knowledge on dynamic objects in their surroundings. Especially in urban areas with many objects and possible occlusions, an infrastructure system based on a multi-sensor setup can provide the required environment mode
Externí odkaz:
http://arxiv.org/abs/2011.05657
Autor:
Buchholz, Michael, Strohbeck, Jan, Adaktylos, Anna-Maria, Vogl, Friedrich, Allmer, Gottfried, Barros, Sergio Cabrero, Lassoued, Yassine, Wimmer, Markus, Hätty, Birger, Massot, Guillemette, Ponchel, Christophe, Bretin, Maxime, Sourlas, Vasilis, Amditis, Angelos
Information and communication technology (ICT) is an enabler for establishing automated vehicles (AVs) in today's traffic systems. By providing complementary and/or redundant information via radio communication to the AV's perception by on-board sens
Externí odkaz:
http://arxiv.org/abs/2003.05229
Autor:
Müller, Johannes, Herrmann, Martin, Strohbeck, Jan, Belagiannis, Vasileios, Buchholz, Michael
Sensor calibration usually is a time consuming yet important task. While classical approaches are sensor-specific and often need calibration targets as well as a widely overlapping field of view (FOV), within this work, a cooperative intelligent vehi
Externí odkaz:
http://arxiv.org/abs/1911.01711
Urban intersections put high demands on fully automated vehicles, in particular, if occlusion occurs. In order to resolve such and support vehicles in unclear situations, a popular approach is the utilization of additional information from infrastruc
Externí odkaz:
http://arxiv.org/abs/1908.01980