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Robert Krajewski
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2020 – today
- 2024
- [i5]Robert Krajewski, Huijo Kim:
DeepAerialMapper: Deep Learning-based Semi-automatic HD Map Creation for Highly Automated Vehicles. CoRR abs/2410.00769 (2024) - 2022
- [c13]Tobias Moers, Lennart Vater, Robert Krajewski, Julian Bock, Adrian Zlocki, Lutz Eckstein:
The exiD Dataset: A Real-World Trajectory Dataset of Highly Interactive Highway Scenarios in Germany. IV 2022: 958-964 - [i4]Christoph Glasmacher, Robert Krajewski, Lutz Eckstein:
An Automated Analysis Framework for Trajectory Datasets. CoRR abs/2202.07438 (2022) - 2021
- [c12]Robert Krajewski, Lennart Vater, Marvin Klimke, Tobias Moers, Julian Bock, Lutz Eckstein:
Drone-based Generation of Sensor Reference and Training Data for Highly Automated Vehicles. ITSC 2021: 3067-3074 - [c11]Amarin Kloeker, Robert Krajewski, Lutz Eckstein:
Comparison of Camera-Equipped Drones and Infrastructure Sensors for Creating Trajectory Datasets of Road Users. VEHITS 2021: 161-170 - [i3]Alexander Tenbrock, Alexander König, Thomas Keutgens, Julian Bock, Hendrik Weber, Robert Krajewski, Adrian Zlocki:
The ConScenD Dataset: Concrete Scenarios from the highD Dataset According to ALKS Regulation UNECE R157 in OpenX. CoRR abs/2103.09772 (2021) - 2020
- [c10]Robert Krajewski, Tobias Moers, Julian Bock, Lennart Vater, Lutz Eckstein:
The rounD Dataset: A Drone Dataset of Road User Trajectories at Roundabouts in Germany. ITSC 2020: 1-6 - [c9]Robert Krajewski, Michael Hoss, Adrian Meister, Fabian Thomsen, Julian Bock, Lutz Eckstein:
Using Drones as Reference Sensors for Neural-Networks-Based Modeling of Automotive Perception Errors**The research leading to these results is funded by the Federal Ministry for Economic Affairs and Energy within the project "VVM - Verification and Validation Methods for Automated Vehicles Level 4 and 5". The authors would like to thank the consortium for the successful cooperation. IV 2020: 708-715 - [c8]Julian Bock, Robert Krajewski, Tobias Moers, Steffen Runde, Lennart Vater, Lutz Eckstein:
The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections. IV 2020: 1929-1934
2010 – 2019
- 2019
- [c7]Robert Krajewski, Tobias Moers, Adrian Meister, Lutz Eckstein:
BézierVAE: Improved Trajectory Modeling using Variational Autoencoders for the Safety Validation of Highly Automated Vehicles. ITSC 2019: 3788-3795 - [c6]Robert Krajewski, Tobias Moers, Lutz Eckstein:
VeGAN: Using GANs for Augmentation in Latent Space to Improve the Semantic Segmentation of Vehicles in Images From an Aerial Perspective. WACV 2019: 1440-1448 - [i2]Julian Bock, Robert Krajewski, Tobias Moers, Steffen Runde, Lennart Vater, Lutz Eckstein:
The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections. CoRR abs/1911.07602 (2019) - 2018
- [c5]Robert Krajewski, Julian Bock, Laurent Kloeker, Lutz Eckstein:
The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems. ITSC 2018: 2118-2125 - [c4]Robert Krajewski, Tobias Moers, Dominik Nerger, Lutz Eckstein:
Data-Driven Maneuver Modeling using Generative Adversarial Networks and Variational Autoencoders for Safety Validation of Highly Automated Vehicles. ITSC 2018: 2383-2390 - [i1]Robert Krajewski, Julian Bock, Laurent Kloeker, Lutz Eckstein:
The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems. CoRR abs/1810.05642 (2018) - 2016
- [j1]Robert Krajewski, Henryk Rybinski, Marek Kozlowski:
A novel method for dictionary translation. J. Intell. Inf. Syst. 47(3): 491-514 (2016) - [c3]Robert Krajewski, Philipp Themann, Lutz Eckstein:
Decoupled cooperative trajectory optimization for connected highly automated vehicles at urban intersections. Intelligent Vehicles Symposium 2016: 741-746 - 2014
- [c2]Robert Krajewski, Henryk Rybinski, Marek Kozlowski:
A Seed Based Method for Dictionary Translation. ISMIS 2014: 415-424 - [c1]Philipp Themann, Robert Krajewski, Lutz Eckstein:
Discrete dynamic optimization in automated driving systems to improve energy efficiency in cooperative networks. Intelligent Vehicles Symposium 2014: 370-375
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last updated on 2024-11-06 20:27 CET by the dblp team
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