A Seed Based Method for Dictionary Translation. - Robert Krajewski; Henryk Rybinski; Marek Kozlowski (ISMIS, 2014)

Discrete dynamic optimization in automated driving systems to improve energy efficiency in cooperative networks. - Philipp Themann; Robert Krajewski; Lutz Eckstein (Intelligent Vehicles Symposium, 2014)

Decoupled cooperative trajectory optimization for connected highly automated vehicles at urban intersections. - Robert Krajewski; Philipp Themann; Lutz Eckstein (Intelligent Vehicles Symposium, 2016)

A novel method for dictionary translation. - Robert Krajewski; Henryk Rybinski; Marek Kozlowski (J. Intell. Inf. Syst., 2016)

The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems. - Robert Krajewski; Julian Bock; Laurent Kloeker; Lutz Eckstein (ITSC, 2018)

Data-Driven Maneuver Modeling using Generative Adversarial Networks and Variational Autoencoders for Safety Validation of Highly Automated Vehicles. - Robert Krajewski; Tobias Moers; Dominik Nerger; Lutz Eckstein (ITSC, 2018)

BézierVAE: Improved Trajectory Modeling using Variational Autoencoders for the Safety Validation of Highly Automated Vehicles. - Robert Krajewski; Tobias Moers; Adrian Meister; Lutz Eckstein (ITSC, 2019)

VeGAN: Using GANs for Augmentation in Latent Space to Improve the Semantic Segmentation of Vehicles in Images From an Aerial Perspective. - Robert Krajewski; Tobias Moers; Lutz Eckstein (WACV, 2019)

The rounD Dataset: A Drone Dataset of Road User Trajectories at Roundabouts in Germany. - Robert Krajewski; Tobias Moers; Julian Bock; Lennart Vater; Lutz Eckstein (ITSC, 2020)

The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections. - Julian Bock; Robert Krajewski; Tobias Moers; Steffen Runde; Lennart Vater; Lutz Eckstein (IV, 2020)

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. - Robert Krajewski; Michael Hoss; Adrian Meister; Fabian Thomsen; Julian Bock; Lutz Eckstein (IV, 2020)

Drone-based Generation of Sensor Reference and Training Data for Highly Automated Vehicles. - Robert Krajewski; Lennart Vater; Marvin Klimke; Tobias Moers; Julian Bock; Lutz Eckstein (ITSC, 2021)

Comparison of Camera-Equipped Drones and Infrastructure Sensors for Creating Trajectory Datasets of Road Users. - Amarin Kloeker; Robert Krajewski; Lutz Eckstein (VEHITS, 2021)

The ConScenD Dataset: Concrete Scenarios from the highD Dataset According to ALKS Regulation UNECE R157 in OpenX. - Alexander Tenbrock; Alexander König; Thomas Keutgens; Julian Bock; Hendrik Weber; Robert Krajewski; Adrian Zlocki (CoRR, 2021)

The exiD Dataset: A Real-World Trajectory Dataset of Highly Interactive Highway Scenarios in Germany. - Tobias Moers; Lennart Vater; Robert Krajewski; Julian Bock; Adrian Zlocki; Lutz Eckstein (IV, 2022)

An Automated Analysis Framework for Trajectory Datasets. - Christoph Glasmacher; Robert Krajewski; Lutz Eckstein (CoRR, 2022)

DeepAerialMapper: Deep Learning-based Semi-automatic HD Map Creation for Highly Automated Vehicles. - Robert Krajewski; Huijo Kim (CoRR, 2024)