27 citations · 31 across the 5 of their papers we have counts for
5 papers
DeepAerialMapper: Deep Learning-based Semi-automatic HD Map Creation for Highly Automated Vehicles
Robert Krajewski, Huijo Kim
High-definition maps (HD maps) play a crucial role in the development, safety validation, and operation of highly automated vehicles. Efficiently collecting up-to-date sensor data…
An Automated Analysis Framework for Trajectory Datasets
Christoph Glasmacher, Robert Krajewski, Lutz Eckstein
Trajectory datasets of road users have become more important in the last years for safety validation of highly automated vehicles. Several naturalistic trajectory datasets with eac…
The ConScenD Dataset: Concrete Scenarios from the highD Dataset According to ALKS Regulation UNECE R157 in OpenX
Alexander Tenbrock, Alexander König, Thomas Keutgens +4
With Regulation UNECE R157 on Automated Lane-Keeping Systems, the first framework for the introduction of passenger cars with Level 3 systems has become available in 2020. In accor…
The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections
Julian Bock, Robert Krajewski, Tobias Moers +3
Automated vehicles rely heavily on data-driven methods, especially for complex urban environments. Large datasets of real world measurement data in the form of road user trajectori…
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 +1
Scenario-based testing for the safety validation of highly automated vehicles is a promising approach that is being examined in research and industry. This approach heavily relies…