148 citations · 350 across the 12 of their papers we have counts for
12 papers
Offline Auto Labeling: BAAS
Stefan Haag, Bharanidhar Duraisamy, Felix Govaers +3
This paper introduces BAAS, a new Extended Object Tracking (EOT) and fusion-based label annotation framework for radar detections in autonomous driving. Our framework utilizes Baye…
Landmark-based Vehicle Self-Localization Using Automotive Polarimetric Radars
Fabio Weishaupt, Julius F. Tilly, Nils Appenrodt +3
Automotive self-localization is an essential task for any automated driving function. This means that the vehicle has to reliably know its position and orientation with an accuracy…
UNIFY: Multi-Belief Bayesian Grid Framework based on Automotive Radar
Stefan Haag, Bharanidhar Duraisamy, Daniel Pfrommer +3
Grid maps are widely established for the representation of static objects in robotics and automotive applications. Though, incorporating velocity information is still widely examin…
RadarScenes: A Real-World Radar Point Cloud Data Set for Automotive Applications
Ole Schumann, Markus Hahn, Nicolas Scheiner +4
A new automotive radar data set with measurements and point-wise annotations from more than four hours of driving is presented. Data provided by four series radar sensors mounted o…
Motion Classification and Height Estimation of Pedestrians Using Sparse Radar Data
Markus Horn, Ole Schumann, Markus Hahn +2
A complete overview of the surrounding vehicle environment is important for driver assistance systems and highly autonomous driving. Fusing results of multiple sensor types like ca…
Off-the-shelf sensor vs. experimental radar -- How much resolution is necessary in automotive radar classification?
Nicolas Scheiner, Ole Schumann, Florian Kraus +3
Radar-based road user detection is an important topic in the context of autonomous driving applications. The resolution of conventional automotive radar sensors results in a sparse…