no code implementations • 1 Apr 2024 • Florian Kraus, Nicolas Scheiner, Werner Ritter, Klaus Dietmayer
In this article, we present a dataset with detailed manual annotations for different kinds of ghost detections.
no code implementations • 6 Apr 2021 • Ole Schumann, Markus Hahn, Nicolas Scheiner, Fabio Weishaupt, Julius F. Tilly, Jürgen Dickmann, Christian Wöhler
A new automotive radar data set with measurements and point-wise annotations from more than four hours of driving is presented.
no code implementations • 10 Jul 2020 • Florian Kraus, Nicolas Scheiner, Werner Ritter, Klaus Dietmayer
We show that we can use a state-of-the-art automotive radar classifier in order to detect ghost objects alongside real objects.
no code implementations • 9 Jun 2020 • Nicolas Scheiner, Ole Schumann, Florian Kraus, Nils Appenrodt, Jürgen Dickmann, Bernhard Sick
Furthermore, the generalization capabilities of both data sets are evaluated and important comparison metrics for automotive radar object detection are discussed.
1 code implementation • CVPR 2020 • Nicolas Scheiner, Florian Kraus, Fangyin Wei, Buu Phan, Fahim Mannan, Nils Appenrodt, Werner Ritter, Jürgen Dickmann, Klaus Dietmayer, Bernhard Sick, Felix Heide
In this work, we depart from visible-wavelength approaches and demonstrate detection, classification, and tracking of hidden objects in large-scale dynamic environments using Doppler radars that can be manufactured at low-cost in series production.
no code implementations • 8 Jul 2019 • Nicolas Scheiner, Nils Appenrodt, Jürgen Dickmann, Bernhard Sick
The merging process is often implemented in form of a clustering algorithm.
no code implementations • 28 May 2019 • Nicolas Scheiner, Stefan Haag, Nils Appenrodt, Bharanidhar Duraisamy, Jürgen Dickmann, Martin Fritzsche, Bernhard Sick
The reference system allows to much more precisely generate real world radar data distributions of VRUs than compared to conventional methods.
no code implementations • 28 May 2019 • Nicolas Scheiner, Nils Appenrodt, Jürgen Dickmann, Bernhard Sick
Radar-based road user classification is an important yet still challenging task towards autonomous driving applications.
no code implementations • 27 May 2019 • Nicolas Scheiner, Nils Appenrodt, Jürgen Dickmann, Bernhard Sick
The classification of individual traffic participants is a complex task, especially for challenging scenarios with multiple road users or under bad weather conditions.
no code implementations • 27 May 2019 • Nicolas Scheiner, Nils Appenrodt, Jürgen Dickmann, Bernhard Sick
Annotating automotive radar data is a difficult task.