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 • 24 Aug 2021 • Stefanie Walz, Mario Bijelic, Florian Kraus, Werner Ritter, Martin Simon, Igor Doric
Current driver assistance systems and autonomous driving stacks are limited to well-defined environment conditions and geo fenced areas.
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.
1 code implementation • 24 May 2019 • Florian Kraus, Klaus Dietmayer
Environment perception is the task for intelligent vehicles on which all subsequent steps rely.
1 code implementation • CVPR 2020 • Mario Bijelic, Tobias Gruber, Fahim Mannan, Florian Kraus, Werner Ritter, Klaus Dietmayer, Felix Heide
The fusion of multimodal sensor streams, such as camera, lidar, and radar measurements, plays a critical role in object detection for autonomous vehicles, which base their decision making on these inputs.
Ranked #2 on 2D Object Detection on Clear Weather