no code implementations • 13 Nov 2023 • Johannes Kopp, Dominik Kellner, Aldi Piroli, Vinzenz Dallabetta, Klaus Dietmayer
The unique properties of radar sensors, such as their robustness to adverse weather conditions, make them an important part of the environment perception system of autonomous vehicles.
no code implementations • 2 Oct 2023 • Aldi Piroli, Vinzenz Dallabetta, Johannes Kopp, Marc Walessa, Daniel Meissner, Klaus Dietmayer
LiDAR-based 3D object detectors have achieved unprecedented speed and accuracy in autonomous driving applications.
no code implementations • 2 Oct 2023 • Aldi Piroli, Vinzenz Dallabetta, Johannes Kopp, Marc Walessa, Daniel Meissner, Klaus Dietmayer
In this way, the detected objects are less affected by the adverse weather in the scene, resulting in a more accurate perception of the environment.
1 code implementation • 25 May 2023 • Aldi Piroli, Vinzenz Dallabetta, Johannes Kopp, Marc Walessa, Daniel Meissner, Klaus Dietmayer
Autonomous vehicles rely on LiDAR sensors to perceive the environment.
no code implementations • 11 Jul 2022 • Aldi Piroli, Vinzenz Dallabetta, Marc Walessa, Daniel Meissner, Johannes Kopp, Klaus Dietmayer
We address this problem by presenting a two-step approach for the detection of condensed vehicle gas exhaust.
no code implementations • 24 May 2022 • Aldi Piroli, Vinzenz Dallabetta, Marc Walessa, Daniel Meissner, Johannes Kopp, Klaus Dietmayer
Second, we introduce a point cloud augmentation process that can be used to add gas exhaust to datasets recorded in good weather conditions.
no code implementations • 10 Aug 2021 • Stefano Gasperini, Patrick Koch, Vinzenz Dallabetta, Nassir Navab, Benjamin Busam, Federico Tombari
While self-supervised monocular depth estimation in driving scenarios has achieved comparable performance to supervised approaches, violations of the static world assumption can still lead to erroneous depth predictions of traffic participants, posing a potential safety issue.