18 citations · 49 across the 15 of their papers we have counts for
15 papers
ALOOD: Exploiting Language Representations for LiDAR-based Out-of-Distribution Object Detection
Michael Kösel, Marcel Schreiber, Michael Ulrich +2
LiDAR-based 3D object detection plays a critical role for reliable and safe autonomous driving systems. However, existing detectors often produce overly confident predictions for o…
Toward Fully Autonomous Driving: AI, Challenges, Opportunities, and Needs
Lars Ullrich, Michael Buchholz, Klaus Dietmayer +1
Automated driving (AD) is promising, but the transition to fully autonomous driving is, among other things, subject to the real, ever-changing open world and the resulting challeng…
A Concept for Efficient Scalability of Automated Driving Allowing for Technical, Legal, Cultural, and Ethical Differences
Lars Ullrich, Michael Buchholz, Jonathan Petit +2
Efficient scalability of automated driving (AD) is key to reducing costs, enhancing safety, conserving resources, and maximizing impact. However, research focuses on specific vehic…
AI Safety Assurance for Automated Vehicles: A Survey on Research, Standardization, Regulation
Lars Ullrich, Michael Buchholz, Klaus Dietmayer +1
Assuring safety of artificial intelligence (AI) applied to safety-critical systems is of paramount importance. Especially since research in the field of automated driving shows tha…
Dynamic Objective MPC for Motion Planning of Seamless Docking Maneuvers
Oliver Schumann, Michael Buchholz, Klaus Dietmayer
Automated vehicles and logistics robots must often position themselves in narrow environments with high precision in front of a specific target, such as a package or their charging…
Expanding the Classical V-Model for the Development of Complex Systems Incorporating AI
Lars Ullrich, Michael Buchholz, Klaus Dietmayer +1
Research in the field of automated vehicles, or more generally cognitive cyber-physical systems that operate in the real world, is leading to increasingly complex systems. Among ot…
The ADUULM-360 Dataset -- A Multi-Modal Dataset for Depth Estimation in Adverse Weather
Markus Schön, Jona Ruof, Thomas Wodtko +2
Depth estimation is an essential task toward full scene understanding since it allows the projection of rich semantic information captured by cameras into 3D space. While the field…
MGNiceNet: Unified Monocular Geometric Scene Understanding
Markus Schön, Michael Buchholz, Klaus Dietmayer
Monocular geometric scene understanding combines panoptic segmentation and self-supervised depth estimation, focusing on real-time application in autonomous vehicles. We introduce…
SemanticSpray++: A Multimodal Dataset for Autonomous Driving in Wet Surface Conditions
Aldi Piroli, Vinzenz Dallabetta, Johannes Kopp +3
Autonomous vehicles rely on camera, LiDAR, and radar sensors to navigate the environment. Adverse weather conditions like snow, rain, and fog are known to be problematic for both c…
Label-Efficient Semantic Segmentation of LiDAR Point Clouds in Adverse Weather Conditions
Aldi Piroli, Vinzenz Dallabetta, Johannes Kopp +3
Adverse weather conditions can severely affect the performance of LiDAR sensors by introducing unwanted noise in the measurements. Therefore, differentiating between noise and vali…
Revisiting Out-of-Distribution Detection in LiDAR-based 3D Object Detection
Michael Kösel, Marcel Schreiber, Michael Ulrich +2
LiDAR-based 3D object detection has become an essential part of automated driving due to its ability to localize and classify objects precisely in 3D. However, object detectors fac…
Simultaneous Clutter Detection and Semantic Segmentation of Moving Objects for Automotive Radar Data
Johannes Kopp, Dominik Kellner, Aldi Piroli +2
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 vehic…
Multimodal Object Query Initialization for 3D Object Detection
Mathijs R. van Geerenstein, Felicia Ruppel, Klaus Dietmayer +1
3D object detection models that exploit both LiDAR and camera sensor features are top performers in large-scale autonomous driving benchmarks. A transformer is a popular network ar…
Towards Robust 3D Object Detection In Rainy Conditions
Aldi Piroli, Vinzenz Dallabetta, Johannes Kopp +3
LiDAR sensors are used in autonomous driving applications to accurately perceive the environment. However, they are affected by adverse weather conditions such as snow, fog, and ra…
LS-VOS: Identifying Outliers in 3D Object Detections Using Latent Space Virtual Outlier Synthesis
Aldi Piroli, Vinzenz Dallabetta, Johannes Kopp +3
LiDAR-based 3D object detectors have achieved unprecedented speed and accuracy in autonomous driving applications. However, similar to other neural networks, they are often biased…