activity
20242026
most citedToward Fully Autonomous Driving: AI, Challenges, Opportunities, and Needs

4 citations · 4 across the 2 of their papers we have counts for

collaborators

11 papers

cs.CV2026

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…

cs.RO20264 cited

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…

cs.CY2025

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…

cs.CY2025

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…

cs.RO2025

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…

cs.CV2025

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…