49 citations · 56 across the 9 of their papers we have counts for
9 papers
Creating Impactful Autonomous Driving Datasets: A Strategic Guide from Research Gap to Benchmark
Richard Schwarzkopf, Jonas Merkert, Frank Bieder +22
Well-designed autonomous driving datasets have fundamentally shaped research progress, yet existing literature primarily describes what datasets contain rather than how to strategi…
The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset
Richard Schwarzkopf, Fabian Immel, Alexander Blumberg +21
Existing autonomous driving datasets have enabled major progress, but fall short in sensor fidelity, map completeness, or geographic diversity. We present KITScenes Multimodal, a E…
Reasoning models do not yet follow their reasoning in autonomous driving: The KITScenes LongTail Dataset
Royden Wagner, Omer Sahin Tas, Jaime Villa +20
Handling rare events is the central open challenge in autonomous driving. Reasoning models, which generate explicit chains of reasoning before acting, promise to generalize to such…
PITA: Physics-Informed Trajectory Autoencoder
Johannes Fischer, Kevin Rösch, Martin Lauer +1
Validating robotic systems in safety-critical appli-cations requires testing in many scenarios including rare edgecases that are unlikely to occur, requiring to complement real-wor…
LDFA: Latent Diffusion Face Anonymization for Self-driving Applications
Marvin Klemp, Kevin Rösch, Royden Wagner +2
In order to protect vulnerable road users (VRUs), such as pedestrians or cyclists, it is essential that intelligent transportation systems (ITS) accurately identify them. Therefore…
Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving
Kevin Rösch, Florian Heidecker, Julian Truetsch +5
Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users' movement and behavior patterns.…
Fast and Robust Ground Surface Estimation from LIDAR Measurements using Uniform B-Splines
Sascha Wirges, Kevin Rösch, Frank Bieder +1
We propose a fast and robust method to estimate the ground surface from LIDAR measurements on an automated vehicle. The ground surface is modeled as a UBS which is robust towards v…
Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems
Hannes Reichert, Lukas Lang, Kevin Rösch +6
Full-stack autonomous driving perception modules usually consist of data-driven models based on multiple sensor modalities. However, these models might be biased to the sensor setu…
An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving
Florian Heidecker, Jasmin Breitenstein, Kevin Rösch +5
Systems and functions that rely on machine learning (ML) are the basis of highly automated driving. An essential task of such ML models is to reliably detect and interpret unusual,…