161 citations · 428 across the 24 of their papers we have counts for
6 papers · 1 filter
MUVO: A Multimodal Generative World Model for Autonomous Driving with Geometric Representations
Daniel Bogdoll, Yitian Yang, Tim Joseph +2
World models for autonomous driving have the potential to dramatically improve the reasoning capabilities of today's systems. However, most works focus on camera data, with only a…
Conditioning Latent-Space Clusters for Real-World Anomaly Classification
Daniel Bogdoll, Svetlana Pavlitska, Simon Klaus +1
Anomalies in the domain of autonomous driving are a major hindrance to the large-scale deployment of autonomous vehicles. In this work, we focus on high-resolution camera data from…
Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving
Daniel Bogdoll, Lukas Bosch, Tim Joseph +3
In recent years there have been remarkable advancements in autonomous driving. While autonomous vehicles demonstrate high performance in closed-set conditions, they encounter diffi…
From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Driving
Ferdinand Mütsch, Helen Gremmelmaier, Nicolas Becker +3
Simulation is an integral part in the process of developing autonomous vehicles and advantageous for training, validation, and verification of driving functions. Even though simula…
Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey
Daniel Bogdoll, Svenja Uhlemeyer, Kamil Kowol +1
Deep neural networks (DNN) which are employed in perception systems for autonomous driving require a huge amount of data to train on, as they must reliably achieve high performance…
Impact, Attention, Influence: Early Assessment of Autonomous Driving Datasets
Daniel Bogdoll, Jonas Hendl, Felix Schreyer +3
Autonomous Driving (AD), the area of robotics with the greatest potential impact on society, has gained a lot of momentum in the last decade. As a result of this, the number of dat…