Description of Corner Cases in Automated Driving: Goals and Challenges. - Daniel Bogdoll; Jasmin Breitenstein; Florian Heidecker; Maarten Bieshaar; Bernhard Sick; Tim Fingscheidt; J. Marius Zöllner (ICCVW, 2021)

Kiglis: Smart Networks for Smart Cities. - Daniel Bogdoll; Patrick Matalla; Christoph Füllner; Christian Raack; Shi Li; Tobias Käfer; Stefan Orf; Marc René Zofka; Finn Sartoris; Christoph Schweikert; Thomas Pfeiffer 001; André Richter; et al. (ISC2, 2021)

Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems. - Hannes Reichert; Lukas Lang; Kevin Rösch; Daniel Bogdoll; Konrad Doll; Bernhard Sick; Hans-Christian Reuss; Christoph Stiller; J. Marius Zöllner (ISC2, 2021)

Reliving the Dataset: Combining the Visualization of Road Users' Interactions with Scenario Reconstruction in Virtual Reality. - Lars Töttel; Maximilian Zipfl; Daniel Bogdoll; Marc René Zofka; J. Marius Zöllner (CoRR, 2021)

Taxonomy and Survey on Remote Human Input Systems for Driving Automation Systems. - Daniel Bogdoll; Stefan Orf; Lars Töttel; J. Marius Zöllner (CoRR, 2021)

Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models. - Daniel Bogdoll; Johannes Jestram; Jonas Rauch; Christin Scheib; Moritz Wittig; J. Marius Zöllner (CoRR, 2021)

Anomaly Detection in Autonomous Driving: A Survey. - Daniel Bogdoll; Maximilian Nitsche; J. Marius Zöllner (CVPR Workshops, 2022)

One Ontology to Rule Them All: Corner Case Scenarios for Autonomous Driving. - Daniel Bogdoll; Stefani Guneshka; J. Marius Zöllner (ECCV Workshops (5), 2022)

Quantification of Actual Road User Behavior on the Basis of Given Traffic Rules. - Daniel Bogdoll; Moritz Nekolla; Tim Joseph; J. Marius Zöllner (IV, 2022)

Multimodal Detection of Unknown Objects on Roads for Autonomous Driving. - Daniel Bogdoll; Enrico Eisen; Maximilian Nitsche; Christin Scheib; J. Marius Zöllner (SMC, 2022)

Ad-datasets: A Meta-collection of Data Sets for Autonomous Driving. - Daniel Bogdoll; Felix Schreyer; J. Marius Zöllner (VEHITS, 2022)

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey. - Julian Wörmann; Daniel Bogdoll; Etienne Bührle; Han Chen; Evaristus Fuh Chuo; Kostadin Cvejoski; Ludger van Elst; Tobias Gleißner; Philip Gottschall; Stefan Griesche; Christian Hellert; Christian Hesels; et al. (CoRR, 2022)

Experiments on Anomaly Detection in Autonomous Driving by Forward-Backward Style Transfers. - Daniel Bogdoll; Meng Zhang; Maximilian Nitsche; J. Marius Zöllner (CoRR, 2022)

DLCSS: Dynamic Longest Common Subsequences. - Daniel Bogdoll; Jonas Rauch; J. Marius Zöllner (CoRR, 2022)

What Does Really Count? Estimating Relevance of Corner Cases for Semantic Segmentation in Automated Driving. - Jasmin Breitenstein; Florian Heidecker; Maria Lyssenko; Daniel Bogdoll; Maarten Bieshaar; J. Marius Zöllner; Bernhard Sick; Tim Fingscheidt (ICCV (Workshops), 2023)

Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey. - Daniel Bogdoll; Svenja Uhlemeyer; Kamil Kowol; J. Marius Zöllner (IV, 2023)

Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving. - Daniel Bogdoll; Lukas Bosch; Tim Joseph; Helen Gremmelmaier; Yitian Yang; J. Marius Zöllner (SSCI, 2023)

Conditioning Latent-Space Clusters for Real-World Anomaly Classification. - Daniel Bogdoll; Svetlana Pavlitska; Simon Klaus; J. Marius Zöllner (SSCI, 2023)

Impact, Attention, Influence: Early Assessment of Autonomous Driving Datasets. - Daniel Bogdoll; Jonas Hendl; Felix Schreyer; Nishanth Gowda; Michael Färber 001; J. Marius Zöllner (CoRR, 2023)

From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Driving. - Ferdinand Mütsch; Helen Gremmelmaier; Nicolas Becker; Daniel Bogdoll; Marc René Zofka; J. Marius Zöllner (CoRR, 2023)

MUVO: A Multimodal Generative World Model for Autonomous Driving with Geometric Representations. - Daniel Bogdoll; Yitian Yang; J. Marius Zöllner (CoRR, 2023)

On The Impact of Replacing Private Cars with Autonomous Shuttles: An Agent-Based Approach. - Daniel Bogdoll; Louis Karsch; Jennifer Amritzer; J. Marius Zöllner (CoRR, 2023)

Hybrid Video Anomaly Detection for Autonomous Driving. - Daniel Bogdoll; Jan Imhof; Tim Joseph; Svetlana Pavlitska; J. Marius Zöllner (BMVC Workshops, 2024)

UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Driving. - Daniel Bogdoll; Noël Ollick; Tim Joseph; Svetlana Pavlitska; J. Marius Zöllner (BMVC Workshops, 2024)

AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving. - Daniel Bogdoll; Iramm Hamdard; Lukas Namgyu Rößler; Felix Geisler; Muhammed Bayram; Felix Wang; Jan Imhof; Miguel de Campos; Anushervon Tabarov; Yitian Yang; Martin Gontscharow; Hanno Gottschalk; et al. (ECCV Workshops (7), 2024)

Informed Reinforcement Learning for Situation-Aware Traffic Rule Exceptions. - Daniel Bogdoll; Jing Qin; Moritz Nekolla; Ahmed Abouelazm; Tim Joseph; J. Marius Zöllner (ICRA, 2024)

Scalable Remote Operation for Autonomous Vehicles: Integration of Cooperative Perception and Open Source Communication. - Martin Gontscharow; Jens Doll; Albert Schotschneider; Daniel Bogdoll; Stefan Orf; Johannes Jestram; Marc René Zofka; J. Marius Zöllner (IV, 2024)

One Stack to Rule them All: To Drive Automated Vehicles, and Reach for the 4th level. - Sven Ochs; Jens Doll; Daniel Grimm; Tobias Fleck; Marc Heinrich; Stefan Orf; Albert Schotschneider; Helen Gremmelmaier; Rupert Polley; Svetlana Pavlitska; Maximilian Zipfl; Helen Schneider; et al. (CoRR, 2024)

AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving. - Daniel Bogdoll; Iramm Hamdard; Lukas Namgyu Rößler; Felix Geisler; Muhammed Bayram; Felix Wang; Jan Imhof; Miguel de Campos; Anushervon Tabarov; Yitian Yang; Hanno Gottschalk; J. Marius Zöllner (CoRR, 2024)

UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Driving. - Daniel Bogdoll; Noël Ollick; Tim Joseph; J. Marius Zöllner (CoRR, 2024)

Hybrid Video Anomaly Detection for Anomalous Scenarios in Autonomous Driving. - Daniel Bogdoll; Jan Imhof; Tim Joseph; J. Marius Zöllner (CoRR, 2024)

Complementary Learning for Real-World Model Failure Detection. - Daniel Bogdoll; Finn Sartoris; Vincent Geppert; Svetlana Pavlitska; J. Marius Zöllner (CoRR, 2024)

Mcity Data Engine: Iterative Model Improvement Through Open-Vocabulary Data Selection. - Daniel Bogdoll; Rajanikant Patnaik Ananta; Abeyankar Giridharan; Isabel Moore; Gregory Stevens; Henry X. Liu (CoRR, 2025)