Daniel Bogdoll

47 papers A* 1B 1C 4Journal 29Unranked 12

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Beyond Scalar Rewards: Distributional Reinforcement Learning with Preordered Objectives for Safe and Reliable Autonomous Driving. CoRR Ahmed Abouelazm, Jonas Michel, Daniel Bogdoll, Philip Schörner, J. Marius Zöllner
2025	—	`conf`	Mcity Data Engine: Iterative Model Improvement Through Open-Vocabulary Data Selection. ITSC Daniel Bogdoll, Rajanikant Patnaik Ananta, Abeyankar Giridharan, Isabel Moore, Gregory Stevens, Henry X. Liu
2025	J	`jnl`	Mcity Data Engine: Iterative Model Improvement Through Open-Vocabulary Data Selection. CoRR Daniel Bogdoll, Rajanikant Patnaik Ananta, Abeyankar Giridharan, Isabel Moore, Gregory Stevens, Henry X. Liu
2024	—	`conf`	AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving. ECCV Workshops (7) 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, J. Marius Zöllner
2024	J	`jnl`	AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving. CoRR 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
2024	J	`jnl`	Complementary Learning for Real-World Model Failure Detection. CoRR Daniel Bogdoll, Finn Sartoris, Vincent Geppert, Svetlana Pavlitska, J. Marius Zöllner
2024	J	`jnl`	Hybrid Video Anomaly Detection for Anomalous Scenarios in Autonomous Driving. CoRR Daniel Bogdoll, Jan Imhof, Tim Joseph, J. Marius Zöllner
2024	—	`conf`	Hybrid Video Anomaly Detection for Autonomous Driving. BMVC Workshops Daniel Bogdoll, Jan Imhof, Tim Joseph, Svetlana Pavlitska, J. Marius Zöllner
2024	A*	`conf`	Informed Reinforcement Learning for Situation-Aware Traffic Rule Exceptions. ICRA Daniel Bogdoll, Jing Qin, Moritz Nekolla, Ahmed Abouelazm, Tim Joseph, J. Marius Zöllner
2024	J	`jnl`	Informed Reinforcement Learning for Situation-Aware Traffic Rule Exceptions. CoRR Daniel Bogdoll, Jing Qin, Moritz Nekolla, Ahmed Abouelazm, Tim Joseph, J. Marius Zöllner
2024	J	`jnl`	One Stack to Rule them All: To Drive Automated Vehicles, and Reach for the 4th level. CoRR Sven Ochs, Jens Doll, Daniel Grimm, Tobias Fleck, Marc Heinrich, Stefan Orf, Albert Schotschneider, Helen Gremmelmaier, Rupert Polley, Svetlana Pavlitska, Maximilian Zipfl, Helen Schneider, Ferdinand Mütsch, Daniel Bogdoll, Florian Kuhnt, Philip Schörner, Marc René Zofka, J. Marius Zöllner
2024	C	`conf`	Scalable Remote Operation for Autonomous Vehicles: Integration of Cooperative Perception and Open Source Communication. IV Martin Gontscharow, Jens Doll, Albert Schotschneider, Daniel Bogdoll, Stefan Orf, Johannes Jestram, Marc René Zofka, J. Marius Zöllner
2024	—	`conf`	UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Driving. BMVC Workshops Daniel Bogdoll, Noël Ollick, Tim Joseph, Svetlana Pavlitska, J. Marius Zöllner
2024	J	`jnl`	UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Driving. CoRR Daniel Bogdoll, Noël Ollick, Tim Joseph, J. Marius Zöllner
2023	—	`conf`	Conditioning Latent-Space Clusters for Real-World Anomaly Classification. SSCI Daniel Bogdoll, Svetlana Pavlitska, Simon Klaus, J. Marius Zöllner
2023	J	`jnl`	Conditioning Latent-Space Clusters for Real-World Anomaly Classification. CoRR Daniel Bogdoll, Svetlana Pavlitska, Simon Klaus, J. Marius Zöllner
2023	—	`conf`	Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving. SSCI Daniel Bogdoll, Lukas Bosch, Tim Joseph, Helen Gremmelmaier, Yitian Yang, J. Marius Zöllner
2023	J	`jnl`	Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving. CoRR Daniel Bogdoll, Lukas Bosch, Tim Joseph, Helen Gremmelmaier, Yitian Yang, J. Marius Zöllner
2023	J	`jnl`	From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Driving. CoRR Ferdinand Mütsch, Helen Gremmelmaier, Nicolas Becker, Daniel Bogdoll, Marc René Zofka, J. Marius Zöllner
2023	J	`jnl`	Impact, Attention, Influence: Early Assessment of Autonomous Driving Datasets. CoRR Daniel Bogdoll, Jonas Hendl, Felix Schreyer, Nishanth Gowda, Michael Färber, J. Marius Zöllner
2023	J	`jnl`	MUVO: A Multimodal Generative World Model for Autonomous Driving with Geometric Representations. CoRR Daniel Bogdoll, Yitian Yang, J. Marius Zöllner
2023	J	`jnl`	On The Impact of Replacing Private Cars with Autonomous Shuttles: An Agent-Based Approach. CoRR Daniel Bogdoll, Louis Karsch, Jennifer Amritzer, J. Marius Zöllner
2023	C	`conf`	Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey. IV Daniel Bogdoll, Svenja Uhlemeyer, Kamil Kowol, J. Marius Zöllner
2023	J	`jnl`	Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey. CoRR Daniel Bogdoll, Svenja Uhlemeyer, Kamil Kowol, J. Marius Zöllner
2023	—	`conf`	What Does Really Count? Estimating Relevance of Corner Cases for Semantic Segmentation in Automated Driving. ICCV (Workshops) Jasmin Breitenstein, Florian Heidecker, Maria Lyssenko, Daniel Bogdoll, Maarten Bieshaar, J. Marius Zöllner, Bernhard Sick, Tim Fingscheidt
2022	C	`conf`	Ad-datasets: A Meta-collection of Data Sets for Autonomous Driving. VEHITS Daniel Bogdoll, Felix Schreyer, J. Marius Zöllner
2022	J	`jnl`	Ad-datasets: a meta-collection of data sets for autonomous driving. CoRR Daniel Bogdoll, Felix Schreyer, J. Marius Zöllner
2022	—	`conf`	Anomaly Detection in Autonomous Driving: A Survey. CVPR Workshops Daniel Bogdoll, Maximilian Nitsche, J. Marius Zöllner
2022	J	`jnl`	Anomaly Detection in Autonomous Driving: A Survey. CoRR Daniel Bogdoll, Maximilian Nitsche, J. Marius Zöllner
2022	J	`jnl`	DLCSS: Dynamic Longest Common Subsequences. CoRR Daniel Bogdoll, Jonas Rauch, J. Marius Zöllner
2022	J	`jnl`	Experiments on Anomaly Detection in Autonomous Driving by Forward-Backward Style Transfers. CoRR Daniel Bogdoll, Meng Zhang, Maximilian Nitsche, J. Marius Zöllner
2022	J	`jnl`	Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey. CoRR 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, Sebastian Houben, Tim Joseph, Niklas Keil, Johann Kelsch, Hendrik Königshof, Erwin Kraft, Leonie Kreuser, Kevin Krone, Tobias Latka, Denny Mattern, Stefan Matthes, Mohsin Munir, Moritz Nekolla, Adrian Paschke, Maximilian Alexander Pintz, Tianming Qiu, Faraz Qureishi, Syed Tahseen Raza Rizvi, Jörg Reichardt, Laura von Rüden, Stefan Rudolph, Alexander Sagel, Gerhard Schunk, Hao Shen, Hendrik Stapelbroek, Vera Stehr, Gurucharan Srinivas, Anh Tuan Tran, Abhishek Vivekanandan, Ya Wang, Florian Wasserrab, Tino Werner, Christian Wirth, Stefan Zwicklbauer
2022	B	`conf`	Multimodal Detection of Unknown Objects on Roads for Autonomous Driving. SMC Daniel Bogdoll, Enrico Eisen, Maximilian Nitsche, Christin Scheib, J. Marius Zöllner
2022	J	`jnl`	Multimodal Detection of Unknown Objects on Roads for Autonomous Driving. CoRR Daniel Bogdoll, Enrico Eisen, Maximilian Nitsche, Christin Scheib, J. Marius Zöllner
2022	—	`conf`	One Ontology to Rule Them All: Corner Case Scenarios for Autonomous Driving. ECCV Workshops (5) Daniel Bogdoll, Stefani Guneshka, J. Marius Zöllner
2022	J	`jnl`	One Ontology to Rule Them All: Corner Case Scenarios for Autonomous Driving. CoRR Daniel Bogdoll, Stefani Guneshka, J. Marius Zöllner
2022	C	`conf`	Quantification of Actual Road User Behavior on the Basis of Given Traffic Rules. IV Daniel Bogdoll, Moritz Nekolla, Tim Joseph, J. Marius Zöllner
2022	J	`jnl`	Quantification of Actual Road User Behavior on the Basis of Given Traffic Rules. CoRR Daniel Bogdoll, Moritz Nekolla, Tim Joseph, J. Marius Zöllner
2021	J	`jnl`	Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models. CoRR Daniel Bogdoll, Johannes Jestram, Jonas Rauch, Christin Scheib, Moritz Wittig, J. Marius Zöllner
2021	—	`conf`	Description of Corner Cases in Automated Driving: Goals and Challenges. ICCVW Daniel Bogdoll, Jasmin Breitenstein, Florian Heidecker, Maarten Bieshaar, Bernhard Sick, Tim Fingscheidt, J. Marius Zöllner
2021	J	`jnl`	Description of Corner Cases in Automated Driving: Goals and Challenges. CoRR Daniel Bogdoll, Jasmin Breitenstein, Florian Heidecker, Maarten Bieshaar, Bernhard Sick, Tim Fingscheidt, J. Marius Zöllner
2021	J	`jnl`	KIGLIS: Smart Networks for Smart Cities. CoRR Daniel Bogdoll, Patrick Matalla, Christoph Füllner, Christian Raack, Shi Li, Tobias Käfer, Stefan Orf, Marc René Zofka, Finn Sartoris, Christoph Schweikert, Thomas Pfeiffer, André Richter, Sebastian Randel, Rene Bonk
2021	—	`conf`	Kiglis: Smart Networks for Smart Cities. ISC2 Daniel Bogdoll, Patrick Matalla, Christoph Füllner, Christian Raack, Shi Li, Tobias Käfer, Stefan Orf, Marc René Zofka, Finn Sartoris, Christoph Schweikert, Thomas Pfeiffer, André Richter, Sebastian Randel, Rene Bonk
2021	J	`jnl`	Reliving the Dataset: Combining the Visualization of Road Users' Interactions with Scenario Reconstruction in Virtual Reality. CoRR Lars Töttel, Maximilian Zipfl, Daniel Bogdoll, Marc René Zofka, J. Marius Zöllner
2021	J	`jnl`	Taxonomy and Survey on Remote Human Input Systems for Driving Automation Systems. CoRR Daniel Bogdoll, Stefan Orf, Lars Töttel, J. Marius Zöllner
2021	—	`conf`	Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems. ISC2 Hannes Reichert, Lukas Lang, Kevin Rösch, Daniel Bogdoll, Konrad Doll, Bernhard Sick, Hans-Christian Reuss, Christoph Stiller, J. Marius Zöllner
2021	J	`jnl`	Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems. CoRR Hannes Reichert, Lukas Lang, Kevin Rösch, Daniel Bogdoll, Konrad Doll, Bernhard Sick, Hans-Christian Reuss, Christoph Stiller, J. Marius Zöllner

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.