161 citations · 428 across the 24 of their papers we have counts for
24 papers
Beyond Scalar Rewards: Distributional Reinforcement Learning with Preordered Objectives for Safe and Reliable Autonomous Driving
Ahmed Abouelazm, Jonas Michel, Daniel Bogdoll +2
Autonomous driving involves multiple, often conflicting objectives such as safety, efficiency, and comfort. In reinforcement learning (RL), these objectives are typically combined…
Mcity Data Engine: Iterative Model Improvement Through Open-Vocabulary Data Selection
Daniel Bogdoll, Rajanikant Patnaik Ananta, Abeyankar Giridharan +3
With an ever-increasing availability of data, it has become more and more challenging to select and label appropriate samples for the training of machine learning models. It is esp…
Label-Free Model Failure Detection for Lidar-based Point Cloud Segmentation
Daniel Bogdoll, Finn Sartoris, Vincent Geppert +2
Autonomous vehicles drive millions of miles on the road each year. Under such circumstances, deployed machine learning models are prone to failure both in seemingly normal situatio…
One Stack to Rule them All: To Drive Automated Vehicles, and Reach for the 4th level
Sven Ochs, Jens Doll, Daniel Grimm +15
Most automated driving functions are designed for a specific task or vehicle. Most often, the underlying architecture is fixed to specific algorithms to increase performance. There…
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…
One Ontology to Rule Them All: Corner Case Scenarios for Autonomous Driving
Daniel Bogdoll, Stefani Guneshka, J. Marius Zöllner
The core obstacle towards a large-scale deployment of autonomous vehicles currently lies in the long tail of rare events. These are extremely challenging since they do not occur of…
DLCSS: Dynamic Longest Common Subsequences
Daniel Bogdoll, Jonas Rauch, J. Marius Zöllner
Autonomous driving is a key technology towards a brighter, more sustainable future. To enable such a future, it is necessary to utilize autonomous vehicles in shared mobility model…
Experiments on Anomaly Detection in Autonomous Driving by Forward-Backward Style Transfers
Daniel Bogdoll, Meng Zhang, Maximilian Nitsche +1
Great progress has been achieved in the community of autonomous driving in the past few years. As a safety-critical problem, however, anomaly detection is a huge hurdle towards a l…
Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey
Julian Wörmann, Daniel Bogdoll, Christian Brunner +49
The availability of representative datasets is an essential prerequisite for many successful artificial intelligence and machine learning models. However, in real life applications…
Multimodal Detection of Unknown Objects on Roads for Autonomous Driving
Daniel Bogdoll, Enrico Eisen, Maximilian Nitsche +2
Tremendous progress in deep learning over the last years has led towards a future with autonomous vehicles on our roads. Nevertheless, the performance of their perception systems i…
Anomaly Detection in Autonomous Driving: A Survey
Daniel Bogdoll, Maximilian Nitsche, J. Marius Zöllner
Nowadays, there are outstanding strides towards a future with autonomous vehicles on our roads. While the perception of autonomous vehicles performs well under closed-set condition…
Quantification of Actual Road User Behavior on the Basis of Given Traffic Rules
Daniel Bogdoll, Moritz Nekolla, Tim Joseph +1
Driving on roads is restricted by various traffic rules, aiming to ensure safety for all traffic participants. However, human road users usually do not adhere to these rules strict…
Ad-datasets: a meta-collection of data sets for autonomous driving
Daniel Bogdoll, Felix Schreyer, J. Marius Zöllner
Autonomous driving is among the largest domains in which deep learning has been fundamental for progress within the last years. The rise of datasets went hand in hand with this dev…
Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models
Daniel Bogdoll, Johannes Jestram, Jonas Rauch +3
In the foreseeable future, autonomous vehicles will require human assistance in situations they can not resolve on their own. In such scenarios, remote assistance from a human can…
Description of Corner Cases in Automated Driving: Goals and Challenges
Daniel Bogdoll, Jasmin Breitenstein, Florian Heidecker +4
Scaling the distribution of automated vehicles requires handling various unexpected and possibly dangerous situations, termed corner cases (CC). Since many modules of automated dri…
Taxonomy and Survey on Remote Human Input Systems for Driving Automation Systems
Daniel Bogdoll, Stefan Orf, Lars Töttel +1
Corner cases for driving automation systems can often be detected by the system itself and subsequently resolved by remote humans. There exists a wide variety of technical approach…
KIGLIS: Smart Networks for Smart Cities
Daniel Bogdoll, Patrick Matalla, Christoph Füllner +11
Smart cities will be characterized by a variety of intelligent and networked services, each with specific requirements for the underlying network infrastructure. While smart city a…
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…
Reliving the Dataset: Combining the Visualization of Road Users' Interactions with Scenario Reconstruction in Virtual Reality
Lars Töttel, Maximilian Zipfl, Daniel Bogdoll +2
One core challenge in the development of automated vehicles is their capability to deal with a multitude of complex trafficscenarios with many, hard to predict traffic participants…