161 citations · 338 across the 33 of their papers we have counts for
37 papers
User Experience in Human-Machine Interaction: Insights from Field Studies in Autonomous Mobility
Helen Schneider, Svetlana Pavlitska, J. Marius Zöllner
Autonomous vehicles (AVs) promise safer, cleaner, and more inclusive mobility, yet large-scale adoption is hindered by user acceptance rather than by technical challenges. Prior st…
Towards a Systematic Risk Assessment of Deep Neural Network Limitations in Autonomous Driving Perception
Svetlana Pavlitska, Christopher Gerking, J. Marius Zöllner
Safety and security are essential for the admission and acceptance of automated and autonomous vehicles. Deep neural networks (DNNs) are widely used for perception and further comp…
Domain-Specialized Object Detection via Model-Level Mixtures of Experts
Svetlana Pavlitska, Malte Stüven, Beyza Keskin +1
Mixture-of-Experts (MoE) models provide a structured approach to combining specialized neural networks and offer greater interpretability than conventional ensembles. While MoEs ha…
DenseBEV: Transforming BEV Grid Cells into 3D Objects
Marius Dähling, Sebastian Krebs, J. Marius Zöllner
In current research, Bird's-Eye-View (BEV)-based transformers are increasingly utilized for multi-camera 3D object detection. Traditional models often employ random queries as anch…
Runtime Safety Monitoring of Deep Neural Networks for Perception: A Survey
Albert Schotschneider, Svetlana Pavlitska, J. Marius Zöllner
Deep neural networks (DNNs) are widely used in perception systems for safety-critical applications, such as autonomous driving and robotics. However, DNNs remain vulnerable to vari…
DEAP DIVE: Dataset Investigation with Vision transformers for EEG evaluation
Annemarie Hoffsommer, Helen Schneider, Svetlana Pavlitska +1
Accurately predicting emotions from brain signals has the potential to achieve goals such as improving mental health, human-computer interaction, and affective computing. Emotion p…
CrowdQuery: Density-Guided Query Module for Enhanced 2D and 3D Detection in Crowded Scenes
Marius Dähling, Sebastian Krebs, J. Marius Zöllner
This paper introduces a novel method for end-to-end crowd detection that leverages object density information to enhance existing transformer-based detectors. We present CrowdQuery…
Exploring Semantic Clustering and Similarity Search for Heterogeneous Traffic Scenario Graph
Ferdinand Mütsch, Maximilian Zipfl, Nikolai Polley +1
Scenario-based testing is an indispensable instrument for the comprehensive validation and verification of automated vehicles (AVs). However, finding a manageable and finite, yet r…
Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation
Svetlana Pavlitska, Enrico Eisen, J. Marius Zöllner
Vulnerability to adversarial attacks is a well-known deficiency of deep neural networks. Larger networks are generally more robust, and ensembling is one method to increase adversa…
Evaluating Adversarial Attacks on Traffic Sign Classifiers beyond Standard Baselines
Svetlana Pavlitska, Leopold Müller, J. Marius Zöllner
Adversarial attacks on traffic sign classification models were among the first successfully tried in the real world. Since then, the research in this area has been mainly restricte…
TLD-READY: Traffic Light Detection -- Relevance Estimation and Deployment Analysis
Nikolai Polley, Svetlana Pavlitska, Yacin Boualili +4
Effective traffic light detection is a critical component of the perception stack in autonomous vehicles. This work introduces a novel deep-learning detection system while addressi…
Hybrid Video Anomaly Detection for Anomalous Scenarios in Autonomous Driving
Daniel Bogdoll, Jan Imhof, Tim Joseph +2
In autonomous driving, the most challenging scenarios can only be detected within their temporal context. Most video anomaly detection approaches focus either on surveillance or tr…
UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Driving
Daniel Bogdoll, Noël Ollick, Tim Joseph +2
Dealing with atypical traffic scenarios remains a challenging task in autonomous driving. However, most anomaly detection approaches cannot be trained on raw sensor data but requir…
AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving
Daniel Bogdoll, Iramm Hamdard, Lukas Namgyu Rößler +9
The scale-up of autonomous vehicles depends heavily on their ability to deal with anomalies, such as rare objects on the road. In order to handle such situations, it is necessary t…
CAGE: Circumplex Affect Guided Expression Inference
Niklas Wagner, Felix Mätzler, Samed R. Vossberg +3
Understanding emotions and expressions is a task of interest across multiple disciplines, especially for improving user experiences. Contrary to the common perception, it has been…
Informed Reinforcement Learning for Situation-Aware Traffic Rule Exceptions
Daniel Bogdoll, Jing Qin, Moritz Nekolla +3
Reinforcement Learning is a highly active research field with promising advancements. In the field of autonomous driving, however, often very simple scenarios are being examined. C…
Heterogeneous Graph-based Trajectory Prediction using Local Map Context and Social Interactions
Daniel Grimm, Maximilian Zipfl, Felix Hertlein +7
Precisely predicting the future trajectories of surrounding traffic participants is a crucial but challenging problem in autonomous driving, due to complex interactions between tra…
Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence
Svetlana Pavlitska, Hannes Grolig, J. Marius Zöllner
Increasing the model capacity is a known approach to enhance the adversarial robustness of deep learning networks. On the other hand, various model compression techniques, includin…
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…
Utilizing Hybrid Trajectory Prediction Models to Recognize Highly Interactive Traffic Scenarios
Maximilian Zipfl, Sven Spickermann, J. Marius Zöllner
Autonomous vehicles hold great promise in improving the future of transportation. The driving models used in these vehicles are based on neural networks, which can be difficult to…
Holistic Graph-based Motion Prediction
Daniel Grimm, Philip Schörner, Moritz Dreßler +1
Motion prediction for automated vehicles in complex environments is a difficult task that is to be mastered when automated vehicles are to be used in arbitrary situations. Many fac…
Self Supervised Clustering of Traffic Scenes using Graph Representations
Maximilian Zipfl, Moritz Jarosch, J. Marius Zöllner
Examining graphs for similarity is a well-known challenge, but one that is mandatory for grouping graphs together. We present a data-driven method to cluster traffic scenes that is…
Fingerprint of a Traffic Scene: an Approach for a Generic and Independent Scene Assessment
Maximilian Zipfl, Barbara Schütt, J. Marius Zöllner +1
A major challenge in the safety assessment of automated vehicles is to ensure that risk for all traffic participants is as low as possible. A concept that is becoming increasingly…
Robotic Control Using Model Based Meta Adaption
Karam Daaboul, Joel Ikels, Marius Zöllner
In machine learning, meta-learning methods aim for fast adaptability to unknown tasks using prior knowledge. Model-based meta-reinforcement learning combines reinforcement learning…