7 citations · 17 across the 25 of their papers we have counts for
25 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…
Real-World On-Vehicle Evaluation of Embedding-Based Anomaly Detection
Albert Schotschneider, Daniel Bogdoll, Svetlana Pavlitska +2
Detecting anomalies in traffic scenes is crucial for ensuring safety in autonomous driving, yet collecting representative anomalous data remains challenging. Existing anomaly detec…
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…
Design and Behavior of Sparse Mixture-of-Experts Layers in CNN-based Semantic Segmentation
Svetlana Pavlitska, Haixi Fan, Konstantin Ditschuneit +1
Sparse mixture-of-experts (MoE) layers have been shown to substantially increase model capacity without a proportional increase in computational cost and are widely used in transfo…
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…
Datasets for Valence and Arousal Inference: A Survey
Helen Schneider, Svetlana Pavlitska, Helen Gremmelmaier +1
Understanding human affect can be used in robotics, marketing, education, human-computer interaction, healthcare, entertainment, autonomous driving, and psychology to enhance decis…
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…
Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers
Svetlana Pavlitska, Haixi Fan, Konstantin Ditschuneit +1
Robustifying convolutional neural networks (CNNs) against adversarial attacks remains challenging and often requires resource-intensive countermeasures. We explore the use of spars…
Extracting Uncertainty Estimates from Mixtures of Experts for Semantic Segmentation
Svetlana Pavlitska, Beyza Keskin, Alwin Faßbender +2
Estimating accurate and well-calibrated predictive uncertainty is important for enhancing the reliability of computer vision models, especially in safety-critical applications like…
Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors
Svetlana Pavlitska, Jamie Robb, Nikolai Polley +2
Realistic adversarial attacks on various camera-based perception tasks of autonomous vehicles have been successfully demonstrated so far. However, only a few works considered attac…
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…
Efficient Edge AI: Deploying Convolutional Neural Networks on FPGA with the Gemmini Accelerator
Federico Nicolas Peccia, Svetlana Pavlitska, Tobias Fleck +1
The growing concerns regarding energy consumption and privacy have prompted the development of AI solutions deployable on the edge, circumventing the substantial CO2 emissions asso…
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…
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…
Iterative Filter Pruning for Concatenation-based CNN Architectures
Svetlana Pavlitska, Oliver Bagge, Federico Peccia +2
Model compression and hardware acceleration are essential for the resource-efficient deployment of deep neural networks. Modern object detectors have highly interconnected convolut…
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…
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…
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…
Traffic Light Recognition using Convolutional Neural Networks: A Survey
Svetlana Pavlitska, Nico Lambing, Ashok Kumar Bangaru +1
Real-time traffic light recognition is essential for autonomous driving. Yet, a cohesive overview of the underlying model architectures for this task is currently missing. In this…
Adversarial Attacks on Traffic Sign Recognition: A Survey
Svetlana Pavlitska, Nico Lambing, J. Marius Zöllner
Traffic sign recognition is an essential component of perception in autonomous vehicles, which is currently performed almost exclusively with deep neural networks (DNNs). However,…