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…