3 papers
cs.CV2025
Measuring the Effect of Background on Classification and Feature Importance in Deep Learning for AV Perception
Anne Sielemann, Valentin Barner, Stefan Wolf +3
Common approaches to explainable AI (XAI) for deep learning focus on analyzing the importance of input features on the classification task in a given model: saliency methods like S…
cs.CV2025
Synset Signset Germany: a Synthetic Dataset for German Traffic Sign Recognition
Anne Sielemann, Lena Loercher, Max-Lion Schumacher +3
In this paper, we present a synthesis pipeline and dataset for training / testing data in the task of traffic sign recognition that combines the advantages of data-driven and analy…
cs.RO2025
Physically-Based Simulation of Automotive LiDAR
L. Dudzik, M. Roschani, A. Sielemann +4
We present an analytic model for simulating automotive time-of-flight (ToF) LiDAR that includes blooming, echo pulse width, and ambient light, along with steps to determine model p…