13 citations · 27 across the 5 of their papers we have counts for
5 papers
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…
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…
A Two-Level Stochastic Model for the Lateral Movement of Vehicles Within Their Lane Under Homogeneous Traffic Conditions
Nicole Neis, Juergen Beyerer
The lateral position of vehicles within their lane is a decisive factor for the range of vision of vehicle sensors. This, in turn, is crucial for a vehicle's ability to perceive it…
Literature Review on Maneuver-Based Scenario Description for Automated Driving Simulations
Nicole Neis, Juergen Beyerer
The increasing complexity of automated driving functions and their growing operational design domains imply more demanding requirements on their validation. Classical methods such…
NIFF: Alleviating Forgetting in Generalized Few-Shot Object Detection via Neural Instance Feature Forging
Karim Guirguis, Johannes Meier, George Eskandar +3
Privacy and memory are two recurring themes in a broad conversation about the societal impact of AI. These concerns arise from the need for huge amounts of data to train deep neura…