6 citations · 10 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
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…