4 papers
ECoLAD: Selecting Anomaly Detectors for Automotive Deployment via Compute-Reduction Evaluation
Kadir-Kaan Ãzer, René Ebeling, Markus Enzweiler
Automotive anomaly detectors are often selected from accuracy only benchmarks on workstation class hardware, whereas in-vehicle monitoring requires predictable scoring latency unde…
Surprised by Attention: Predictable Query Dynamics for Time Series Anomaly Detection
Kadir-Kaan Ãzer, René Ebeling, Markus Enzweiler
Multivariate time series anomalies often manifest as shifts in cross-channel dependencies rather than simple amplitude excursions. In autonomous driving, for instance, a steering c…
Just Repair: A Minimal Denoising Network for Time Series Anomaly Detection
Kadir-Kaan Ãzer, Kadir-Kaan Özer, René Ebeling +2
Time series anomaly detectors have grown steadily more complex, incorporating attention mechanisms, adversarial training, and stochastic latent variables. Yet, it is unclear how mu…
STREAM-VAE: Dual-Path Routing for Slow and Fast Dynamics in Vehicle Telemetry Anomaly Detection
Kadir-Kaan Ãzer, Kadir-Kaan Özer, René Ebeling +2
Automotive telemetry data exhibits slow drifts and fast spikes, often within the same sequence, making reliable anomaly detection challenging. Standard reconstruction-based methods…