6 papers
SearchAD: Large-Scale Rare Image Retrieval Dataset for Autonomous Driving
Felix Embacher, Jonas Uhrig, Marius Cordts +1
Retrieving rare and safety-critical driving scenarios from large-scale datasets is essential for building robust autonomous driving (AD) systems. As dataset sizes continue to grow,…
Just Repair: A Minimal Denoising Network for Time Series Anomaly Detection
Kadir-Kaan Özer, René Ebeling, Markus Enzweiler
Time series anomaly detectors have grown steadily more complex, incorporating attention mechanisms, adversarial training, and stochastic latent variables. Yet, it is unclear how mu…
LAD-Drive: Bridging Language and Trajectory with Action-Aware Diffusion Transformers
Fabian Schmidt, Karol Fedurko, Markus Enzweiler +1
While multimodal large language models (MLLMs) provide advanced reasoning for autonomous driving, translating their discrete semantic knowledge into continuous trajectories remains…
Surprised by Attention: Predictable Query Dynamics for Time Series Anomaly Detection
Kadir-Kaan Özer, 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…
ECoLAD: Selecting Anomaly Detectors for Automotive Deployment via Compute-Reduction Evaluation
Kadir-Kaan Özer, 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…
STREAM-VAE: Dual-Path Routing for Slow and Fast Dynamics in Vehicle Telemetry Anomaly Detection
Kadir-Kaan Özer, René Ebeling, Markus Enzweiler
Automotive telemetry data exhibits slow drifts and fast spikes, often within the same sequence, making reliable anomaly detection challenging. Standard reconstruction-based methods…