3 papers
cs.CV2026
Multi-modal Video Representation Alignment for Robust Self-supervised Driver Distraction Detection
David J. Lerch, Livien Majer, Zeyun Zhong +3
Robust self-supervised learning of multi-modal video representations is critical for real-world applications such as driver distraction detection, where multiple sensors provide co…
cs.CV2026
Vision-language Models for Driver Monitoring Systems: A Driver Activity Description Dataset
David J. Lerch, Sarath Mulugurthi, Manuel Martin +2
Understanding subtle driver actions is essential for building reliable driver monitoring systems. Existing visionlanguage models (VLMs) are trained on general datasets and struggle…
cs.CV2026
Knowledge-Guided Failure Prediction: Detecting When Object Detectors Miss Safety-Critical Objects
Jakob Paul Zimmermann, Gerrit Holzbach, David Lerch
Object detectors deployed in safety-critical environments can fail silently, e.g. missing pedestrians, workers, or other safety-critical objects without emitting any warning. Tradi…