Intelligent vehicle technology has made tremendous progress due to Artificial Intelligence
(AI) techniques. Accurate behavior prediction of surrounding traffic actors is essential for the …

Recognizing Activities of Daily Living (ADL) is a vital process for intelligent assistive robots,
but collecting large annotated datasets requires time-consuming temporal labeling and …

Traditional video-based human activity recognition has experienced remarkable progress
linked to the rise of deep learning, but this effect was slower as it comes to the downstream …

With increasing automation in passenger vehicles, the study of safe and smooth occupant-
vehicle interaction and control transitions is key. In this study, we focus on the development …

Visual recognition inside the vehicle cabin leads to safer driving and more intuitive human-
vehicle interaction but such systems face substantial obstacles as they need to capture …

Driver observation models are rarely deployed under perfect conditions. In practice,
illumination, camera placement and type differ from the ones present during training and …

Deep learning-based models are at the top of most driver observation benchmarks due to
their remarkable accuracies but come with a high computational cost, while the resources …

Human affect recognition is a well-established research area with numerous applications,
eg in psychological care, but existing methods assume that all emotions-of-interest are given …

Foundation models (FMs) are large neural networks trained on broad datasets, excelling in
downstream tasks with minimal fine-tuning. Human activity recognition in video has …

For driver observation frameworks, clean datasets collected in controlled simulated
environments often serve as the initial training ground. Yet, when deployed under real …