Distracted or drowsy driving is unsafe driving behavior responsible for thousands of crashes
every year. Studying driver behavior has challenges associated with observing drivers in …

Every year worldwide more than one million people die and a further 50 million people are
injured in traffic accidents. Therefore, the development of car safety features that actively …

We introduce the novel domain-specific Drive&Act benchmark for fine-grained
categorization of driver behavior. Our dataset features twelve hours and over 9.6 million …

Video understanding calls for a model to learn the characteristic interplay between static
scene content and its dynamics: Given an image, the model must be able to predict a future …

Driver behavior recognition is crucial for traffic safety in intelligent transportation systems. To
understand the driver distraction behavior, deep learning methods has been used to learn …

Distracted driving has become one of the main contributors to traffic accidents. It is therefore
of great interest for intelligent vehicles to establish a distraction detection system that can …

This work describes an end-to-end approach for real-time human action recognition from
raw depth image-sequences. The proposal is based on a 3D fully convolutional neural …

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 …

Lane change trajectory prediction is crucial for autonomous vehicles (AVs) to assess their
own driving safety in advance. However, there are significant uncertainties in the …