We studied clinical AI-supported decision-making as an example of a high-stakes setting in
which explainable AI (XAI) has been proposed as useful (by theoretically providing …

Human drivers use their attentional mechanisms to focus on critical objects and make
decisions while driving. As human attention can be revealed from gaze data, capturing and …

Recent technological developments in mobile brain and body imaging are enabling new
frontiers of real-world neuroscience. Simultaneous recordings of body movement and brain …

The intuitive collaboration of humans and intelligent robots (embodied AI) in the real-world is
an essential objective for many desirable applications of robotics. Whilst there is much …

Having efficient testing strategies is a core challenge that needs to be overcome for the
release of automated driving. This necessitates clear requirements as well as suitable …

Vision-based end-to-end driving models trained by imitation learning can lead to affordable
solutions for autonomous driving. However, training these well-performing models usually …

Motivated by the goal of endowing robots with a means for focusing attention in order to
operate reliably in complex, uncertain, and time-varying environments, we consider how a …

Motion analysis plays a critical role in various applications, from virtual reality and
augmented reality to assistive visual navigation. Traditional self-driving technologies, while …

Despite much effort to try to research driver's spatial attention allocation in driving situations,
the computer vision community rarely focuses on what causes driver's attention shifts. In this …

Assistive visual navigation systems for visually impaired individuals have become
increasingly popular thanks to the rise of mobile computing. Most of these devices work by …