Intention prediction has become a relevant field of research in Human–Machine and Human–
Robot Interaction. Indeed, any artificial system (co)-operating with and along humans …

Drivers pay unequal attention to different road environmental elements and visual fields,
which greatly influences their driving behavior. However, existing collision warning systems …

Safe path planning in autonomous driving is a complex task due to the interplay of static
scene elements and uncertain surrounding agents. While all static scene elements are a …

To further advance driver monitoring and assistance systems, it is important to understand
how drivers allocate their attention, in other words, where do they tend to look and why …

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 …

The ability to identify important objects in a complex and dynamic driving environment is
essential for autonomous driving agents to make safe and efficient driving decisions. It also …

Intelligent driving assistance can alert drivers to objects in their environment; however, such
systems require a model of drivers' situational awareness (SA)(what aspects of the scene …

Many intelligent driver assistance algorithms try to improve on-road safety by using driver
eye gaze, commonly using foveal gaze as an estimate of human attention. While human …

Driving is a visuomotor task, ie, there is a connection between what drivers see and what
they do. While some models of drivers' gaze account for top-down effects of drivers' actions …

This paper introduces a transformative edge computing-based approach for enhancing
driver attention and road safety using EEG-driven deep reinforcement learning (DRL). As …