Recent automotive research often focuses on automated driving, including the interaction
between automated vehicles (AVs) and so-called “vulnerable road users”(VRUs). While …

The prediction of pedestrian behavior is essential for automated driving in urban traffic and
has attracted increasing attention in the vehicle industry. This task is challenging because …

Automated vehicles rely heavily on data-driven methods, especially for complex urban
environments. Large datasets of real world measurement data in the form of road user …

The booming self-driving cars need to understand the behaviors of other road users for
better performance. Recognizing pedestrians' crossing intentions is one of the most critical …

Pedestrians' red-light crossing can present a threat to traffic safety. Among all the existing
work related to pedestrian's red-light crossing, there are few studies using trajectory data in …

For the autonomous driving system, accurately recognizing the actions of different roles in
the traffic scene is the prerequisite for realizing this kind of human-vehicle information …

This study explores the modernization of road-based technologies for the enhancement of
mobility while also implementing safer transportation. Mobility plays a critical role in …

The increasing adoption of robot systems in industrial settings and teaming with humans
have led to a growing interest in human-robot interaction (HRI) research. While many robots …

In this paper, Vulnerable Road User (VRU) trajectory prediction for autonomous driving
based on the Intention-Attention-Gate Recurrent Unit (IA-GRU), Improved Social Force …

Awareness messages have been recently introduced to extend the information horizon of
connected vehicles and Vulnerable Road Users (VRUs) beyond their line-of-sight range. As …