Autonomous vehicles (AVs) are expected to communicate to vulnerable road users as a
substitution of, for example, driver-pedestrian communication, leading to increased safety …

Recently, eXplainable AI (XAI) research has focused on the use of counterfactual
explanations to address interpretability, algorithmic recourse, and bias in AI system decision …

To analyze the influence of right of way and automated vehicle (AV) deceleration maneuvers
on pedestrians' behavior, a virtual reality study was conducted. Participants were asked to …

The end-to-end learning ability of self-driving vehicles has achieved significant milestones
over the last decade owing to rapid advances in deep learning and computer vision …

The study aimed to investigate whether different driving parameters that influence the
trajectory can be used to communicate the intention of an automated vehicle (AV) regarding …

In recent years, the advancement of deep-learning technologies has greatly promoted the
research progress of autonomous driving. However, deep neural network is like a black box …

Autonomous vehicles operating in dynamic environments are required to account for other
traffic participants. By interpreting sensor information and assessing the collision risk with …

Recent studies indicate that people are negatively predisposed toward utilizing autonomous
systems. These findings highlight the necessity of conducting research to better understand …

The type and amount of information pedestrians should receive while interacting with an
autonomous vehicle (AV) remains an unsolved challenge. The information about the vehicle …

Extant levels of automation (LoAs) taxonomies describe variations in function allocations
between the driver and the driving automation system (DAS) from a technical perspective …