This paper explores the emerging knowledge-driven autonomous driving technologies. Our
investigation highlights the limitations of current autonomous driving systems, in particular …

Multi-agent behavior modeling and trajectory forecasting are crucial for the safe navigation
of autonomous agents in interactive scenarios. Variational Autoencoder (VAE) has been …

Effective interaction modeling and behavior prediction of dynamic agents play a significant
role in interactive motion planning for autonomous robots. Although existing methods have …

Nowadays, machine learning methods are increasingly used in different parts of
autonomous driving and driving assistance systems. Yet, data and computational …

Effective understanding of dynamically evolving multiagent interactions is crucial to
capturing the underlying behavior of agents in social systems. It is usually challenging to …

With the development of Artificial Intelligence (AI) technology, autonomous vehicles (AVs)
have entered the general public's view, however, the challenges brought by" long-tail" …

Understanding and learning the actor-to-X inter-actions (AXIs), such as those between the
focal vehicles (actor) and other traffic participants (eg, other vehicles, pedestrians) as well as …

The past decade has witnessed significant breakthroughs in autonomous driving
technologies. We are heading toward an intelligent and efficient transportation system …

Human-robot collaboration (HRC) is a field that studies how to combine the strengths of
humans and robots to perform joint tasks. A crucial element of any successful collaboration …