In this paper, a novel approach is presented for modeling the interaction dynamics between
an ego car and a bicycle in a traffic scenario using a hybrid reinforcement learning …

The advent of autonomous vehicles (AVs) alongside human-driven vehicles (HVs) has
ushered in an era of mixed traffic flow, presenting a significant challenge: the intricate …

Autonomous driving vehicles play a critical role in intelligent transportation systems and
have garnered considerable attention. Currently, the popular approach in autonomous …

Recent years have witnessed the rapid development of the Cooperative Vehicle
Infrastructure System (CVIS), where road infrastructures such as traffic lights (TL) and …

Planning under social interactions with other agents is an essential problem for autonomous
driving. As the actions of the autonomous vehicle in the interactions affect and are also …

Although deep reinforcement learning (DRL) has shown promising results for autonomous
navigation in interactive traffic scenarios, existing work typically adopts a fixed behavior …

With the advent of machine learning, several autonomous driving tasks have become easier
to accomplish. Nonetheless, the proliferation of autonomous vehicles in urban traffic …

Despite the advances in the autonomous driving domain, autonomous vehicles (AVs) are
still inefficient and limited in terms of cooperating with each other or coordinating with …

As increasing numbers of autonomous vehicles (AVs) are being deployed, it is important to
construct a multi-agent self-driving (MASD) system for navigating traffic flows of AVs. In an …

Autonomous vehicle (AV) navigation in the presence of Human-driven vehicles (HVs) is
challenging, as HVs continuously update their policies in response to AVs. In order to …