Training intelligent agents that can drive autonomously in various urban and highway
scenarios has been a hot topic in the robotics society within the last decades. However, the …

In general-sum games, the interaction of self-interested learning agents commonly leads to
socially worse outcomes, such as defect-defect in the iterated stag hunt (ISH). Previous …

In this work, we use the communication of intent as a means to facilitate cooperation
between autonomous vehicle agents. Generally speaking, intents can be any reliable …

Human-level driving is the ultimate goal of autonomous driving. As the top-level decision-
making aspect of autonomous driving, behavior decision establishes short-term driving …

Multi-Agent Reinforcement Learning (MARL) has become a promising solution for
constructing a multi-agent autonomous driving system (MADS) in complex and dense …

End to end learning systems are becoming increasingly common in autonomous driving
research, from perception, to planning and control. In particular, distributed reinforcement …

This paper introduces a trajectory prediction model tailored for autonomous driving, focusing
on capturing complex interactions in dynamic traffic scenarios without reliance on high …

Safe interaction between vehicles requires the ability to choose actions that reveal the
preferences of the other vehicles. Since exploratory actions often do not directly contribute to …

Reinforcement learning has been shown to have great potential applications in autonomous
driving. For collaborative driving scenarios, multi-agent reinforcement learning can be used …

Ethical decision-making in autonomous vehicles has been a significant area of research
since the emergence of the Trolley Problem. However, current studies fail to effectively …