No human drives a car in a vacuum; she/he must negotiate with other road users to achieve
their goals in social traffic scenes. A rational human driver can interact with other road users …

This paper surveys the field of deep multiagent reinforcement learning (RL). The
combination of deep neural networks with RL has gained increased traction in recent years …

Opponent modeling is the ability to use prior knowledge and observations in order to predict
the behavior of an opponent. This survey presents a comprehensive overview of existing …

This paper surveys the field of deep multiagent reinforcement learning. The combination of
deep neural networks with reinforcement learning has gained increased traction in recent …

The ability to model the mental states of others is crucial to human social intelligence, and
can offer similar benefits to artificial agents with respect to the social dynamics induced in …

The interpretability of decision-making in autonomous driving is crucial for the building of
virtual driver, promoting the trust worth of artificial intelligence (AI) and the efficiency of …

Intention estimation has been widely studied in lane change scenarios, which explains a
vehicle's behaviour and implies its future motion. However, in dense traffic, lane‐changing is …

Theory of Mind (ToM) is the ability to attribute mental states to others, an important
component of human cognition. At present, there has been growing interest in the artificial …

One of the bottlenecks of automated driving technologies is safe and socially acceptable
interactions with human-driven vehicles, for example during merging. Driver models that …

Theory of Mind (ToM) is a fundamental cognitive architecture that endows humans with the
ability to attribute mental states to others. Humans infer the desires, beliefs, and intentions of …