Autonomous driving (AV) technology has elicited discussion on social dilemmas where
trade-offs between individual preferences, social norms, and collective interests may impact …

In order to drive effectively, a driver must be aware of how they can expect other vehicles'
behaviour to be affected by their decisions, and also how they are expected to behave by …

In a multi-agent system (MAS), a social learning scheme allows independent agents to learn
through interactions with agents randomly selected from a pool. Such a scheme is important …

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 …

Deployment of autonomous vehicles on public roads promises increased efficiency and
safety. It requires understanding the intent of human drivers and adapting to their driving …

Autonomous vehicles (AVs) have the potential to revolutionize transportation, but their
effective integration into the real world requires addressing the challenge of interacting with …

In recent years, there has been enormous public interest in autonomous vehicles (AV), with
more than 80 billion dollars invested in self-driving car technology. However, for the …

Recent work on decision making and planning for autonomous driving has made use of
game theoretic methods to model interaction between agents. We demonstrate that methods …

Road congestion induces significant costs across the world, and road network disturbances,
such as traffic accidents, can cause highly congested traffic patterns. If a planner had control …

Autonomous vehicles have the potential to increase the capacity of roads via platooning,
even when human drivers and autonomous vehicles share roads. However, when users of a …