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 …

Mental models are knowledge structures employed by humans to describe, explain, and
predict the world around them. Shared Mental Models (SMMs) occur in teams whose …

In order to drive safely in a dynamic environment, autonomous vehicles should be able to
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …

Multi-agent interacting systems are prevalent in the world, from purely physical systems to
complicated social dynamic systems. In many applications, effective understanding of the …

Learning from demonstration, or imitation learning, is the process of learning to act in an
environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a …

Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …

Driving behavior modeling is of great importance for designing safe, smart, and
personalized autonomous driving systems. In this paper, an internal reward function-based …

It is critical to predict the motion of surrounding vehicles for self-driving planning, especially
in a socially compliant and flexible way. However, future prediction is challenging due to the …

Reinforcement learning (RL) methods are commonly regarded as effective solutions for
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …

In the past decades, we have witnessed significant progress in the domain of autonomous
driving. Advanced techniques based on optimization and reinforcement learning become …