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 …

Scenario understanding and motion prediction are essential components for completely
replacing human drivers and for enabling highly and fully automated driving (SAE-Level …

For autonomous vehicles (AVs) to behave appropriately on roads populated by human-
driven vehicles, they must be able to reason about the uncertain intentions and decisions of …

This paper serves as an introduction and overview of the potentially useful models and
methodologies from artificial intelligence (AI) into the field of transportation engineering for …

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

Many problems in robotics involve multiple decision making agents. To operate efficiently in
such settings, a robot must reason about the impact of its decisions on the behavior of other …

Merging is, in general, a challenging task for both human drivers and autonomous vehicles,
especially in dense traffic, because the merging vehicle typically needs to interact with other …

One of the most difficult challenges in robot motion planning is to account for the behavior of
other moving agents, such as humans. Commonly, practitioners employ predictive models to …

An open problem in autonomous vehicle safety validation is building reliable models of
human driving behavior in simulation. This work presents an approach to learn neural …

In this article, we propose a nonlinear receding horizon game-theoretic planner for
autonomous cars in competitive scenarios with other cars. The online planner is specifically …