Automated driving has the potential to revolutionize personal, public, and freight mobility.
Beside accurately perceiving the environment, automated vehicles must plan a safe …

Many autonomous agents, such as intelligent vehicles, are inherently required to interact
with one another. Game theory provides a natural mathematical tool for robot motion …

As intelligent robots like autonomous vehicles become increasingly deployed in the
presence of people, the extent to which these systems should leverage model-based game …

Trajectory planning is one of the key challenges to the rapid and large-scale deployment of
autonomous driving. The lane-changing trajectory planning algorithm for autonomous …

Urban driving is a challenging task that requires autonomous agents to account for the
stochastic dynamics and interactions with other vehicles. In this paper, we propose a novel …

Coalitions naturally exist in many real-world systems involving multiple decision makers
such as ridesharing, security, and online ad auctions, but the coalition structure among the …

We propose a stochastic first-order algorithm to learn the rationality parameters of
simultaneous and non-cooperative potential games, ie, the parameters of the agents' …

Game theory offers an interpretable mathematical framework for modeling multi-agent
interactions. However, its applicability in real-world robotics applications is hindered by …

While there has been an increasing focus on the use of game theoretic models for
autonomous driving, empirical evidence shows that there are still open questions around …

A particular challenge for both autonomous and human driving is dealing with risk
associated with dynamic occlusion, ie, occlusion caused by other vehicles in traffic. Based …